Put enterprise AI into production.
ADEL embeds senior Forward Deployed Engineers and AI Pods into your existing engineering environment to solve hard problems, accelerate delivery and build AI capability your team can own.
ADEL — the experts behind enterprise AI.
- Scope
- One defined business problem with material uncertainty
- Team
- Senior Enterprise FDE plus the skills the problem needs
- You leave with
- Shared problem definition, agreed measures, a working proof where feasible, the open risks, and a practical production backlog
- Boundary
- Fixed scope, agreed upfront, reviewed as evidence changes the path
- Ends with
- A production recommendation you can act on with or without us
Built for the work between ambition and production
AI ambition is not the bottleneck. Production is.
Powerful models are widely available. Enterprise value is harder: the solution has to work with real data, real systems, real security controls, real operating processes and real people.
ADEL closes that gap. We start with a defined business problem, work inside your environment, and build the path from working proof to production operation.
Start with one hard problem.
The ADEL Production Sprint is a focused way to test value, feasibility and the path to production before committing to a large programme.
It is deliberately bounded. If the evidence says the use case is not worth productionizing, that is a useful sprint outcome and we will say so.
- Readiness review
- Clarify the use case, constraints, data, controls, architecture and delivery risks
- Production Sprint
- Test the highest-value and highest-risk assumptions in a defined, measurable sprint
- FDE Pod
- Embed a senior multidisciplinary team to build, productionize and transfer
- Scale foundation
- Shared platform, context, evaluation and operating capability across teams
What we help you build.
A team built around your problem — not a technology stack.
An ADEL Pod is led by an experienced Enterprise Forward Deployed Engineer who can work with business leaders, architects and delivery teams. The Pod combines the skills the problem needs and joins your existing way of working.
The goal is not long-term dependency. The goal is a working system, clear operating ownership, and a team that is stronger for the next challenge.
- Leads
- Enterprise Forward Deployed Engineer
- May include
- AI/ML, software, data, platform, product, security and responsible-AI expertise
- Works in
- Your backlog, repositories, ceremonies, architecture decisions and security processes
- Agreed upfront
- Access, decision rights, communication and engineering standards
- Exit criterion
- Your team can take the next step without waiting for ADEL
Understand. Prove. Productionize. Transfer.
Four stages, each with an output you can inspect. The sequence matters: nothing moves forward on enthusiasm alone.
Understand
Define the business problem, operating context, constraints and success measures.
Problem charter · measures · access plan · initial backlog
Prove
Build the smallest credible proof and test the highest-risk assumptions first.
Working proof · evaluation results · updated risks
Productionize
Integrate security, data, evaluation, observability, performance and operations.
Operated capability · architecture · controls · runbooks
Transfer
Document, coach and establish the team, controls and backlog you will own.
Ownership map · enablement · open backlog · exit criteria
Designed for complex, high-accountability environments.
ADEL brings production discipline to financial services, insurance, healthcare and enterprise technology — and to any organization where reliability, security, explainability and ownership matter.
Written by the people doing the work.
Practical guidance for leaders and engineers moving AI from experiment to operated capability. Specific enough to use, honest about the trade-offs.
From AI pilot to production: the readiness checklist
The questions a use case has to survive before it earns an integration budget.
Read the field note Delivery modelWhat an Enterprise Forward Deployed Engineer actually does
The role, the decision rights, and why it is not a senior contractor by another name.
Read the field note EvaluationEvaluating RAG and agent systems beyond demo accuracy
What to measure when the demo works and you still cannot ship it.
Read the field noteYour team. Your technology. Your ownership.
Model and platform choices should follow the problem, the risk, the performance, the cost and the portability requirements. ADEL does not require a proprietary framework. We bring engineering expertise and shared accountability; you keep the technology, the knowledge and the capability created together.
Have a hard AI problem?
Tell us what you are trying to solve, what is blocking progress and what success would look like. We will help determine the most useful thing to prove or build first.
A senior team member reads every submission. If ADEL is not the right fit, we will say so and point you somewhere better.
Six ways we help you reach production.
Start where the uncertainty is highest. Every engagement is shaped around your environment, your constraints and your ownership model — not around a fixed methodology we need you to adopt.
What we help you build.
Most clients begin with a Sprint and continue with a Pod. The rest are capabilities that can be built inside either, or run on their own once the direction is clear.
Start where the uncertainty is highest.
The right first engagement depends on what you do not yet know. If the use case is unproven, prove it. If the use case is proven and delivery is the constraint, embed a team.
If you are unsure, describe the problem and we will tell you which of these is the smallest useful step — including when the answer is none of them.
- Readiness review
- Clarify the use case, constraints, data, controls, architecture and delivery risks
- Production Sprint
- Test the highest-value and highest-risk assumptions in a defined, measurable sprint
- FDE Pod
- Embed a senior multidisciplinary team to build, productionize and transfer
- Scale foundation
- Shared platform, context, evaluation and operating capability across teams
Before you get in touch.
Do we have to start with a Production Sprint?
No. If the use case is already proven and the constraint is delivery capacity or production engineering, a Pod is usually the better first step. The Sprint exists to reduce uncertainty, so it earns its place only when there is real uncertainty to reduce.
Can you work with our existing vendors and system integrators?
Yes. We are frequently one team among several. We agree interfaces, decision rights and escalation paths at the start so that accountability is explicit rather than assumed.
Which models and platforms do you use?
Whichever the problem, the risk profile, the performance requirements, the cost envelope and your portability constraints point to. We do not resell licences and we do not require a proprietary framework.
What happens to the code and the knowledge?
It is yours. Every engagement includes documentation, enablement and an explicit ownership handover. The exit criterion is that your team can take the next step without waiting for us.
Have a hard AI problem?
Tell us what you are trying to solve, what is blocking progress and what success would look like. We will help determine the most useful thing to prove or build first.
A senior team member reads every submission. If ADEL is not the right fit, we will say so and point you somewhere better.
Start with one hard problem.
A focused way to test value, feasibility and the path to production before committing to a large programme. Deliberately bounded, deliberately honest about what the evidence shows.
A short, senior-led engagement with a decision at the end.
A Sprint takes one defined business problem and works it until you can make an informed call: productionize it, reshape it, or stop. We work inside your environment with your people, so what we learn is true of your systems rather than of a sandbox.
If the evidence says the use case is not worth productionizing, that is a useful sprint outcome and we will say so. A clear no, reached in weeks, is cheaper than a slow yes reached in quarters.
- One problem, agreed in writing before we start
- A senior Enterprise FDE leading the work end to end
- Measures defined up front, so the result is not a matter of opinion
- A written recommendation you can take to a steering group
- Scope
- One defined business problem with material uncertainty
- Team
- Senior Enterprise FDE plus the skills the problem needs
- You leave with
- Shared problem definition, agreed measures, a working proof where feasible, the open risks, and a practical production backlog
- Boundary
- Fixed scope, agreed upfront, reviewed as evidence changes the path
- Ends with
- A production recommendation you can act on with or without us
Four moves, in order.
The sequence matters. Nothing moves forward on enthusiasm alone, and each step produces something you can inspect.
Frame
Agree the business problem, the operating context, the constraints and how success will be measured.
Problem charter · measures · access plan
Build the proof
Construct the smallest credible working system against real data and real interfaces.
Working proof · evaluation harness
Stress it
Test the highest-risk assumptions: accuracy, latency, cost, security review, failure behaviour.
