The audit and the proof of concept, answered in full.
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60 questions on the audit and the proof of concept, answered by Tenhaw, a UK AI consultancy and AI delivery partner based in London. Nothing here is a summary: each answer is the exact text from the page that owns it, and every group links back to that page for the context around it.
AI Readiness Audit
Answered on AI Readiness Audit, and rendered here in the same words.
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What is an AI readiness audit?
An AI readiness audit is a structured assessment of where AI can create measurable value in an organisation, where it is constrained by data, risk or regulation, and whether the workforce and culture are ready to adopt it. Tenhaw's version examines four domains rather than one: the external product a customer touches, the internal processes that run the business, operations, and the software development process itself. It runs 4 weeks at a fixed price of £44,000 and produces a sequenced, costed plan rather than a maturity score.
What does the audit actually look at?
Four domains, in this order. The external product your customers touch, and whether AI belongs in it or only behind it. The internal processes that run the business, traced end to end rather than taken from a process map. Operations, including the run cost of anything already live. And your own software development process, which is usually the domain with the shortest path from a decision to a measurable result, because it is the one place an organisation already instruments itself. Each is ranked against the others as well as within itself, because the first question a board asks is which one to fund.
How much does an AI readiness assessment cost in the UK?
Tenhaw prices the AI Readiness Audit at £44,000 as a fixed fee, whatever the size of the organisation. Large consultancies typically price equivalent assessments between £150,000 and £500,000, our estimate rather than a published figure. The fixed price means the scope is agreed before the work starts and does not expand mid-engagement, and the number does not move once it is in writing.
Should we start with the audit or a proof of concept?
Start with the audit if the question is where to invest across the organisation and you need a board-ready case. Start with a proof of concept if you already know which workflow you want to attack and the question is whether it can actually be done. Some clients run the proof of concept first because a working thing persuades internal sceptics that a plan does not.
Who from Tenhaw actually does the work?
James Rooney leads every audit personally. Where specialist input is needed, it comes from an associate he has already delivered alongside, screened to BS7858 standard before any client access, and every person on your engagement is senior. Tenhaw does not sell work that someone else then delivers, and there is no pyramid of junior consultants.
Should we hire a Head of AI or run an audit first?
A permanent Head of AI search runs six to nine months, and the specification usually gets written from market narrative. The audit takes four weeks at £44,000 and gives whoever you hire a sequenced, costed plan to arrive into rather than a blank page. If you have already made the hire, the audit is the fastest way to give them a defensible first hundred days. It is not a substitute for the permanent role.
What is included in the £44,000 audit price?
Everything the four weeks produce. Two or three candidate workflows built as working prototypes against your real data, inside your own tenancy, with the code yours to keep. A heat map across all four domains, the constraints that would block you, an estimated run cost per candidate workflow, and a costed plan in three-month increments capped at twelve months, presented to your board at the end. The number is fixed in writing before the engagement starts and does not move with scope.
Can the audit sort out the AI tools we have already bought?
It can tell you which of them are earning their keep. Weeks one and two inventory what is actually in use across the business, sanctioned or not, map each tool to the workflows it touches, and show where two or three of them cover the same job. That overlap, and what each costs to run, then sits in the sequenced plan alongside everything else, and consolidation decisions usually land on the do-not-do list. Nothing on the ladder involves us selling you a licence, so this is an assessment rather than a procurement exercise.
What does an AI readiness audit deliver in four weeks?
Working prototypes and a costed plan, not a slide pack. Two or three candidate workflows are built against your real data inside your own tenancy, each with a measured accuracy and confidence read and an estimated run cost per unit of work. Alongside them you get a heat map of where AI will and will not pay across product, internal process, operations and your own software delivery, the constraints that would block you, and a sequenced plan in three-month increments capped at twelve months.
How do you assess whether a data estate is ready for agentic AI?
