Services
Specific engagements. Specific outcomes.
Specific sells better than “software consulting.” Here are the four ways I most often help teams ship AI.
01
AI Architecture Review
For teams exploring AI but unsure where to start.
A focused review of your goals, data, and constraints, ending in a concrete architecture and a build plan you can act on: model choice, retrieval strategy, evaluation approach, cost envelope, and the risks worth de-risking first.
Typical deliverables
- →Reference architecture diagram
- →Build vs. buy recommendation
- →Cost & latency model
- →Prioritized risk list
02
RAG / Internal Knowledge Assistant
For companies that want an assistant grounded in their own content.
An end-to-end retrieval-augmented assistant: ingestion and chunking, embeddings, a vector store, grounded generation with citations, and evaluations so you can trust the answers. Built to be observable and maintainable, not a one-off demo.
Typical deliverables
- →Ingestion + embeddings pipeline
- →Grounded answers with citations
- →Evaluation harness
- →Observability dashboards
03
LLM Guardrails & Safety Layer
For teams that need safer, more predictable AI workflows.
A middleware layer for prompt/response validation, PII detection and redaction, jailbreak and injection checks, and policy enforcement, with tests and documentation so your team can extend it. The difference between a demo and something you can put in front of users.
Typical deliverables
- →Validation middleware
- →PII detection & redaction
- →Injection / jailbreak checks
- →Test suite + docs
04
Fractional AI Tech Lead
For startups or teams that need senior AI/backend guidance.
Ongoing, hands-on technical leadership without a full-time hire: architecture decisions, code review, mentoring, and unblocking the hard parts of shipping AI features. I work alongside your team and leave them more capable than I found them.
Typical deliverables
- →Architecture & roadmap input
- →Hands-on implementation
- →Code review & mentoring
- →Vendor / model strategy