Evaluation results · risk register
Recommend
Set out the production path, the effort, the open risks and the decision we think you should take.
Recommendation · production backlog
Six weeks is typical. The boundary is agreed upfront and reviewed only when evidence changes the path — not when scope drifts.
When a Sprint is the right first step.
A good fit when the business value is plausible but unproven, when nobody can yet say what production would cost, or when two credible technical approaches are being argued in the abstract.
A poor fit when the use case is already proven and the real constraint is delivery capacity, or when the blocker is organisational rather than technical. In the first case, start with a Pod. In the second, a Sprint will just document what you already suspect.
- A decision owner
- Someone who can act on the recommendation
- Environment access
- Repositories, data and a path through your security review
- Two named experts
- People who understand the domain and the systems
- Time
- Roughly half a day a week from the core participants
Practical detail.
How long does a Sprint actually take?
Six weeks is the common shape. Shorter is possible when the problem is narrow and access is already in place; longer usually means the scope is really two problems and should be split.
What if access takes longer than expected?
It often does, and it is the single most common cause of slippage. We agree the access plan in week one and start the parts of the work that do not depend on it, but we will tell you early if the timeline is at risk.
Do we own what is produced?
Yes, including the code, the evaluation harness and the documentation. The recommendation is written so it stands on its own if you take the next step with another partner or in-house.
What if the answer is no?
Then you have the answer in six weeks rather than after a year of programme funding, along with a written account of why. Several of our best engagements have ended this way.
Where teams go next.
Forward-Deployed AI Pods
The usual next step when the Sprint says the use case is worth building.
Explore Related serviceHow we work
The delivery model behind every engagement: understand, prove, productionize, transfer.
Explore Related serviceAI platform and infrastructure
The shared foundations that make the second and third use case faster than the first.
ExploreHave a candidate for a Sprint?
Describe the problem, what is blocking it, and what a good outcome would look like. We will tell you whether a Sprint is the right shape — and what we would test first.
A senior team member reads every submission. If ADEL is not the right fit, we will say so and point you somewhere better.
A team built around your problem.
An embedded, senior, multidisciplinary team accountable for a defined outcome — working inside your backlog, your repositories and your way of working.
Not a queue of individual CVs.
A Pod is led by an experienced Enterprise Forward Deployed Engineer who can hold a conversation with a business leader in the morning and an architect in the afternoon. Around that lead we assemble the skills the problem actually needs.
The Pod joins your existing way of working rather than running a parallel process. Your ceremonies, your architecture decisions, your definition of done, your security review.
- Accountable for an outcome, not for hours logged
- Senior by default — there is no pyramid to staff
- Composition changes as the problem changes
- Exit criteria written at the start, not negotiated at the end
- Leads
- Enterprise Forward Deployed Engineer
- May include
- AI/ML, software, data, platform, product, security and responsible-AI expertise
- Works in
- Your backlog, repositories, ceremonies, architecture decisions and security processes
- Agreed upfront
- Access, decision rights, communication and engineering standards
- Exit criterion
- Your team can take the next step without waiting for ADEL
What an Enterprise FDE actually does.
The title is borrowed and often misused. Here is the version we hire for.
- Translates
- Turns a business problem stated in business language into a technical problem with measurable success criteria — and back again when the answer needs explaining to a steering group.
- Decides
- Holds real technical decision rights within an agreed boundary, so the Pod is not blocked waiting for a weekly forum to approve a library choice.
- Builds
- Writes production code. This is not an oversight role with a delivery team underneath it.
- Navigates
- Works through security review, data access, procurement and platform constraints as part of the job rather than as someone else's dependency.
- Hands over
- Documents, pairs and coaches from the first week, on the assumption that the engagement ends.
How a Pod runs.
Pods typically run in quarterly cycles with an explicit review at the end of each. Continuing is a decision, not a default. Each cycle has a named outcome, a measure, and a written account of what moved.
If a cycle does not produce what it promised, we would rather have that conversation at the review than let a contract renew quietly.
- Weekly
- Working software demonstrated against the agreed measure
- Fortnightly
- Risk and decision log reviewed with your technical owner
- Quarterly
- Outcome review, ownership check and an explicit continue-or-stop decision
- Always
- Your engineers in the code, not observing it
Practical detail.
How is this different from staff augmentation?
Staff augmentation gives you people who work to your instruction and carry no outcome risk. A Pod is accountable for a defined outcome and brings its own delivery discipline. If what you need is extra hands under your own direction, a Pod is an expensive way to get it and we will say so.
How large is a Pod?
Usually three to six people. Larger than that and the coordination cost starts to outweigh the benefit; at that point it is generally better to run two Pods with separate outcomes.
Do your engineers work on-site?
We work the way your teams work. Most engagements are primarily remote with regular on-site time at the points where it matters most: framing, integration and handover.
What stops a Pod becoming permanent?
The exit criteria, written at the start, and the quarterly review that tests them. Dependency is a failure mode we design against, not a commercial goal.
Where teams go next.
Production Sprint
The usual way in: prove the problem is worth a Pod before you fund one.
Explore Related serviceAgentic software engineering
How the Pod works day to day, and how your team can adopt the same workflows.
Explore Related serviceEnterprise context and knowledge
The groundwork that most AI use cases turn out to depend on.
ExploreReady to embed a team?
Tell us the outcome you need and the constraints around it. We will describe the Pod we would put on it and what we would want agreed before day one.
A senior team member reads every submission. If ADEL is not the right fit, we will say so and point you somewhere better.
AI-native delivery, with the controls intact.
Engineering workflows where AI does real work in the development cycle — without giving up review, traceability, security or the ability to explain how something was built.
Most teams have the tools and none of the workflow.
Licences get bought, individual developers get faster at writing code, and nothing measurable changes at the level of the delivery organisation. The bottleneck was never typing speed.
Real gains come from redesigning the workflow around what AI is now good at: specification, test generation, migration, review support, documentation and the long tail of maintenance work that nobody wants. That redesign has to survive your security model and your audit obligations.
- Where AI writes, and where a human must
- What gets reviewed, by whom, against what standard
- How generated code is traced, licensed and attributed
- What is measured, so the claim of improvement can be tested
- Baseline
- Measure the current cycle honestly before changing anything
- Pilot
- One team, one workflow, one measurable claim
- Controls
- Review rules, traceability, licensing and security sign-off
- Rollout
- Enablement, standards and the internal owner who keeps it alive
- Ends with
- A workflow your engineering leadership can defend
The work that actually moves.
We start with the parts of your cycle where the evidence is easiest to gather and the risk is lowest, then widen.
Test and specification
Coverage on legacy paths, property-based tests, and specifications written before implementation rather than after.
Measurable, low blast radius
Migration and modernisation
Framework upgrades, language migrations and dependency work that has been deferred for years because it is tedious rather than hard.
Bounded, verifiable
Review and operations
Review assistance, incident triage and runbook maintenance — useful, but only with clear rules about what a human still signs.
Human accountability preserved
The part most rollouts skip.
Agentic workflows change what your evidence looks like. If a regulator, an auditor or a customer asks how a change reached production, the answer has to be as good as it was before — ideally better.
We design the control model alongside the workflow rather than retrofitting it once adoption has already spread.
- Traceability
- Which changes were AI-assisted, and to what degree
- Review policy
- What a human must read, approve and sign
- Data boundaries
- What may leave your environment, and what may not
- Licensing
- How generated code is checked against your obligations
- Measurement
- Cycle time, defect escape rate, review load, rework
Practical detail.
