Most AI consulting engagements produce a strategy document and a list of use cases nobody builds. Ours are run by the engineers who will build the first one, so every recommendation has already been checked against your data, your systems and your budget.
Two to three weeks. We inventory your data, systems, security posture and team, score each candidate use case on value and feasibility, and tell you plainly which ones are ready, which need data work first, and which are not worth doing. You get a written report you can take to the board.
A sequenced plan with owners, budgets and dependencies rather than a vision statement. Which use cases go first, what has to be true about your data before the second wave, where you buy versus build, and what governance has to exist before anything touches a customer.
Workshops with the people who run the process, not just their managers. We map the workflow as it actually happens, find where judgment is repeated at scale, and build the ROI model against your own volumes and labour costs so finance signs off on numbers they recognise.
The unglamorous part that decides whether the pilot survives contact with production. Data pipelines, retrieval design, model and vendor selection, integration with the systems of record, cost modelling per transaction, and a security review your CISO will accept.
Where large language models and autonomous agents genuinely pay off, and where a rules engine would do the job cheaper. We have 32 agents in production across finance, procurement, sales and clinical operations, so the advice comes from what has held up, not from a vendor roadmap.
Model risk policy, human in the loop design, evaluation and monitoring, audit trails, and the regulatory mapping your sector requires: HIPAA and HITECH in healthcare, model risk management in financial services, the EU AI Act if you operate in Europe. Designed in before launch, because retrofitting it is what stalls programmes.
Four phases. You can stop after any of them with something useful in hand. Hover or tap a phase to see what it involves and what you receive.
Interviews with process owners, a data and systems inventory, a security and compliance review, and a scored list of candidate use cases. You receive a written assessment with a clear recommendation on where to start, or a clear recommendation not to start yet and what to fix first.
The top use cases sequenced with dependencies, budgets, owners and a build versus buy decision for each. An ROI model built on your volumes. Architecture and vendor recommendations with the cost per transaction worked out. A governance baseline: what must exist before anything reaches a customer or a patient.
The first use case built by the same team that assessed it, on real data, with human review gates and evaluation from the first week. The goal is not a demo. It is a system running on live volume with measured results you can put in front of the board, and a decision about the next one.
Wave two and three from the roadmap, an internal team trained to own what has been built, monitoring and model evaluation running as a routine, and a quarterly review of what the models are doing against what they were meant to do. We stay as long as you want us and leave you able to run it without us.
AI consulting is sold by very different kinds of firm. Here is what each one is actually good for, including the cases where it is not us.
If you need a transformation programme with change management across tens of thousands of staff and a brand the board already trusts, hire one. Expect strategy from partners and delivery from a rotating bench, a long runway before anything ships, and pricing to match.
Small teams of practitioners who will train your leadership, run offsites and write a good strategy. Many do not build. If you already have an engineering team that can take a roadmap and execute it, this is often enough.
A strong individual can be excellent value for a vendor evaluation or an architecture review. They cannot staff a build, carry compliance obligations, or be there in two years when the model drifts.
The end state we work toward with every client. The failure mode is starting here: an internal team learning production AI on a live customer process, without having seen what goes wrong. Most organisations do better bringing that experience in for the first one.
The people who assess your readiness are the engineers who build the pilot and take it to production. Recommendations are checked against your data before they reach a slide. 32 agents already running in production means the advice comes from what has held up, and a fixed price after the assessment means no surprise in month four.
If your data is not in a state to support the use case, if the process is too inconsistent to automate, or if a rules engine would do the job for a tenth of the cost, the assessment will say so. Around one in four assessments end with a recommendation to fix something else first. That is the report doing its job.
Each number is from a system we assessed, built and shipped. Each card links to the full case study.
See all case studiesThe same discipline, applied to the processes each sector repeats at scale.
