AI consulting services that ship code, not slide decks
Most AI consulting engagements end with a recommendation and a roadmap. Someone else then has to build it, usually with less context than the people who wrote the deck. This is the other kind: senior engineers who scope the problem, design the system, write the code, and stay until it runs in production.
What we actually do
Feasibility and scoping. Before anything gets built, an engineer works through your data and your business question. A significant share of AI projects fail because the problem was framed wrong at the start: a retrieval problem treated as a fine-tuning problem, or a forecasting question a statistical model answers better and cheaper than an LLM. That determination takes days, not months, and it’s the cheapest work in the engagement.
Architecture. Model selection, data flow, evaluation strategy, and the cost envelope at your expected volume. Written down in enough detail that your own engineers can argue with it.
Delivery. The same engineers implement it. RAG pipelines, AI agents, fine-tuning, inference optimisation, and the data infrastructure underneath.
Handover. Documentation, evaluation harness, and pairing with your permanent team so the capability doesn’t leave when the engagement does.
How to evaluate any AI consulting firm
Four questions separate firms that build from firms that advise:
- Who writes the code? If the answer involves a delivery partner you haven’t met, the people who understood your problem are not the people solving it.
- What does the engagement produce in week two? A working spike beats a discovery phase. If nothing runs until month three, the risk sits entirely with you.
- How is evaluation defined? Any AI system needs a measurable definition of “working” agreed before build. Firms that skip this ship demos.
- What happens when the model underperforms? The honest answer is “we change approach and tell you.” Watch for firms whose commercial structure makes that expensive.
Why we bill T&M and not fixed price
Fixed-price AI work requires a specification you can defend before anyone has seen how the model behaves on your real data. That specification is a guess. When it turns out wrong, and with AI it usually does inside the first fortnight, a fixed-price contract makes changing direction a commercial negotiation instead of an engineering decision.
Time and material puts the incentive in the right place. If the retrieval approach beats the fine-tuning approach, we switch. No change order, no argument about whether it was in scope.
Rates by role and market are published on the daily rates benchmark and updated monthly.
Where our engineers come from
Every engineer is based in Eastern Europe with a minimum of seven years of commercial experience and production AI work in recent history. They sit inside the EU legal perimeter, so GDPR applies by default and you need no data transfer mechanism to give them access to regulated data.
Rates run 50–70% below equivalent US senior consulting rates, and the gap is not explained by seniority. See why Eastern Europe.
Go deeper
- Generative AI consulting — LLM-specific engagements
- Machine learning consulting — classical ML, forecasting and MLOps
- AI development services — build-focused engagements
- AI agent development — agentic systems in production
- Hire AI engineers — embed engineers directly in your team
Start with the problem, not the proposal
Bring the business question and whatever data you have to a free 30-minute call. You get an engineer’s honest read on whether AI is the right tool, what it would take, and roughly what it costs. If the answer is that you don’t need us, we’ll say so.
staffai.eu · Senior AI and data engineers from Eastern Europe, on T&M