Fractional CTO for AI & Machine Learning

As Lead & Architect at Xogito, I shipped the MVP of a GenAI chatbot for a Seattle FinOps startup in one month, reusing the components and infrastructure the team already had. Early customers signed before anyone argued about a rewrite. See the case study ↓

As CTO at MateBio, I kept the frontier model off the hot path. Entity recognition and disambiguation (mapping "p53" or "breast cancer" to exact graph identifiers) ran on a heuristic biomedical NER model and GPT-4o mini. The expensive model only saw the steps that needed it. Typical reduction: 40-70%.

On that same platform I built a GraphRAG pipeline where a researcher's question becomes a validated query over 80+ verified data sources, with provenance and confidence scoring on every answer. Their users can trace any response back to its source. See the case study ↓ You can try a public one: Lenny's Knowledge Graph turns Lenny's Podcast episodes into a graph and a chat, with every entity traced to timestamped transcript excerpts.

I implement multiple layers of protection: retrieval-augmented generation (RAG) with verified sources, confidence scoring, output validation, and human-in-the-loop workflows for critical decisions. For high-stakes applications, I add guardrails and fallback mechanisms.

It depends on your use case, data privacy requirements, and budget. I help you evaluate options based on: model quality for your task, latency requirements, data residency needs, cost at scale, and vendor lock-in risk. Often the answer is "start with one, prepare to switch."

RAG retrieves relevant information and adds it to prompts, which works well for knowledge that changes frequently. Fine-tuning trains models on your data, which is better for specialized tasks or domain-specific language. I usually recommend starting with RAG (faster, cheaper) and fine-tuning only when RAG isn't sufficient.

For RAG systems: as little as your documented knowledge base. For fine-tuning: typically 500-5,000 high-quality examples. For training from scratch: millions (don't do this). Most startups overestimate data needs: foundation models are powerful out-of-the-box.

Yes, this is one of my core services. I add production-grade infrastructure: monitoring, error handling, rate limiting, cost controls, compliance guardrails, and scalability. Typical timeline: 6-10 weeks from working POC to production-ready system handling real users.

I implement data anonymization, on-premise/private cloud deployment options, audit logging, and ensure compliance with GDPR, HIPAA, or SOC 2 as needed. For sensitive data, I can architect AI systems that never send raw data to third-party LLMs.

I build cost controls from day one: prompt optimization, caching strategies, rate limiting, model right-sizing, and monitoring dashboards. I also implement automatic alerts when costs exceed thresholds. Typical cost reductions: 40-70% without quality loss.