By building on it rather than surveying it. We trace real workflows end to end, pull your delivery data instead of relying on what the org chart claims happens, and then prototype two or three candidates directly against your data. Readiness shows up fast that way: retrieval quality, how much of the context an agent actually needs is reachable, where provenance breaks, and which workflows quietly depend on a system nobody in the centre knows about.
How do you work out where AI will not deliver value?
The same way we work out where it will, which is why it comes out of the same four weeks. A workflow fails the test when the data is not there, when the risk appetite will not carry an automated decision, when the volume is too low for the run cost to make sense, or when the real constraint is a process problem that agents would simply do faster. Those land on a do-not-do list, with the reason attached, so the plan is defensible in both directions.
Which stakeholders need to be involved in an AI readiness audit?
Fewer than you would expect, and less of their time than a survey-based assessment. The method works by sitting with the people who actually do the work, so it needs access to those teams rather than to a data room. Beyond that: an executive sponsor who can make decisions inside the agreed scope, someone from risk or the second line to agree a risk classification before anything is built, and whoever owns the platform the prototypes run on.
How do you baseline current operating costs so AI savings can be measured later?
In currency, per workflow, before anything is built. Volume, elapsed time, people involved and rework, measured on the workflow as it runs today rather than as it is documented. That baseline is what the prototypes are then measured against, and it is what makes a later claim of saving checkable rather than asserted. It also sets the run cost side, an estimated cost per unit of work, so the operating cost is known before you commit to it.
What security constraints are examined before an AI build starts?
Where the data can go, who can reach it, and what an agent is allowed to touch. Prototypes are built inside your own tenancy on your own contracts, so the constraints examined are the real ones: identity and access, least-privilege scoping for agent permissions, where model inference is allowed to run, what your data-handling requirements mean for retrieval corpora, and what the second line needs to see before a risk classification is agreed.
Can an audit tell us whether our engineering team can build AI-native?
Yes, and it is one of the four domains rather than an afterthought. We measure how your organisation ships today, against its own delivery data rather than against a vendor benchmark, and the prototypes are pair-programmed with your engineers, which is the most direct test there is. Software delivery is usually the domain with the shortest path from a decision to a measurable result, because it is the one place an organisation already instruments itself.
How does this compare with an AI strategy engagement from a large consultancy?
Ours ends in working code and a fixed £44,000. An equivalent large-firm assessment is an estimated £150,000 to £500,000 and typically ends in a document. The difference is the shape of the team rather than the day rate, and ours is a partner, a senior operator and a build engineer for four weeks, with no pyramid to fund. Every figure we publish is on the pricing page with the competitors' own published framework rates beside it.
How do you make sure an audit ends in engineering work rather than slides?
The engineering starts inside the audit itself. By the end of week three, two or three workflows are running against your data and the code is in your repositories. The plan that follows is written against things that already exist, with sequencing grounded in what the prototypes measured rather than in what a workshop guessed. If you stop after the audit, you still keep the prototypes.
What is the return on £44,000 spent before committing to a build?
It is the cheapest way to find out that a workflow does not work. The alternative is discovering it inside a six or twelve month build, where the same finding costs the whole run rate rather than four weeks. The audit produces a measured accuracy read per candidate workflow, an estimated run cost, and a baseline in currency to measure against later. It is priced to be approved without a committee for exactly that reason.
Can the audit be used as AI due diligence before an acquisition?
Yes, scoped to the target or to the business unit under review, at the same fixed price over the same four weeks. The method works by sitting with the people doing the work, so it needs access to them rather than to a data room alone, and four weeks now fits inside most exclusivity periods, where six to eight did not. Both are worth raising on the first call. It is an operational read on what is real, not legal, financial or regulatory due diligence, and it replaces none of those.
Is an agent readiness audit the same as an AI readiness audit?