Will this make our developers faster?
Individually, often yes, and that is the least interesting part. The question worth asking is whether the delivery organisation ships more working software with the same or better quality. That is measurable, and we insist on baselining it before the pilot so the claim can be tested.
Our security team is not comfortable with code leaving the estate.
That is a reasonable position and it constrains the design rather than blocking it. Deployment models that keep code inside your boundary exist; they trade some capability for control, and we will be direct about that trade.
Do you replace our existing tooling?
Rarely. Most organisations already have the licences. The gap is workflow, standards and measurement, which is where the work usually goes.
How do we stop quality quietly degrading?
By watching defect escape rate and rework alongside throughput, and by keeping the review rules explicit. A throughput gain with a rising escape rate is not a gain, and it shows up in the numbers within a couple of cycles.
Where teams go next.
Forward-Deployed AI Pods
An embedded team that works this way from day one, alongside your engineers.
Explore Related serviceAI platform and infrastructure
The shared foundations these workflows depend on.
Explore Related serviceHow we work
Understand, prove, productionize, transfer.
ExploreWant to test this on one team?
Tell us how your delivery cycle works today and where it is slowest. We will propose one pilot with a measurable claim attached to it.
A senior team member reads every submission. If ADEL is not the right fit, we will say so and point you somewhere better.
Governed context for people and agents.
Most enterprise AI failures are not model failures. They are context failures: the system could not find the right information, or could not be trusted with it.
The demo worked because someone chose the documents.
A retrieval pilot on a curated folder tells you almost nothing about behaviour across a real estate of wikis, drives, ticket systems, contracts and eleven years of email. The hard parts are permissions, freshness, duplication, contradiction and provenance.
We build context as a governed service rather than as a feature of one application — so the second and third use case do not each rebuild it, and so the answer to "why did it say that" is always available.
- Permission-aware retrieval that respects existing entitlements
- Provenance on every answer, down to the source and the version
- Freshness and decay rules, so stale policy stops surfacing
- Contradiction handling, because your sources disagree
- Starts with
- One domain and one set of users, not the whole estate
- Core build
- Inventory, entitlements, ingestion, evaluation and a governed serving interface
- Proves
- Measured retrieval quality against a real question set
- Scales by
- Adding domains to a working service rather than rebuilding per use case
- Ends with
- A context capability your platform team operates
The layers under a trustworthy answer.
- Source inventory
- What exists, who owns it, how current it is, what it is authoritative for, and what should never be indexed at all.
- Entitlement model
- Retrieval that enforces the permissions your systems already define, including at the chunk level where a document mixes sensitivities.
- Structure and enrichment
- Parsing, chunking, metadata and relationships that survive tables, appendices, scanned documents and the formats your business actually uses.
- Evaluation set
- Real questions from real users, with agreed correct answers, so retrieval quality is measured rather than asserted.
- Serving interface
- One governed way for applications, copilots and agents to ask — so policy is enforced once, not re-implemented per team.
Beyond demo accuracy.
"It gave a good answer" is not a measure. We build an evaluation set from questions your users actually ask, including the ones with no good answer, and we track retrieval quality separately from generation quality so you know which half is failing.
Refusal behaviour matters as much as accuracy. A system that confidently answers a question it should have declined is worse than one that says it does not know.
- Retrieval
- Whether the right source was found at all
- Grounding
- Whether the answer is supported by what was retrieved
- Permissions
- Whether anyone can surface what they should not see
- Freshness
- Whether superseded content still appears
- Refusal
- Whether the system declines when it should
Practical detail.
We already have a search platform. Is this a replacement?
Usually not. It more often sits alongside it and uses it as one source among several. Replacing enterprise search is a much bigger programme than most AI use cases need, and we would rather not turn a six-month problem into a three-year one.
How do you handle documents that contradict each other?
First by surfacing that they do, which is frequently the most valuable early output. Then by an authority model that says which source wins for which question, agreed with the business owners rather than decided by us.
Is this just RAG?
Retrieval-augmented generation is one pattern this supports. The work here is the governance, entitlement and evaluation layer around it, which is what determines whether the pattern survives contact with a real estate.
What about personal data?
It is scoped explicitly at the inventory stage, with your privacy function involved from the start. Some sources get excluded, some get masked, and the decisions are recorded rather than implicit.
Where teams go next.
AI platform and infrastructure
Where a context service usually ends up being operated.
Explore Related serviceForward-Deployed AI Pods
The team shape most context work is delivered through.
Explore Related serviceProduction Sprint
A way to test whether context is really your constraint before committing.
ExploreNot sure context is your constraint?
Describe the use case that stalled and what it was retrieving from. Context problems have a recognisable signature, and we can usually tell you quickly.
A senior team member reads every submission. If ADEL is not the right fit, we will say so and point you somewhere better.
Foundations many teams can build on.
Shared capability for model access, evaluation, observability, security, cost control and reliable deployment — so the fifth use case is faster than the first, not slower.
Every team solving the same five problems separately.
Once more than two or three AI use cases are live, the duplicated work becomes visible: each team has built its own key management, its own evaluation approach, its own logging, its own answer to the security questionnaire, its own cost surprise.
A platform is worth building at the point where duplication costs more than the platform does. That point is later than platform teams think and earlier than delivery teams admit — and it is measurable.
- One route to model access, with governance attached
- Evaluation as shared infrastructure, not per-team invention
- Observability that covers prompts, cost and behaviour, not just uptime
- A security posture reviewed once and inherited by everyone
- Starts with
- An honest inventory of what teams have already built separately
- Sequence
- The component with the most duplicated pain, first
- Rule
- Every component ships with at least one live consumer
- Run by
- Your platform team, with our engineers alongside them
- Ends with
- Operated capability, runbooks and a named internal owner
What we typically build.
Not all of it, and rarely all at once. We build the parts where duplication is already hurting and leave the rest until it is.
- Model gateway
- A single governed route to models across providers, with authentication, quota, routing, fallback and audit — and without locking application teams to one vendor.
- Evaluation service
- Shared harnesses, datasets and regression gates so a model or prompt change cannot silently degrade a live system.
- Observability
- Traces, prompt and response capture with appropriate redaction, latency and cost attribution per team and per use case.
- Guardrail layer
- Shared policy enforcement for input and output, applied consistently instead of re-implemented by each application.
- Deployment path
- The route from a working prototype to something operable, with the environments, promotion gates and runbooks that implies.
- Cost control
- Attribution, budgets and alerting, so spend is a managed line rather than a quarterly surprise.
Platforms built too early become shelfware.
The most common failure we see is a capable platform built ahead of demand, adopted by nobody, and quietly decommissioned two years later. The second most common is a platform that becomes a queue every delivery team has to wait in.
We build platform capability out of real use cases, with at least one live consumer for every component. If a component has no consumer, it does not get built yet.
- Teams onboarded
- How many use cases route through the platform
- Time to first call
- How long a new team takes to get running
- Duplicated builds
- Whether teams still go around it, and why
- Cost per use case
- Trend after the platform, not just before
Practical detail.
Cloud provider, vendor product, or build?
Usually a mix, and the split should follow your portability requirements and your existing estate rather than a preference of ours. We are frequently assembling and integrating rather than building from scratch.
How do we avoid becoming a bottleneck for delivery teams?
