Generative AI consulting
Most organisations we meet have already run a generative AI pilot. It worked in the demo, impressed a steering committee, and then stalled somewhere between “promising” and “in production.” The gap is rarely the model. It’s evaluation, cost at volume, and the integration work nobody scoped.
Where pilots stall, and what unsticks them
No definition of correct. A demo is judged by whether the output looked good to the person presenting it. Production needs a measurable bar agreed in advance: a fixed test set, a scoring method, and a number you’d defend. Without it, nobody can approve the launch because nobody can say whether it works.
Cost that only appears at volume. Per-query costs that are invisible at 50 test queries become a line item at 50,000 a day. Prompt size, retrieved context, retries and model tier all compound. This is arithmetic you can do before building, and it occasionally kills the use case. Better to learn that in week one.
Integration was never scoped. The model was the easy part. Authentication, rate limits, audit logging, PII handling and a rollback path are the work. Pilots skip all of it by definition.
No owner after launch. Generative systems drift as prompts, models and source documents change. Somebody has to own the evaluation suite. If that’s nobody, quality degrades quietly.
What we do
Use-case triage. Score candidate use cases on value, feasibility and risk before committing engineering time. Some of what gets proposed is better solved with search, a rules engine, or a form.
Evaluation design. Build the test set and scoring harness first, so every later decision is measurable rather than argued.
Architecture and build. RAG pipelines, agents, fine-tuning where behaviour needs changing, and the data plumbing underneath.
Cost and latency engineering. Model routing, caching, prompt compression and context discipline. Usually the difference between a viable unit economic and an abandoned project.
Guardrails and compliance. PII handling, output filtering, audit trails, and the GDPR questions your legal team will ask. Our engineers work inside the EU, so the data itself never leaves the perimeter.
How we engage
Senior engineers embedded in your team, billed time and material. No fixed scope: generative AI work changes direction in the first weeks almost without exception, and fixed-price contracts turn each of those changes into a commercial negotiation.
Engineers start within 14 days, at 50–70% below equivalent US senior consulting rates. Current figures by role and market are on the daily rates benchmark.
Go deeper
- AI consulting services — broader AI engagements
- Machine learning consulting — classical ML and forecasting
- What is RAG? — grounding models in your own documents
- Prompt engineering — reliable output from the generation layer
- Hire AI engineers — embed LLM engineers directly
Have a pilot that hasn’t shipped?
Bring it to a free 30-minute call. You get a specific read on what’s blocking production, what it would take to clear it, and whether the unit economics work at your volume.
staffai.eu · Senior AI and data engineers from Eastern Europe, on T&M