Same engagement, wider scope. This started life as an agent readiness audit. Agents turned out to be one answer rather than the question, so the rename was a widening rather than a relabel, and the four weeks now cover the product your customers touch, the internal processes that run the business, operations, and your own software development process. The agent-specific part is a decision inventory in the board readout: which decisions in the workflows we examine could move to an agent, which must stay with a person, and the consequence and reversibility test behind each call. Two or three candidates are then built as working prototypes against your real data, at a fixed £44,000.
Can you audit a programme our current AI supplier is already building?
Yes, and it is one of the reasons the audit gets bought. A seven-figure programme is on the table and someone wants the scope sanity-checked before it is signed. Nothing needs to pause while it runs, because the method is sitting with the teams doing the work and tracing real workflows end to end rather than reviewing a data room. What comes back is evidence rather than an opinion: two or three of the candidate workflows built against your real data, each with a measured accuracy read and an estimated run cost, a heat map ranked by return and feasibility, and a do-not-do list. We sell no licences, so nothing in that recommendation is a sale.
What happens in the weeks after the audit readout?
Nothing by default, and that is deliberate. The engagement ends on the date it said it would, nothing rolls on, and the prototype code has been in your repositories since it was written, so there is no handover to wait for. The costed plan is yours to keep even where the recommendation is to stop. It comes in three-month increments capped at twelve months, with decision points marked where it should be re-tested against what you learn, so your own team can start the first increment without us. If you want us for part of it, that is a separate contract: a proof of concept at £20,000 to £55,000, a design team, or programme and delivery management.
How many hours a week does the audit ask of our teams?
Most of it lands on the engineers pairing in weeks two and three, and Tenhaw shapes the rest of the four weeks to sit inside normal work rather than in workshops. Week one is observation, spent sitting with the teams doing the work and tracing real workflows end to end, so the time goes on being watched rather than on preparing material for us. Weeks two and three are the heaviest, because the two or three candidate workflows are pair-programmed with your engineers against your real data. Week four needs your leadership for the readout. How heavy those two weeks get depends on how many of your own engineers you want in the pairing.
Is the readout a document or a session with our board?
Both, and Tenhaw runs the session rather than sending a pack over. The week four readout is presented to your leadership with the prototypes running behind it, so the board watches the two or three workflows work rather than reads about them, and the written deliverable runs to seven sections in a fixed order, from the heat map through the constraint register and the decision inventory to the do-not-do list. The prototype code has been in your repositories since it was written, so nothing is handed over on the day. Who needs to be in that session depends on which of the four domains your board is closest to funding.
Is the heat map a score out of five, or is there evidence behind it?
Evidence, named against each placement. Tenhaw ranks the candidate workflows by expected return against feasibility and writes what sits behind each placement into the readout rather than scoring it out of five, because a maturity model is not something a board can fund from. The same rule keeps sample findings off this page. A worked heat map with plausible rows in it would be an invented result for an organisation nobody has audited. The two or three workflows built as prototypes each carry a measured accuracy and confidence read, which is the hardest evidence in the pack. Which placements get argued over depends on how much of your delivery data is already instrumented.
Is four weeks long enough for an organisation our size?
Yes, because the scope is bounded before the clock starts rather than stretched to fit. Tenhaw agrees how many workflows get built in writing before the engagement starts, and scoping settles which parts of the business the four domains are examined across, so the four weeks go deep on those instead of shallow everywhere. Two or three candidates are then built as working prototypes against your real data, and that depth is the test a timebox has to pass. What four weeks cannot do is cover every operating company in a large group at once, so where to point the four domains is the first conversation rather than a detail settled later.
What if our board challenges the numbers in the investment case?
Then they can rework them in the room. Tenhaw states the value in currency with the assumptions exposed, a confidence level attached to each figure and the arithmetic laid out, so your finance function can run its own numbers through it rather than take a benefit figure on trust. James Rooney leads every audit personally, so the person answering the challenge is the person who did the work, and the two or three prototypes are running behind the readout with a measured accuracy read on each rather than screenshotted into a slide. Which figures get pushed on hardest usually turns on how your finance team already values the time a workflow consumes.
