Generative AI consulting for enterprise workflows
For leadership teams moving from AI curiosity to AI commitment — without lighting capital on fire.
Most enterprise AI programmes do not fail on model quality. They fail because nobody mapped the workflow the model was supposed to enter, so the pilot works in a notebook and dies at the first handoff. We start at the handoff.
CTOs, COOs and transformation offices with a concrete process in mind and a board asking what it will cost, what it will return, and what happens when the model is wrong.
What you get
Workflow audit at the handoff level
We trace one real process end to end and record where work waits, who re-keys data, and which steps exist only to check the previous one.
Model and vendor selection matrix
Candidate models and platforms scored against your latency, residency, cost and accuracy constraints — not against public benchmarks.
Risk, security and provenance posture
Where the data goes, what gets logged, how an answer is traced back to its source, and what an auditor will ask.
Build-vs-buy financial model
Total cost across both paths over three years, including the maintenance nobody budgets for.
90-day deployment roadmap
Sequenced so the first measurable result lands before the budget review, not after it.
Internal enablement plan
Who needs to be able to run, evaluate and challenge the system once we leave.
How the engagement runs
Discovery call
One conversation with an engineer to establish whether AI belongs in the process at all. If it does not, we say so.
Workflow trace
Two to three weeks observing the real process, including the spreadsheet nobody mentions in the org chart.
Architecture and modelling
Target design, vendor scoring, and the financial case, reviewed with your team rather than presented to it.
Roadmap handover
A sequenced plan your engineers can execute, with the evaluation criteria defined before anything ships.
How it's measured
Every engagement defines its success metric before the build starts — cycle time, error rate, throughput or cost per case — measured against a baseline we record during the workflow trace. No baseline, no claim.
Frequently asked
- How long does a consulting engagement take?
- Typically four to six weeks from discovery call to roadmap handover, depending on how many systems the workflow touches and how quickly we can observe the real process.
- Do we have to use your implementation team afterwards?
- No. The roadmap is written so your own engineers can execute it. Some clients hand it to an internal team, some to another vendor, some back to us.
- What if the conclusion is that we should not use AI here?
- Then that is the deliverable. A documented reason not to spend the budget is worth more than a pilot that quietly stalls after nine months.
- Which models and vendors do you work with?
- We are not resellers and hold no vendor commitments. Selection is driven by your latency, data-residency, cost and accuracy constraints, and the matrix shows the scoring so you can challenge it.
- How do you handle data residency and confidentiality?
- The provenance and security posture is part of the audit, not an afterthought: where data is processed, what is retained, what is logged, and which options keep processing inside your own boundary.