Self-service by default, with the platform team owning the paved road rather than approving each journey along it. If teams have to raise a ticket to make a model call, they will route around you within a quarter.
Should we build this before our first use case?
No. Build the first use case properly, note what you had to invent, and let the second one tell you what to make shared. Platform work done before that is guesswork with a budget attached.
Who operates it afterwards?
Your platform or infrastructure team. Naming that owner is a precondition of starting, not a question we defer to the end.
Where teams go next.
Enterprise context and knowledge
The capability most often built on top of these foundations.
Explore Related serviceAgentic software engineering
How your delivery teams work once the foundations exist.
Explore Related serviceForward-Deployed AI Pods
The team shape that builds platform capability alongside your engineers.
ExploreWondering if it is platform time?
Tell us how many AI use cases you have live and what each team has had to build for itself. The duplication usually answers the question.
A senior team member reads every submission. If ADEL is not the right fit, we will say so and point you somewhere better.
Vetted people, not a CV pipeline.
Access to AI, engineering and technology professionals from the ADEL Experts Network — assessed by the engineers who would work alongside them.
The people behind the Pods, available directly.
We already assess senior AI and engineering professionals to staff our own delivery work. This service opens that network to clients who need the people rather than the engagement — for a role you are hiring into, a gap on an in-flight programme, or a capability you are building in-house.
Every candidate has been through the same technical assessment we use for our own Pods. If we would not put someone on our own delivery, we will not put them in front of you.
- Assessed by practising engineers, not by a recruiter with a keyword list
- Drawn from the ADEL Experts Network across the UK, Europe and India
- Permanent, contract or embedded, depending on what the work needs
- We tell you when the role as written will not attract the person you want
- Roles
- AI engineering, forward deployment, platform, data, security and technology leadership
- Basis
- Permanent, contract or embedded
- Assessment
- Technical screening by practising ADEL engineers
- Regions
- United Kingdom, Europe and India
- What we will not do
- Send volume, or put forward someone we would not staff ourselves
Not a recruitment agency.
Traditional technology staffing is transactional — CVs, placements, headcount. ADEL operates as a technology partner. We understand the engineering challenges, the AI landscape and the talent market well enough to connect all three.
Three situations this fits.
If none of these describe you, a Pod or a Sprint is probably the better route and we will say so.
Building the in-house team
You have decided AI capability belongs inside the organisation and you need the first senior hires to be right, because they will set the standard for everyone after them.
Permanent placement
A gap mid-programme
Delivery is underway and a specific skill is missing — evaluation, platform, data engineering, security review — and waiting two quarters to hire is not an option.
Contract or embedded
After a Pod
An engagement is ending, ownership is transferring, and you want to hire permanently into the roles the Pod has been covering.
Transfer into permanent
Expertise that matters.
A curated community across the disciplines shaping the future of AI and technology.
AI & Machine Learning
Practitioners building and deploying intelligent systems at enterprise scale.
AI Engineering
Engineers designing, integrating and shipping production-grade AI solutions.
Forward-Deployed Engineering
Specialists working at the intersection of business problems and technical delivery.
Software Engineering
Engineers building the systems, platforms and products that power modern organisations.
Cloud & Platform
Architects and engineers designing the infrastructure that AI and software runs on.
Data & Analytics
Professionals building the data foundations that make AI and insight possible.
Software Architecture
Architects shaping the structural decisions that determine how systems scale and evolve.
Engineering Leadership
Leaders building, scaling and transforming high-performing engineering organisations.
Technology Transformation
Practitioners driving the programmes that modernise how enterprises use technology.
Product & Technology Leadership
Executives and leaders at the intersection of product strategy and technology delivery.
Depth across every discipline. Breadth across every level.
Where the people come from.
The ADEL Experts Network is a curated professional community of AI, engineering and technology practitioners. It exists to connect exceptional people with relevant opportunities, expert conversations and continuous learning.
It is deliberately not a job board. Members are approached about roles that genuinely match their expertise and career stage, which is why the network stays worth being part of.
- The real problem
- What this person needs to accomplish in their first two quarters
- The team
- Who they work with, and who decides
- Constraints
- Location, working pattern, security clearance, budget range
- A decision process
- Who interviews, in what order, and how quickly
Your expertise should open doors.
Join a curated community where your experience can connect you with relevant technology opportunities, expert conversations, learning and industry connections.
Looking for exceptional technology talent?
Connect with ADEL’s curated network of AI, engineering and technology professionals and leaders.
From expertise to opportunity, ADEL connects the right people with the right conversations, capabilities and opportunities.
Practical detail.
Is this a recruitment agency?
Functionally it overlaps, but the assessment is done by engineers who do the work rather than by recruiters matching keywords. That narrows what we can cover and raises what we can vouch for. If you need volume hiring across many generic roles, a traditional agency will serve you better.
How large is the network?
Deliberately curated rather than large. It grows through referral and through people who have worked with us on engagements. We would rather be able to speak personally about the people we put forward.
Can we hire someone who worked in our Pod?
Yes, and it happens often. We would rather your team ends up owning the capability than that we protect a billing relationship. The terms are agreed at the start of the engagement so it is never an awkward conversation later.
Do you place outside AI?
We place across the technology functions that surround AI delivery — platform, data, security and engineering leadership. Roles with no connection to that work are outside what we can assess properly.
Where teams go next.
Forward-Deployed AI Pods
When you need the outcome delivered rather than the people hired.
Explore Related serviceProduction Sprint
When the use case is still unproven and hiring would be premature.
Explore Related serviceAll services
The full set of ways we help enterprises reach production.
ExploreLooking for someone specific?
Tell us the role, the problem behind it and the constraints. We will tell you whether the network has the right people — and whether the role as written will attract them.
A senior team member reads every submission. If ADEL is not the right fit, we will say so and point you somewhere better.
Understand. Prove. Productionize. Transfer.
Four stages, each with an output you can inspect. The sequence matters: nothing moves forward on enthusiasm alone, and every stage can end with a decision to stop.
Understand
Define the business problem, operating context, constraints and success measures.
Problem charter · measures · access plan · initial backlog
Prove
Build the smallest credible proof and test the highest-risk assumptions first.
Working proof · evaluation results · updated risks
Productionize
Integrate security, data, evaluation, observability, performance and operations.
Operated capability · architecture · controls · runbooks
Transfer
Document, coach and establish the team, controls and backlog you will own.
Ownership map · enablement · open backlog · exit criteria
Understand
Most failed AI projects were mis-specified before a line of code was written. This stage is short but it is not a formality.
We want the problem stated in business terms, the constraints stated honestly, and the success measure agreed by the person who will judge the result. If those three cannot be pinned down, that is the finding.
- Who decides whether this worked, and on what basis
- What the process looks like today, including the workarounds
- Which constraints are real and which are habits
- What access is needed and how long it realistically takes
- Problem charter
- The problem, the users, the current process and the constraints, in one page
- Measures
- How success will be judged, agreed by the decision owner
- Access plan
- Systems, data and approvals, with named owners and dates
- Initial backlog
- The first things worth building, in order
Prove
The smallest credible system that tests the riskiest assumption. Not a polished demo — a working thing against real data and real interfaces.
We build the evaluation harness before we tune anything, because otherwise "better" is a feeling. The proof is allowed to be ugly. It is not allowed to be unmeasured.