If the audit says do not proceed, do we still pay the £44,000?
Yes, and a recommendation to stop is one of the outcomes Tenhaw builds the four weeks to produce rather than a failure of them. The £44,000 buys four weeks of work, and the finding is the work. You keep the do-not-do list with the reason attached to each entry, the measured accuracy read on each prototype, the constraint register and the costed plan, whether or not you act on any of it. Clients tell us the do-not-do list is usually the most valuable page. More often a stop recommendation reorders which of the four domains to fund and which to leave alone this year, rather than meaning stop everything.
Does the costed plan go stale if we sit on it for six months?
Parts of it do, and Tenhaw writes it to show you which. The plan comes in three-month increments capped at twelve months, with decision points marked where it should be re-tested, so a pause leaves you at a marked point rather than at the top of a document nobody trusts. The estimated run cost per workflow ages fastest, because model pricing moves under it. The constraint register, the operating-cost baseline in currency and the do-not-do list hold longer, because they describe your organisation rather than the market. How much has actually moved depends on what changed in your estate while you waited.
Do the prototypes cost us anything to run after the audit ends?
Only what your own tenancy charges, because that is where they were built. Tenhaw builds the two or three candidate workflows against your real data inside your own infrastructure on your own contracts, so inference and compute sit on your bill during the four weeks and after them, and you can switch them off the day the engagement ends without asking us. An estimated run cost per unit of work for each candidate is part of the deliverable precisely so that number is known before anything is committed. What it would cost at production volume depends on the volumes you would actually put through it.
Can we put the audit prototypes straight into production?
No, and Tenhaw calls them prototypes for that reason. The two or three candidate workflows built in weeks two and three run against your real data inside your own tenancy and produce a measured accuracy and confidence read, which is what grounds the week four sequencing, but four weeks buys evidence rather than a hardened system. The code sits in your own repositories either way, so your engineers can take it forward without us, and the costed plan carries the build cost and the dependencies for doing that properly. What hardening actually costs turns on the volumes and the error tolerance the workflow has to hold.
What if our data access is not ready when week two starts?
It is the dependency most likely to squeeze a fixed four weeks, so Tenhaw front-loads it. Identity and access scoping sits in week one, alongside the risk classification your second line agrees before anything is built, because the prototypes run against your real data inside your own tenancy rather than on ours. How many candidate workflows get built is agreed in writing before the engagement starts, and that scoping conversation is where reachable data gets checked rather than assumed. The price stays £44,000 and the end date stays where it was agreed. Your own provisioning lead time for an account with production-level read access is the number worth confirming first.
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Talk it throughAgentic Proof of Concept
Answered on Agentic Proof of Concept, and rendered here in the same words.
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What is an agentic proof of concept?
A short fixed-price engagement, two to four weeks at Tenhaw, that takes one real workflow and builds a working agentic system against it, in your environment and against your data. The point is to replace an argument about feasibility with a working thing people can use. It is not a production deployment; productionising is scoped and costed separately.
How can a proof of concept take only two weeks?
By treating the requirements as the source code. Every requirement is converted into structured markdown, mapped for relationships, and interrogated for gaps and contradictions before any code is written; the build then runs against the whole requirement set at maximum model reasoning rather than file by file. The full method is published at tenhaw.com/the-tenhaw-way/building-with-ai.
Will our own engineers learn anything, or do you hand over a black box?
The build is pair-programmed with your engineers throughout, deliberately. On a recent engagement the client engineer who paired on a two-week build finished it saying they were 70% confident they could run the process unaided. Seventy per cent after a fortnight is the measured figure, and it is the difference between buying a proof of concept and starting to acquire a capability.
What does an agentic proof of concept cost?
Tenhaw prices agentic proofs of concept between £20,000 and £55,000 as a fixed fee for two to four weeks, depending on the complexity of the workflow and the state of the underlying data. The price is agreed before the work starts and does not move.
Can we contract your engineers by the day instead?
