LLM consulting for teams that need it to work in production
Most LLM projects don’t fail at the demo. They fail where real users, real data, and real latency budgets show up. We help engineering teams get past that point — retrieval that holds, agents that stay in bounds, and evaluation that tells you the truth. Hands-on, in your stack, and built so your team owns it when we’re done.
You leave the call with a written scope and next steps, whether or not we work together.
Fit
Is this the right kind of help?
A good fit if…
- You have a prototype that works in a notebook and a stakeholder asking when it ships.
- Retrieval quality, hallucinations, or agent reliability are what’s blocking launch.
- You need evals and monitoring your team will actually keep maintaining.
- You want senior hands on the problem, not a slide deck about it.
Probably not, if…
- You want a vendor to own the system indefinitely. We build for handoff, and we mean it.
- You want a recommendation before anyone has looked at your data. We measure first.
- You need a large team on-site for a year. We work as a senior lead embedded with yours.
Packages
Engagements
Fixed scope, a clear deliverable, and a handoff built in. Pick the one that matches where you are — or start with a call and we’ll tell you which.
LLM Strategy Sprint
2 weeks- Use-case prioritization with ROI framing — the two or three worth building first
- Data readiness and risk review: privacy, security, compliance
- Architecture roadmap with milestones your engineers can start on
- Best when: you have budget and ambition, but no agreed first step
RAG / Agent MVP
4–6 weeks- A working, reference-quality system on your data
- Evaluation harness and a test set you keep
- Deployment plan and clean handoff to your team
- Best when: the prototype exists and “production” is the open question
Production Hardening
2–4 weeks- Evals, regression gates, and monitoring wired into your CI
- Latency and cost optimization, measured before and after
- Guardrails and reliability work for the failure modes you’ve seen
- Best when: it’s live, and every change feels like a gamble
Fractional AI Lead
Monthly- Weekly architecture and code reviews
- Model and vendor selection backed by your own evals
- Hiring and upskilling guidance, so the capability stays in-house
- Best when: you need senior judgment on tap without a full-time hire
Process
How we work
- Step 1DiscoveryScope, the success metric, constraints, and how we fit your team’s workflow — written down and agreed before we start.
- Step 2ImplementationShort iterations, each measured against the same eval set. You see progress as numbers, not status updates.
- Step 3OperationalizeMonitoring, documentation, and handoff — or ongoing support, if you’d rather keep us close.
FAQ
Do you sign NDAs?
Yes. We work under NDA, follow your security policies, and can work entirely inside your environment when data can’t leave it.
Do you work with our stack?
Yes — Python and TypeScript, every major cloud, and the usual vector and data stacks. We adapt to your tooling rather than importing ours.
Do you build or just advise?
Both, usually in the same engagement: senior hands on the code, with your engineers alongside so the knowledge stays with your team.
Can you train the team?
Yes. Bruno has taught 170+ live sessions for O’Reilly since 2019, and training is often bundled with an engagement to speed up adoption and handoff.
How do we start?
A 30-minute call. We scope the problem and the success metric, and send you written next steps — whether or not we end up working together.