- Working proof
- Running against real data in your environment
- Evaluation harness
- Repeatable, versioned, and yours to keep
- Results
- Against the measures agreed in stage one
- Updated risks
- What we learned that changes the estimate
Productionize
This is where most of the effort actually goes, and where most estimates are wrong. Security review, data handling, failure behaviour, latency under load, cost at volume, monitoring, and the operational process around all of it.
A proof that took three weeks routinely takes three months to operate properly. We would rather say that at the start than discover it with you in month four.
- Operated capability
- Deployed, monitored and supportable
- Architecture
- Documented, reviewed and defensible
- Controls
- Security, privacy, evaluation gates and audit trail
- Runbooks
- Written for the team that will be on call
Transfer
Handover is not a document delivered in the final week. It starts on day one, because a system your team has never touched is not a system you own.
Your engineers are in the code throughout. By the end, they have operated it, debugged it and changed it while we were still there to ask.
- Named owners for the system, the model and the data
- Runbooks tested by your team, not written for them
- The open backlog, honestly prioritised, including the things we would fix next
- Written exit criteria, agreed at the start and checked at the end
- Ownership map
- Who owns the system, the model, the data and the budget
- Enablement
- Pairing, walkthroughs and documented decisions
- Open backlog
- What is left, prioritised, with our honest view
- Exit criteria
- Checked and signed, or explicitly extended
How this works in practice.
Do you always run all four stages?
No. If a use case is already proven, stage two is a formality and we say so. The stages describe a logical order, not a mandatory package to be sold.
What if a stage says stop?
Then we stop, and you get the written reasoning. This has happened often enough that we treat it as a normal outcome rather than a failure of the engagement.
How do you handle our internal governance forums?
We plan around them from stage one, because architecture boards and security reviews have calendars and those calendars are usually the real critical path.
Can our team run this model without you?
That is the intent. The stages are deliberately simple and the artefacts are deliberately plain, so the approach outlives the engagement.
Have a hard AI problem?
Tell us what you are trying to solve, what is blocking progress and what success would look like. We will help determine the most useful thing to prove or build first.
A senior team member reads every submission. If ADEL is not the right fit, we will say so and point you somewhere better.
Built for high-accountability environments.
ADEL brings production discipline to organisations where reliability, security, explainability and ownership are not optional extras — they are the reason the project exists.
Financial services
Model risk governance, audit trails and change control shape what can ship and how. We work with those constraints rather than treating them as friction to route around, and we expect the security review to be part of the timeline rather than a surprise at the end.
- Document-heavy operational processes
- Client servicing and advisor support
- Control testing and evidence gathering
- Legacy modernisation with AI-assisted delivery
Insurance
Underwriting and claims run on judgement encoded in documents, precedent and the heads of experienced people. The value is usually in making that knowledge findable and consistent, not in replacing the judgement.
- Submission intake and triage
- Claims file summarisation with provenance
- Underwriting guideline retrieval
- Policy wording comparison at scale
Healthcare
We work on the administrative and information layers where the benefit is clear and the accountability model is well understood. Clinical decision-making stays with clinicians, and we are explicit about that boundary from the first conversation.
- Clinical documentation support
- Prior authorisation and coding workflows
- Knowledge access for care teams
- Operational and scheduling analytics
Enterprise technology
For software organisations the question is usually inward-facing: how does AI change the delivery cycle, the support function and the cost of maintaining what already exists.
- Agentic delivery workflows
- Support deflection with grounded answers
- Migration and modernisation programmes
- Platform foundations for product teams
The pattern matters more than the sector label.
If your organisation has real regulatory exposure, real legacy systems, real security review and real consequences when a system is wrong, the work looks similar regardless of the industry on the letterhead. Manufacturing, energy, public sector and professional services all show up in our pipeline for exactly that reason.
Have a hard AI problem?
Tell us what you are trying to solve, what is blocking progress and what success would look like. We will help determine the most useful thing to prove or build first.
A senior team member reads every submission. If ADEL is not the right fit, we will say so and point you somewhere better.
Your team. Your technology. Your ownership.
We are an engineering firm, not a licence reseller and not a body shop. These are the operating principles we will hold to, including when it costs us revenue.
Six commitments.
Written plainly enough that you can hold us to them.
- Technology-neutral by default
- Model and platform choices should follow the problem, the risk, the performance requirements, the cost envelope and your portability constraints. We do not resell licences, we take no vendor commissions, and we do not require a proprietary framework. If the right answer is a tool we have never used, that is the answer.
- Senior people, actually doing the work
- The person who scopes your engagement is on it. We do not run a pyramid, which means we are more expensive per head and cheaper per outcome. It also means we cannot staff every opportunity, and we would rather decline than backfill with people we would not put on our own systems.
- Ownership transfer is the goal, not the exit
- Every engagement has written exit criteria from the start. Your engineers are in the code from week one. Long-term dependency would be commercially convenient for us and bad for you, so we design against it deliberately.
- Evidence over enthusiasm
- Measures are agreed before the build, not selected afterwards to flatter the result. We baseline before we change anything. When the numbers do not support the story, we lead with the numbers.
- We will tell you when to stop
- A clear no reached in six weeks is worth more than a slow yes reached in four quarters. Several of our best engagements ended with a recommendation not to proceed, and we would rather be the firm that says it than the firm that does not.
- Production is the standard
- A demo is not a deliverable. If it cannot survive your security review, your load, your failure modes and your on-call rotation, it is not finished, and we will not describe it as though it were.
When we are not the right choice.
There are several situations where another kind of partner will serve you better, and we would rather say so at the first meeting than at the third invoice.
You need volume at a low rate. Large systems integrators and offshore delivery partners do this well and we do not compete on it.
You need a strategy deck. Management consultancies are better at organisational change and executive alignment than we are.
You need a product. If a mature vendor solves 80% of your problem, buy it. We will happily help you evaluate that honestly, and sometimes that is the whole engagement.
- Model
- Embedded engineering teams, not staff placement
- Seniority
- No pyramid; the scoping engineer stays on the work
- Vendors
- No reseller margin, no commissions, no framework lock-in
- Ownership
- Code, documentation and knowledge transfer with every engagement
- Location
- Built in India, working across Europe and North America
The gap is not ambition. It is production.
Powerful models are widely available and getting cheaper. What remains hard is everything around them: real data, real systems, real security controls, real operating processes and real people who have to trust the output. That gap is an engineering problem, and it is the only thing we do.
Have a hard AI problem?
Tell us what you are trying to solve, what is blocking progress and what success would look like. We will help determine the most useful thing to prove or build first.
A senior team member reads every submission. If ADEL is not the right fit, we will say so and point you somewhere better.
Written by the people doing the work.
Practical guidance for leaders and engineers moving AI from experiment to operated capability. Specific enough to use, honest about the trade-offs.
From AI pilot to production: the readiness checklist
The questions a use case has to survive before it earns an integration budget.
What an Enterprise Forward Deployed Engineer actually does
The role, the decision rights, and why it is not a senior contractor by another name.
Evaluating RAG and agent systems beyond demo accuracy
What to measure when the demo works and you still cannot ship it.
We publish when we have something specific to say rather than on a content calendar. If you want to be told when something appears, say so here.
A professional community for people building the future of technology.
The ADEL Experts Network is a curated community of AI, engineering and technology professionals. It exists to connect exceptional people with relevant opportunities, expert conversations and continuous learning — not to flood inboxes with irrelevant roles.
Expertise
A network built around deep technical and leadership expertise — AI engineering, forward-deployed engineering, software architecture, data engineering and technology leadership.