No. Tenhaw is not a staffing agency and does not place people by the day into someone else's plan, which is what lets us publish a rate card and stay accountable for the outcome. Senior people are supplied on an engagement with partner oversight behind them. If you want a single senior lead rather than a build, the closest thing on the ladder is programme and delivery management, where one lead runs delivery and governance without Tenhaw building any of it.
What happens if the proof of concept fails?
You get a documented answer to a question that would otherwise have cost far more to answer, plus the requirement corpus and gap analysis, which retain value regardless. A proof of concept that establishes a workflow is not viable has done its job, and we would rather tell you that in week three than in month nine.
What decides whether we pay £20k or £55k?
The complexity of the workflow, and the state of the data underneath it. Those set the two variables the price is built from, whether one or two Tenhaw engineers are on it and whether it runs two weeks or four, costed from the published senior practitioner rate of £1,250 a day. A tangled workflow with scattered data needs more of both and sits nearer £55,000; a well understood one with its data already in a single place sits nearer £20,000. Either way the number is fixed in writing before anyone starts, and the deliverable is fixed alongside it.
What do you need from us during a proof of concept?
Access, experts and engineers. The system is built in your environment against your real workflow, so we need that environment and the workflow's data from the first day. Your subject-matter experts are wanted around days four and five, once the gap-and-contradiction pass has surfaced what the written requirements contradict or leave out. And engineers of your own to pair with ours throughout, because your people finishing able to run the method is a measured deliverable rather than a hope. Data is the one hard prerequisite, and where it does not yet exist in any usable form, a proof of concept is the wrong place to start.
What happens week by week in a proof of concept?
Days one to three turn your existing requirements, the PDFs, diagrams and decks, into a structured markdown corpus, map the relationships and run the gap-and-contradiction pass, all before any code exists. Days four and five resolve those gaps with your subject-matter experts and record explicitly what we are proceeding without, and why. Week two is the build, paired with your engineers, with a security review roughly every fifth prompt, and it typically reaches around 80% correct by the end of it. Weeks three and four iterate toward roughly 95%, fold user-testing feedback back through the same corpus, and scope what production readiness would require.
Does a proof of concept commit us to anything afterwards?
No. It ends on a date agreed at the start with a fixed-price deliverable, nothing rolls on, and there is no retainer waiting on the other side. You keep the working system, the full requirement corpus as structured markdown, and a costed scope for what productionising it would take, all of it yours to extend or to hand to somebody else. Whether to build for production is a separate decision, and it is a better decision for being made with a working thing and a real number in front of you rather than bought in advance.
When is an agentic proof of concept the wrong thing to buy?
In three situations, all of them cheaper to spot now. An agentic proof of concept is the wrong buy when you do not yet know which workflow to attack, and the audit is the better answer there at £44,000 fixed over four weeks. It is wrong when what you actually need is a production deployment, because this ends in a proof of concept and hardening one for production is a longer, separately scoped job. And it is wrong where the data underneath the workflow does not yet exist in any usable form, because the fortnight then goes on building the source rather than the system.
Can a proof of concept cover more than one workflow?
No, and the narrowness is the point. One real workflow, in your environment and against your data, is what makes a fixed price over two to four weeks honest rather than a guess, and it is what leaves you with a measured result instead of two half-built things. A second workflow is a second engagement, scoped and priced on its own inside the £20,000 to £55,000 band. If choosing between candidates is the real problem, take the one whose result would settle the argument in the room, because that is where a working system earns the most.
Our requirements are out of date. Can we still start?
Yes, and that is the normal starting point. Nothing gets built before the requirements are dealt with. Whatever exists, the PDFs, diagrams and decks, is turned into a structured markdown corpus with the relationships mapped, then interrogated for gaps and contradictions. What that surfaces becomes the agenda for the sessions with your subject-matter experts, and anything still unresolved is recorded as something we are proceeding without, and why. So there is no tidying exercise to run before we start. The corpus is the tidying, and it is yours to keep and extend whatever you decide about the build.