Vendor neutral by design. We recommend what fits your data, your cloud and your compliance obligations, then build on it. The models are the cheap part; the retrieval, evaluation, human review and audit layers around them are where the engineering lives.
Frontier and open weight models chosen per use case on quality, latency, cost per call and data residency. Never a single vendor bet.
How a model becomes a system that does work: grounded in your documents, able to call your APIs, and stopped by a person when confidence is low.
Built inside the cloud you already run, defined in code so environments are reproducible and auditable.
What lets a regulated organisation run this in production: every prompt and output logged, secrets managed, access controlled, drift watched.
If your organisation has standardised on a platform, we build inside it. The assessment maps our recommendations to what you already license before anything new is proposed.
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A readiness assessment of your data, systems and team; use case discovery with a business case built on your own volumes; a sequenced roadmap with budgets, owners and build versus buy decisions; architecture, model and vendor selection; governance and responsible AI design; and, in our case, delivery of the first use case to production by the same team. The distinction that matters is whether the firm builds. Many produce the roadmap and leave.
A pure consulting firm advises and hands over; a pure development company builds what it is told to. The failure mode of the first is a strategy nobody executes, and of the second is a well built system that solves the wrong problem. We run both sides in one team: the engineers who assess your readiness build the pilot, so recommendations are checked against your data before they reach a slide, and the build starts from a business case rather than a feature list.
Two to three weeks of structured work: interviews with process owners, a data and systems inventory, a security and compliance review, and a scored list of candidate use cases on value and feasibility. You receive a written report with a clear recommendation on where to start, what has to be fixed first, and what is not worth doing. Roughly one in four assessments end with advice to fix data or process before automating anything, and that is the report doing its job.
A readiness assessment is a fixed fee engagement, typically in the low tens of thousands of dollars depending on how many business units and systems are in scope. Roadmap and business case work is similar. A pilot to production build is quoted as a fixed price after the assessment, because by then the scope, the data and the integrations are known. We do not bill open ended day rates for discovery, and we do not quote a build before we have seen the data.
The assessment takes two to three weeks and the roadmap two to four. A first use case typically reaches production, running on live volume with measured results, eight to twelve weeks after that. So a realistic path from first conversation to a system the board can see working is four to five months. Anything much shorter is usually a demo, and anything much longer is usually a programme that has stopped shipping.
Yes, and we prefer to. The assessment maps our recommendations to what you already license before anything new is proposed. We build inside AWS, Azure or Google Cloud as you run them, integrate with your systems of record through their APIs, and choose models on quality, latency, cost per call and data residency rather than on a vendor relationship.
Security review is part of the assessment, not an afterthought. In production every prompt and output is logged, secrets are managed, access is role based, and sensitive data is redacted before it reaches a model where the use case allows. In healthcare we work under a Business Associate Agreement to HIPAA and HITECH; more broadly we deliver to SOC 2 Type II controls. Your data is never used to train models.
We have 32 agents in production across finance, procurement, sales, IT operations and clinical workflows, so our advice on where large language models and autonomous agents pay off comes from what has held up in production rather than from a vendor roadmap. Part of that advice is regularly that a rules engine or a simpler model would do the job for a fraction of the cost. We will say so when it is true.
Healthcare and life sciences most deeply, then financial services and insurance, logistics and supply chain, SaaS and technology, and legal and professional services. The published case studies on this site cover accounts payable, prior authorization, insurance eligibility, medical billing, renewal risk and patient outreach, each with measured results.
Yes, and it happens often. If your data cannot support the use case, if the process is too inconsistent to automate safely, or if the honest ROI does not clear your hurdle rate, the assessment will say that in writing. We would rather lose a build than deliver one that fails in production with our name on it.
A readiness assessment tells you where AI will pay off in your operation, what has to be fixed first, and what to leave alone. Fixed fee, written report, no obligation to build with us afterwards. If the answer is that you are not ready, you will hear that too. Already know what you want automated? Go straight to our AI automation services.
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