Community
Connect with peers who understand the challenges of building and leading in an AI-native world. Facilitated conversations, not broadcast noise.
Learning
Access to ADEL Academy programmes — FDE Training, Agentic AI & AI Engineering and AI in the SDLC — for continuous professional development.
Opportunities
Curated technology opportunities matched to your expertise and career stage. Relevant, high-quality and selected — not a generic job feed.
Events
Expert roundtables, practitioner briefings and professional networking events across the UK, Europe and India. In-person and online.
Curated. Connected. Built for people who build things.
How the ADEL Experts Network works.
Join
Join a curated community of AI, engineering and technology professionals. No noise. No generic job alerts. Just the right people.
Connect
Connect with experts, technology leaders, events and industry conversations that are relevant to your expertise and career stage.
Grow
Access learning, insights, roundtables and opportunities to expand your expertise.
Opportunities
Get connected with relevant technology opportunities, organisations and ADEL engagements — matched to your expertise, not your keyword count.
Built around expertise, not resumes.
Have a hard AI problem?
Tell us what you are trying to solve, what is blocking progress and what success would look like. We will help determine the most useful thing to prove or build first.
A senior team member reads every submission. If ADEL is not the right fit, we will say so and point you somewhere better.
From AI pilot to production: the readiness checklist
The questions a use case has to survive before it earns an integration budget.
12 min read · 24 August 2026
Most enterprise AI pilots do not fail. They succeed, get applauded, and then sit unfunded for two quarters while somebody tries to work out what production would actually involve. The pilot proved the wrong thing: that the model can produce a good answer under favourable conditions, chosen by the person running the demonstration.
The checklist below is what we work through before recommending that a use case gets an integration budget. It is deliberately unglamorous. A use case that survives all of it is usually worth building; one that fails three or more of these is usually worth reshaping rather than funding.
1. Is the decision owner identified?
Not the sponsor and not the enthusiast. The person who will be accountable for the output when it is wrong. If nobody can name them, the use case has no home to go to after the pilot ends, and it will stall on exactly that question six months later.
2. Can you state the measure without using the word 'better'?
A production system needs a number, a threshold and a comparison point. Time saved per case against the current baseline. Error rate against the human process, measured the same way. Deflection rate at a stated quality bar. If the measure is qualitative, the system will be judged on vibes, and vibes turn negative the first time someone finds a bad output.
The baseline matters more than the target. Very few organisations have measured how well their existing manual process performs, which makes any claim of improvement unfalsifiable.
3. What happens when it is wrong?
Every system is wrong sometimes. The question is what the wrongness costs and who notices. Map the failure modes explicitly:
- Wrong and obvious: the user sees it and corrects it. Cheap.
- Wrong and plausible: the user believes it. Expensive, and the reason human review exists.
- Wrong and invisible: nothing downstream catches it. This is the one that ends programmes.
If most of your failure modes fall in the third category, you need detection before you need accuracy.
4. Has security seen the real architecture?
Not a slide. The data flows, the retention, the third-party processing, the network path and the authentication model. Security review is frequently the longest single item on the critical path, and it is the one most often discovered rather than planned. Book it during the pilot, not after it.
5. Do you know the cost at volume?
Pilot cost tells you very little. Model the cost at realistic volume, including retries, evaluation runs, the long context you will inevitably end up sending, and the traffic growth that follows a successful launch. Then ask whether the business case survives a doubling, because prices move in both directions and volumes rarely go down.
6. Is there an evaluation set that someone else could run?
A repeatable, versioned set of cases with agreed correct answers, that a person who did not build the system can execute. Without it, no one can safely change the prompt, the model or the retrieval strategy, and the system freezes on the day the original engineer leaves.
7. Who operates it on a Tuesday morning?
A named team, with a runbook they have actually used, a monitoring dashboard they understand and an escalation path. AI systems fail in ways traditional monitoring does not catch: they stay up and get worse. Somebody has to be watching quality, not just uptime.
8. What is the plan for drift?
The model provider will deprecate the version you built on. Your data will change shape. The users will find inputs you never imagined. A production system needs a regression gate that catches degradation before your users report it, and an owner who reviews it.
Using the checklist
Score honestly. Anything that cannot be answered in a sentence is a gap, not a detail. In our experience the two that most often go unanswered are the third and the seventh: what wrongness costs, and who is watching on an ordinary Tuesday. Both are organisational questions wearing technical clothing, which is precisely why they get deferred.
Recognise this problem?
If any of the above describes where you are stuck, describe the situation and we will tell you what we would test first.
A senior team member reads every submission. If ADEL is not the right fit, we will say so and point you somewhere better.
What an Enterprise Forward Deployed Engineer actually does
The role, the decision rights, and why it is not a senior contractor by another name.
9 min read · 24 August 2026
The title has spread quickly and lost precision on the way. It now gets used for solution architects, for pre-sales engineers, and quite often for a senior contractor with a better rate card. Here is the version we hire for, and why the distinction is not pedantry.
The defining feature is decision rights
A contractor executes against a specification someone else owns. A forward deployment engineer owns the specification within an agreed boundary. That boundary is negotiated at the start of the engagement and written down: which technical decisions this person can make alone, which need your architect, and which go to a forum.
Without real decision rights the role collapses into an expensive pair of hands, because every choice queues behind a weekly meeting. Most of the value of embedding a senior engineer is destroyed by that queue.
They write production code
This is not an oversight role. If the person is not in the repository, they will not learn the things that only show up in the repository: the undocumented coupling, the test suite that has been red for eight months, the deployment step that someone does by hand. Those details determine what is actually possible, and they are invisible from a design document.
They translate in both directions
The morning conversation is with a business owner who describes the problem in terms of cases, queues and exceptions. The afternoon conversation is with an architect who needs to know about latency budgets and data residency. The same person has to hold both, or the translation happens through documents and loses fidelity at every hop.
This is the rarest part of the profile and the reason the role is hard to hire for. Strong engineers who can also sit in front of a sceptical business stakeholder and be believed are not common.
They treat access as part of the job
Data access, environment provisioning, security review and procurement are not somebody else's dependency to be tracked in a RAID log. They are the critical path. An FDE who waits to be unblocked will spend a third of the engagement waiting.
They are working towards their own removal
From the first week: pairing, documenting, explaining the decision rather than just making it. The measure of the role is not what the engineer built but what your team can maintain and extend afterwards. An FDE who becomes indispensable has failed at the part of the job that matters most.
Why the distinction matters commercially
If you are buying this role, test for it. Ask what decisions the person will be able to make without escalating. Ask to see code they wrote in the last six months. Ask how the last engagement ended and what the client's team was able to do afterwards. The answers separate the role from the title fairly quickly.
Recognise this problem?
If any of the above describes where you are stuck, describe the situation and we will tell you what we would test first.
A senior team member reads every submission. If ADEL is not the right fit, we will say so and point you somewhere better.
Evaluating RAG and agent systems beyond demo accuracy
What to measure when the demo works and you still cannot ship it.
14 min read · 24 August 2026
A retrieval system that answers twenty curated questions well tells you almost nothing about how it behaves on the twenty-thousandth real one. Demo accuracy is the least informative metric available, and it is usually the only one anybody has collected.
Useful evaluation separates the parts of the pipeline, tests the cases you would rather not think about, and measures behaviour that is not accuracy at all.