What does a forward-deployed AI engineer do on a proof of concept?
They build, in your environment, next to your people. One or two of them are the whole team on this rung, at the published senior practitioner rate of £1,250 a day, each one someone James Rooney has already delivered alongside. The day-to-day work is turning the requirement corpus into a working system against your real data, pairing with your engineers from the first day so the method stays behind, and running a security review roughly every fifth prompt. If what you want instead is a senior person sitting in your stand-up for six months, that is the build team rather than this.
Do real users test the system before the proof of concept ends?
Yes, in weeks three and four. Week two produces the first working build, paired with your engineers, and the fortnight after it folds user-testing feedback back through the same requirement corpus rather than patching the code around it, iterating toward roughly 95% correct. That is also why the engagement ends with something your executives can use rather than a deck about one. It remains a proof of concept, so the testing answers whether the workflow works, and what a production rollout would take is scoped and costed as its own decision.
What do we show the board at the end of a proof of concept?
Four things: a working system your executives can use rather than a deck about one, a measured read on how much of the method your own people absorbed, a costed scope for productionisation as a separate decision, and evidence of whether this workflow is worth pursuing at all. Boards tend to underrate the last one, because evidence that a workflow is not worth pursuing is far more useful before a build is budgeted than after. And if the difficulty is that a plan will not persuade the sceptics in the room, a working system usually does.
Who builds the production version after the proof of concept?
Whoever you choose. You finish holding the working system, the requirement corpus and a costed scope for production, so the options are real ones: your own engineers, a different supplier entirely, or a Tenhaw agentic build team at £70,000 to £85,000 a month. Weeks three and four exist partly to produce that scope and that number, so the production question gets answered with a working thing and a real cost on the table. Your engineers having paired with ours throughout is what makes the first option a genuine one rather than a courtesy.
Who pays if a fixed-price proof of concept overruns?
We do. The number is agreed in writing before anyone starts and it does not move, and the deliverable is fixed alongside it, so an overrun is our problem rather than a change request pointed at you. That is a sane promise rather than a gamble because of where the unknowns surface. The requirements are interrogated for gaps and contradictions in week one, before any code exists, and whatever cannot be resolved is written down at the time. Fixed-price work goes wrong when that discovery lands in week three, and this sequence is built to stop it.
A software vendor offered us a free proof of concept. Why pay for yours?
Because a free vendor pilot is scoped to prove that product, and Tenhaw has no model, platform or licence to resell or take a margin on. What the fixed £20,000 to £55,000 buys is a working system in your own environment against your own data, one or two engineers pairing with yours while it is built, and the requirement corpus that produced it. If you were always going to buy that product anyway, let the vendor prove it and keep your money. What neither exercise can settle from outside is whether the workflow is worth automating at all, and that turns on volumes and the cost of an error, which only your operation knows.
Will a proof of concept still work at our real volumes?
Not on its own, and Tenhaw scopes that gap rather than glossing it. A proof of concept is built to answer one question, whether this workflow can be done at all, in your environment, against your data, inside two to four weeks. Throughput, error handling and identity carry deliberate shortcuts at that stage, which is why weeks three and four end with a costed assessment of what productionising would take rather than a claim that you are finished. The £20,000 to £55,000 buys the first answer cheaply. What sets the second is your peak volume and how unevenly it arrives, which your operations people already know and no fortnight can discover.
Can a proof of concept run on masked or synthetic data?
Masked data usually works, synthetic data usually does not, and since Tenhaw builds inside your environment under your own policies, with UK data residency by default and EU available, the choice is yours to make. The test is whether the mask preserves the features the decisions actually turn on. Replace a customer name and nothing changes; flatten the messy free text and the result flatters itself. Synthetic records fail that test because they are generated from the rules you already know, so the exceptions quietly vanish. The one hard prerequisite is data that exists in usable form, and whether your masked copy still carries the awkward cases is a question for your data owners.
