Generative AI consulting that ends at a system in production.
Most generative AI work stalls between the demo that impressed the board and the system nobody trusts enough to switch on. We take the whole path — use-case selection, retrieval architecture, evaluation, rollout.
- A convincing prototype that nobody will put in front of customers.
- No agreed way to tell whether the output is good enough to ship.
- Answers that are fluent, confident, and wrong often enough to matter.
- A model bill that grows faster than the value it returns.
What’s included
Use-case selection
Which problems generative AI actually pays for, scored against effort and measurable return.
Retrieval architecture
Grounding answers in your own documents, policies, and product data — kept current without retraining.
Evaluation harness
A test set and scoring method that tells you whether a change made the system better or worse.
Guardrails and review
Refusal behaviour, escalation paths, and human approval where the cost of being wrong is high.
Integration
Wired into the systems the work already lives in, not a separate tool people forget to open.
Monitoring
Live quality and cost tracking, so regressions surface before your users report them.
How we work
- Map~1 week · fixed fee
AI Opportunity Sprint
Find where AI creates real value — and what it’s worth.
- Workshop with your team
- Prioritized use-case backlog
- ROI model for the top opportunities
- Prove3–4 weeks
Production PoC
Build one use case for real — and measure it.
- One use case built production-grade
- Evaluated against a real KPI
- Evidence-based go / no-go
- Ship6–12 weeks
Build & Integrate
Put the system into production — integrated and compliant.
- Full build, integrated with your stack
- Security & compliance built in
- Live, monitored, documented
- ScaleOngoing
Scale & Enable
Expand across workflows — and level up your team.
- Rollout across teams and workflows
- Evals & monitoring in place
- Team enablement + fractional AI leadership
Retrieval before fine-tuning, almost every time
The common instinct is to fine-tune. It is usually the wrong first move. Fine-tuning teaches a model a style, a format, or a narrow task — it does not reliably teach it facts, and it freezes whatever it learned on the day you trained it. The moment your policy document changes, a fine-tuned model is confidently out of date and gives no sign of it.
Retrieval-augmented generation solves the problem most enterprises actually have, which is that the valuable knowledge sits in their own documents. You keep the source of truth where it already lives, retrieve the relevant passages at question time, and the model reasons over text you control. Update the document and the answer updates with it.
Fine-tuning earns its place once retrieval is working and a specific gap remains — an output format you cannot get reliably through prompting, a domain vocabulary the base model keeps mangling, or a latency budget that a smaller tuned model can hit and a larger general one cannot.
Grounding is a data problem wearing a model costume
Retrieval quality sets the ceiling on answer quality. If the right passage is not in the retrieved set, no amount of prompt engineering recovers it — the model will produce something plausible from what it was given, which is the failure mode people call hallucination and mistake for a model defect.
That makes chunking, metadata, and access control the real engineering. Documents have to be split so that a retrieved fragment carries enough context to stand alone. Permissions have to travel with the content, so a retrieval layer cannot quietly surface a document the asker was never allowed to read. Stale content has to be evicted rather than out-ranked.
Evaluation is the deliverable, not the paperwork
Teams that ship generative AI have a way to answer one question: did that change make it better? Without an evaluation set, every prompt tweak is a coin flip judged on the last three examples someone happened to try.
A working harness needs a set of real questions with agreed-good answers, a scoring method appropriate to the task, and a regression run that gates changes. It is unglamorous and it is the single strongest predictor of whether a system survives contact with real users.
- How is this different from hiring a generative AI developer?
- A developer builds what you specify. The harder problem is usually deciding what to specify — which use case returns value, whether retrieval or fine-tuning fits, and what standard the output has to meet. We do that alongside the build.
- Do we need our data in order before we start?
- No. The AI Opportunity Sprint assesses what data you have and what state it is in, and the answer shapes the plan. Data readiness is an input to the roadmap, not a prerequisite for talking to us.
- Which models do you work with?
- We stay model-agnostic and choose per use case against quality, latency, cost, and where the data is allowed to go. Systems are built so the model can be swapped without rewriting the application around it.
- How do you handle sensitive or regulated data?
- Access control travels with the content through the retrieval layer, deployment can stay inside your own cloud, and approval gates sit wherever the cost of a wrong answer is high. Our engineers have delivered in regulated banking environments.
- What is the smallest way to start?
- The AI Opportunity Sprint — about a week, fixed fee, ending in a prioritized use-case backlog and an ROI model. It is designed as a low-commitment front door.