Separate retrieval from generation
When an answer is wrong there are two very different causes: the right source was never retrieved, or the right source was retrieved and the model reasoned poorly over it. These require opposite fixes, and a single end-to-end accuracy number cannot distinguish them.
Measure retrieval on its own terms first. Was the necessary document in the returned set at all? Where did it rank? Only then ask whether the generated answer is supported by what was retrieved.
Measure grounding, not plausibility
A well-written answer that is not supported by the retrieved sources is the most dangerous output the system can produce, because it survives casual review. Grounding is checkable: every claim in the answer should be traceable to a specific retrieved passage. Answers that fail this test should be caught by the evaluation, not by a user.
Test the unanswerable
Your evaluation set needs questions with no good answer in the corpus. Questions about things that were never documented. Questions that are out of scope. Questions where two sources disagree and no authority resolves them.
The correct behaviour is to say so. A system that confidently answers a question it should have declined is worse than one that admits the gap, and refusal behaviour is almost never measured because nobody thinks to put those cases in the set.
Test permissions as a first-class case
For every sensitive document, include a case where a user who should not see it asks a question whose best answer lives inside it. This is the failure with the shortest path from technical defect to serious incident, and it is the one least likely to be found by a functional test suite written by the build team.
Agents need trajectory evaluation, not just outcomes
For systems that take multiple steps, the final answer is not sufficient. Two runs can produce the same output while one took three tool calls and the other took forty and burned through the budget. Track:
- Step count and whether it terminates reliably
- Tool selection accuracy: did it choose sensibly at each decision point
- Recovery: what happens when a tool returns an error
- Cost and latency variance across runs, not just the median
Non-determinism means a single pass tells you very little. Run the set repeatedly and look at the spread. A system with a good average and a heavy tail will produce its worst behaviour in front of your most important user.
Build the set from real questions
Synthetic evaluation questions are systematically easier and better-formed than real ones. Real users are terse, ambiguous, full of internal jargon and frequently asking a slightly different question from the one they typed. Harvest from support tickets, search logs and shadowing sessions.
A hundred real questions with carefully agreed answers is worth more than a thousand generated ones, and building it is usually the most valuable week of the project.
Make it a gate, not a report
Evaluation that runs occasionally and produces a document changes nothing. It has to run automatically on every change to the prompt, the model, the retrieval configuration or the corpus, and it has to be able to block a deployment.
Model providers deprecate versions on their own schedule. Without a regression gate, the day your provider retires the version you tuned against is the day your system quietly gets worse, and the first person to notice will be a customer.
Recognise this problem?
If any of the above describes where you are stuck, describe the situation and we will tell you what we would test first.
A senior team member reads every submission. If ADEL is not the right fit, we will say so and point you somewhere better.
The experts behind enterprise AI.
ADEL is an engineering firm. We embed senior Forward Deployed Engineers and AI Pods into enterprise environments to turn hard problems into systems those organisations can operate and own.
The interesting problem moved.
For a while the hard part of applied AI was the model. That has changed. Capable models are widely available, and the difficulty has moved to everything around them: the data that is messier than the diagram suggested, the system that has no API, the security control that was written before any of this existed, and the team that has to trust the output on a Tuesday morning.
That is an engineering problem, and it is poorly served by both ends of the existing market. Strategy firms describe it. Volume delivery partners staff it. Comparatively few organisations put genuinely senior engineers inside the client environment and hold them accountable for something working.
ADEL exists to be that firm. It is a deliberately narrow proposition, and we would rather be the obvious choice for a small number of problems than a plausible choice for all of them.
- What we do
- Embedded AI engineering for enterprise production systems
- What we do not do
- Licence resale, staff placement, strategy decks
- Engagement shapes
- Production Sprints and Forward-Deployed AI Pods
- Sectors
- Financial services, insurance, healthcare, enterprise technology
- Contact
- [email protected]
A firm shaped around the work.
Where we are
ADEL is built in India and works with enterprises across Europe and North America. Most engagements are primarily remote with on-site time concentrated at the points where it changes the outcome: framing the problem, integrating with the estate, and handing over.
Who we hire
Engineers who have operated systems they built, who can explain a trade-off to someone non-technical without condescending, and who are comfortable saying that something will not work. The last one is harder to find than it should be. If that sounds like you, we would like to hear from you.
Four foundational principles.
Our culture and delivery model are anchored by four foundational principles that dictate how we build software, manage relationships, and drive engineering excellence.
Customer Success at the Heart
Your business outcomes are our only true metric of performance. We do not measure success by the complexity of the code we write or the hours we bill, but by the tangible enterprise value we unlock for you. We embed ourselves into your vision, operating not just as an external vendor, but as a deeply aligned technology partner dedicated to your long-term growth.
Ends Over Means
In a rapidly evolving technological landscape, it is easy to get distracted by shiny tools and vanity metrics. We maintain a radical focus on the ultimate objective. Technology — even the most advanced AI — is a mechanism, not a destination. We ruthlessly prioritize the most efficient, secure, and scalable path to solve your actual business challenges, choosing practical high-impact execution over theoretical complexity.
Systemic Transformation Over Siloed Spurts
True innovation is organic and foundational, not cosmetic. We reject the practice of applying superficial, temporary patches or creating isolated AI proofs-of-concept that fail to scale. We specialize in deep-rooted, structural evolution. By reimagining your workflows and data architectures from the ground up, we ensure that the changes we introduce create permanent, compounding advantages across your entire technical estate.
Unwavering Focus, Total Context
The tech landscape is noisy, but we stay relentlessly committed to the mission. We maintain a hyper-focused delivery cadence without ever losing sight of the broader macro-environment or your specific corporate context. By blending deep technical specialization with acute situational awareness, we pivot intelligently when technology shifts, while ensuring your core strategic goals are delivered without compromise.
These are the values behind the work. For the commitments we make on a specific engagement — technology neutrality, seniority, ownership transfer — see Why ADEL.
We build the thing, then hand you the keys.
Model and platform choices should follow the problem, the risk, the performance, the cost and the portability requirements. We bring engineering expertise and shared accountability; you keep the technology, the knowledge and the capability created together.
Have a hard AI problem?
Tell us what you are trying to solve, what is blocking progress and what success would look like. We will help determine the most useful thing to prove or build first.
A senior team member reads every submission. If ADEL is not the right fit, we will say so and point you somewhere better.
Engineering, not slideware.
We hire senior engineers who want to work on hard problems inside real enterprise environments — and who are willing to tell a client something they do not want to hear.
Where we are hiring.
We hire continuously rather than in waves, and we would rather leave a seat empty than fill it badly. If none of these fit but the work does, introduce yourself anyway.
Four conversations, no puzzles.
We do not run algorithm interviews for roles that never touch them. The process is designed to look like the job.
- An intro call about what you have actually built and operated
- A technical conversation over code you bring or code we bring
- A working session on a realistic ambiguous problem, paid if it runs long
- A conversation about how you handle being wrong in front of a client
We aim to give a clear answer within two weeks of the first call, including when the answer is no.
- The work
- Hard problems in real enterprise environments, not internal demos
- Seniority
- Real decision rights; you are not executing someone else's design
- Travel
- Occasional, purposeful, concentrated at framing and handover
- Growth
- Exposure to security, data, platform and business stakeholders in one role
- The catch
- Enterprise constraints are genuinely frustrating; this work requires patience
Interested?
Send us something you have built and a short note on the hardest production problem you have personally owned. We read every one and reply.
