Services
From “this might work” to “we can run this.”
The work usually starts with a concrete pain: answers are inconsistent, latency is bad, model spend is climbing, mocks are stale, the architecture is hard to reason about, or the team needs a senior builder to turn an idea into a system customers can actually use.
🔗Problems I solve
🔗AI prototypes that are not production-ready
A demo can hide the hard parts. Production needs evals, telemetry, fallbacks, review flows, deployment discipline, and a way to know when the model is wrong.
I help teams harden AI features so they are observable, testable, and safe enough to put in front of users.
🔗RAG and search systems that need trust
Search quality is not a vibe. Retrieval needs chunking decisions, ranking strategy, freshness controls, citations, and measurement.
I design and build RAG/search systems with pgvector, re-ranking, evaluation harnesses, dashboards, and the operational pieces that keep answer quality visible.
🔗LLM apps with latency or cost problems
Provider choice, retries, streaming, caching, prompt shape, and fallback logic all affect the user experience and the bill.
I build routing and observability layers that make model usage easier to control without turning the product into a pile of one-off API calls.
🔗Backend systems blocking product velocity
Sometimes the AI feature is not the problem. The real blocker is the platform around it: slow APIs, unclear boundaries, poor observability, brittle integrations, or deployment risk.
I help teams modernize the backend path around AI and data workflows so new product work does not keep fighting the same infrastructure problems.
🔗Senior architecture help without a full-time hire
Founders and CTOs often need principal-level judgment before they need another full-time leader. I can review architecture, de-risk a build, mentor the team, or own a focused delivery track.
🔗Engagement paths
🔗AI production readiness review
Best when you already have a prototype or early product and need to know what will break before customers do.
Typical output:
- Architecture and risk review
- Evaluation and observability plan
- Cost/latency/security findings
- Prioritized build plan
- Specific next steps for the team
🔗RAG or search buildout
Best when users need better answers, better discovery, citations, or measurable retrieval quality.
Typical output:
- Ingestion and chunking design
- pgvector or vector database implementation
- Re-ranking and metadata filtering
- Offline evaluation harness
- Dashboards for quality, latency, and cost
🔗LLM routing and cost control
Best when model calls are becoming slow, expensive, or hard to debug.
Typical output:
- Multi-provider routing layer
- Streaming and fallback behavior
- Budget and usage controls
- OpenTelemetry instrumentation
- Runbooks for provider outages and degradation
🔗Backend and platform modernization
Best when the system around the AI feature needs stronger foundations.
Typical output:
- API/service design
- Performance and reliability improvements
- CI/CD and deployment hardening
- Security review and implementation
- Documentation and handoff materials
🔗Fractional principal engineer
Best when the team needs senior technical judgment over time, but not a full-time hire.
Typical output:
- Architecture reviews
- Code reviews and pairing
- Delivery planning
- Technical decision records
- Mentorship and async support
🔗What you get from me
You get a senior engineer who can diagnose, design, build, instrument, and explain the work.
I do not want to leave you with mystery code. The handoff matters: docs, runbooks, tradeoff notes, setup instructions, and the context your team needs to keep moving.
🔗What I do not do
I am not the right fit for vague “add AI to our app” requests with no product owner, no access to real users, and no appetite for measurement.
I am also not the cheapest option if the work is mostly CRUD, theme changes, or generic staff augmentation. I am best used where architecture, production quality, and applied AI judgment matter.