Production LLM systems that ship
Consulting and training for teams building RAG, agentic workflows, evaluation, and deployment. Practical, measurable, and designed for reliability—not demos.
Fastest path: 30 minutes on a call → clear scoped plan.
RAG & Retrieval
Data pipelines, chunking/search, evals, and production rollouts.
Agents & Workflows
Design patterns, tool use, guardrails, and failure-mode handling.
Evaluation & Observability
Quality, latency, cost, and monitoring you can trust.
Ways we work
Consulting engagements
LLM Strategy Sprint
2 weeks- Use-case selection and ROI framing
- Data readiness
- risk review
- Architecture roadmap
RAG / Agent MVP
4–6 weeks- Working prototype with reference implementation
- Evaluation harness and test set
- Deployment and handoff plan
Production Hardening
2–4 weeks- Evals, monitoring, and regression tests
- Latency/cost optimization
- Guardrails and reliability improvements
Fractional AI Lead
Monthly- Weekly technical advisory and reviews
- Model/vendor selection support
- Hiring and team upskilling guidance
Results & credibility
Proof
“Bruno helped us ship an LLM feature with a rigorous evaluation approach and clear engineering tradeoffs.”
J. Durbin
“Pragmatic, fast, and deeply technical — exactly what we needed to move from prototype to production.”
E. Garner
“The training was hands-on and immediately useful for our team’s day-to-day work.”
R. Thompson
Also:
- • O’Reilly Live Training instructor (LLMs, agents, and modern ML workflows)
- • Writing on practical AI implementation: data4sci.substack.com
Simple process
How we work
1. Diagnose
Clarify goals and/or constraints
Use-cases, data, security, and success metrics.
2. Build
Ship an MVP with evals
Focus on reliability and measurable improvements.
3. Harden
Operationalize
Monitoring, iteration loops, handoff, and training.
Ready to scope a project?
Book a 30-minute call and we’ll leave with next steps.