A senior team member reads every submission. If ADEL is not the right fit, we will say so and point you somewhere better.
Bring us a hard AI problem.
Tell us what you are trying to solve, what is blocking progress and what success would look like. A senior team member reads every submission.
A real reply, from someone who could do the work.
No qualification call with someone who then hands you to a stranger. The first conversation is with an engineer who would be involved in the engagement.
- We reply within two working days
- The first call is 45 minutes and technical
- If we are not the right fit, we say so and suggest who might be
- Nothing you send is shared outside the people who need to read it
Prefer email? Write to [email protected] with the same detail and it reaches the same people.
Recruiters and vendors: please use email rather than this form, and expect a slower reply.
Message sent.
Thanks — it is with us. A senior team member reads every submission and will reply within two working days.
What makes a first message useful.
The stuck thing
A specific use case that has stalled, and your best guess at why. This is the fastest route to a useful reply.
We can usually respond substantively
The constraint
A known blocker — security review, data access, cost at volume, evaluation — without a use case attached yet.
We will ask two or three questions
The open brief
"We want to do something with AI." We can still help, but the first call will mostly be us asking what hurts.
Expect a longer conversation
How we handle your information.
This notice explains what personal data ADEL collects through this website and in the course of client and recruitment conversations, and what we do with it.
Last updated 24 August 2026
Who we are
ADEL provides enterprise AI consulting and forward deployment engineering services. For questions about this notice or about how your data is handled, write to [email protected].
Our full legal entity name and registered address will be confirmed here before launch. Until then, the email address above is the fastest route to a response.
What we collect
Through this website and our direct correspondence, we collect only what you choose to send us:
- Contact details you submit, such as name, work email and organisation
- The content of your enquiry or application, including any materials you attach
- Correspondence with us by email or during calls
- Basic technical information such as browser type and approximate location, where our hosting provider logs it for security and reliability purposes
This site does not use advertising cookies or third-party tracking pixels. Web fonts are served by Google Fonts, which receives a request from your browser when the page loads.
Why we use it
We use the information you send to respond to your enquiry, to assess whether we can help, to deliver services under a contract, and to consider you for a role where you have applied. We also keep records where we are required to for legal, tax or contractual reasons.
We do not sell personal data, and we do not share it with third parties for their own marketing.
Legal basis
Where the UK GDPR or EU GDPR applies, we rely on legitimate interests to respond to business enquiries and to run our recruitment process; on the performance of a contract where we are delivering services to you; and on legal obligation where retention is required by law. Where we rely on consent, you may withdraw it at any time.
How long we keep it
Enquiries that do not lead to an engagement are kept for up to 24 months so that we have context if you come back to us. Recruitment materials are kept for up to 12 months unless you ask us to remove them sooner. Records relating to client engagements are kept for as long as the contract requires and for the retention period that applies afterwards.
Client data
During engagements we frequently work inside our clients' own environments and systems. Where we process personal data on a client's behalf, we do so as a processor under that client's instructions and under the terms of the relevant contract and data processing agreement, not under this notice.
Your rights
Depending on where you are, you may have the right to access the personal data we hold about you, to have it corrected or deleted, to restrict or object to how we use it, and to receive a copy in a portable format. To exercise any of these, email us and we will respond within one month.
If you are in the UK or EU and are unhappy with our response, you have the right to complain to your local supervisory authority.
Changes
We will update this notice when our practices change, and the date at the top will change with it. Material changes will be flagged on this page.
Terms for using this website.
These terms cover your use of weareadel.ai. They do not govern any engagement between ADEL and a client, which is set out in a separate written contract.
Last updated 24 August 2026
Acceptance
By using this website you accept these terms. If you do not accept them, please do not use the site.
What this site is
This website describes ADEL's services and publishes general commentary. Nothing on it is an offer capable of acceptance, a warranty, or professional advice for your particular situation. Any engagement between us begins only when a written contract is signed by both parties.
Where the site describes typical engagement lengths, team shapes or outcomes, these are illustrative. Actual scope, duration and outcomes are agreed case by case.
Content and intellectual property
Unless stated otherwise, the content of this site is owned by ADEL or used with permission. You may read it, share links to it, and quote short extracts with attribution. You may not republish substantial portions, or use the content to train a model or build a competing service, without our written permission.
The ADEL name and logo are ours. Other names and marks that appear on this site belong to their respective owners.
Acceptable use
You agree not to:
- Use the site in a way that breaks any applicable law
- Attempt to gain unauthorised access to the site or its infrastructure
- Interfere with the site's operation or its availability to others
- Submit anything through our forms that is unlawful, malicious or infringing
- Scrape the site at a volume that degrades it for other users
Material you send us
Please do not send confidential information through the contact form. If you need to share something sensitive, tell us and we will agree a secure route and, where appropriate, a non-disclosure agreement first.
By sending us an enquiry you confirm you are entitled to share what you send, and you allow us to use it for the purpose of responding to you and assessing whether we can help.
Availability
We aim to keep this site available and accurate but we do not guarantee either. We may change, suspend or withdraw any part of it at any time without notice.
External links
This site links to third-party websites we do not control. Those links are provided for convenience and are not an endorsement. We are not responsible for the content, security or privacy practices of any external site.
Liability
To the fullest extent permitted by law, ADEL is not liable for any loss arising from your use of, or reliance on, this website. Nothing in these terms excludes or limits liability for death or personal injury caused by negligence, for fraud, or for anything else that cannot lawfully be excluded.
Changes and governing law
We may update these terms; the date at the top will change when we do, and continued use of the site means you accept the current version. The governing law and jurisdiction will be confirmed here alongside our legal entity details before launch.
Everyone should be able to read this.
We want this site to be usable by as many people as possible, including people using screen readers, keyboard navigation, magnification or reduced-motion settings.
Last updated 24 August 2026
Our target
We aim to meet the Web Content Accessibility Guidelines (WCAG) 2.2 at level AA. That is a target rather than a certification: we test against it, and we expect to find things we have missed.
What we have done
- All text and interface colour combinations on this site meet or exceed the WCAG AA contrast ratio of 4.5:1
- The whole site can be operated by keyboard, with a visible focus indicator on every interactive element
- A skip link at the top of each page jumps straight to the main content
- Headings follow a logical order and landmarks are used so screen reader users can navigate by structure
- Animations and scroll effects are disabled automatically when your system requests reduced motion
- Text reflows without horizontal scrolling down to a 320 pixel viewport, and remains readable when zoomed to 200%
- Form fields have persistent visible labels rather than placeholder-only labels
- Decorative graphics are hidden from assistive technology; meaningful ones carry text alternatives
Known limitations
We are aware of the following and intend to address them:
- The services dropdown in the main navigation opens on hover as well as on click on pointer devices; the click and keyboard behaviour is the primary path and works independently
- Some longer field notes have not yet been reviewed for reading-order issues when a page is heavily magnified
- We have not yet completed testing with every combination of browser and screen reader
Testing
We test with keyboard-only navigation, automated contrast and structural checks, browser zoom to 200%, and reduced-motion settings enabled. We are expanding screen reader testing across NVDA, JAWS and VoiceOver.
Tell us when we get it wrong
If you hit a barrier on this site, please email [email protected] and describe what happened, which page you were on and what you were using. We treat accessibility defects as bugs, not as feature requests, and we will reply within five working days with either a fix or a date.
If you need any information on this site in a different format, ask and we will provide it.