How do we agree what counts as working before we sign?
In writing, before the fee is fixed. Tenhaw prices a proof of concept at £20,000 to £55,000 against a fixed deliverable, so what counts as working has to be settled first, and the reference it is settled against is the requirement corpus. Your existing documents are turned into structured markdown, interrogated for gaps and contradictions, with anything your subject-matter experts cannot resolve recorded explicitly as something the build proceeds without. Acceptance is then measured against that, rather than against somebody's memory of a meeting. What the corpus cannot decide for you is which specific cases must be right rather than what percentage, because that is a judgement about consequence your process owner owns.
How long does it take to get a proof of concept into production?
On Tenhaw's own live engagement, a proof of concept built in two weeks is being productionised over four to six weeks with a dedicated team. Treat that as the shape rather than a quote for yours. Weeks three and four of your own engagement exist partly to produce the number for the specific thing you have just watched work, which is why the costed assessment is a deliverable of the fixed fee rather than a follow-on proposal. Three things move it: the integration surface, the non-functional requirements your estate imposes, and how accurate the workflow must be before it runs unchecked. The last is a risk decision rather than an engineering one.
If the system breaks a month after you leave, who fixes it?
Your own engineers, and Tenhaw shapes the two to four weeks so that is realistic rather than a hope. They pair on the build from the first day, the code sits in your repositories as it is written, and the requirement corpus behind it is structured markdown they can read and extend, so a fix is a change to something they already know rather than a call to us. Be clear-eyed about what you are running, though. It was built to answer a question quickly, so it is a thing to learn from while you decide rather than one to leave unattended in front of customers. How much attention it needs meanwhile depends on who is using it, and how often.
Our engineers could try this themselves. Why pay for two weeks of yours?
If you have engineers who have already shipped an agentic system and can be freed for a fortnight, run it yourselves, because Tenhaw publishes the method free, failure modes included. What £20,000 to £55,000 buys is that the fortnight actually happens at a fixed price, with one or two engineers at the published senior practitioner rate of £1,250 a day whose only job for two to four weeks is this one, and a gap-and-contradiction pass run by somebody with no stake in which answer it produces. Internal attempts stall on the first of those far more often than the second. Whether your people can genuinely come off their current commitments is the thing to test before you decide.
Whose AI model licences does the build run on?
Yours, wherever you have an approved enterprise tenancy. Tenhaw does not resell or mark up models, platforms or licences, so the build runs inside your infrastructure on your own contracts, and nothing you specify becomes revenue for us. Where there is no approved stack, the default is named up front as Claude Code with OpenAI models and Gemini, best tool for the case, swapped for your preferred stack on request. The £20,000 to £55,000 is the fee for the work, costed from the published £1,250 senior practitioner day rate, with no licence margin underneath it. Whether your existing agreements cover the models this workflow needs is worth checking early, since procuring a new one can outlast a two-week build.
Does the fee come off the price if we go on to a build?
No, and the reason is worth knowing. Tenhaw prices the proof of concept standalone at £20,000 to £55,000, fixed, ending on the date agreed at kickoff. A fee credited against a later build would quietly put a price on the recommendation to stop, and a proof of concept that establishes a workflow is not viable has done its job. What you carry into the production decision instead is a working system, the requirement corpus and a costed scope, so the next number is a real one whoever ends up spending it. Which route that turns out to be depends on engineering capacity you have and we cannot see from here.
Should we pick our biggest workflow or our simplest one?
Neither, quite. Tenhaw's test is three conditions: the workflow's data already exists in usable form, its requirements exist in writing even if they are years out of date, and its subject-matter expert can be in the room on days four and five. Those are what let a fixed price hold over two to four weeks. Add one more test of your own, that your people can check the output quickly, because a result nobody can verify inside a day cannot be user-tested in weeks three and four. The biggest workflow in the business usually fails the first condition, and the simplest one rarely settles the argument you are running the exercise to settle.
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