AI engineering, scoped to your business — not the other way around.

I treat prompts like production code — versioned, evaluated, and benchmarked. The result is a model that behaves the same way on Tuesday as it did on Monday, even when traffic doubles.

• System + role design with measurable acceptance criteria
• Few-shot, structured-output, and tool-use patterns
• Eval suites with regressions caught before deploy
• Cost & latency tuning across models

Outcome

Stable, testable LLM behavior — instead of a magic black box.

Most teams don't need a chatbot — they need work to disappear. I wire LLMs into your existing CRM, inbox, docs, and ticketing so processes that used to take a person now run on autopilot.

• Lead enrichment, triage, and follow-up
• Document processing pipelines (OCR + extraction)
• Internal copilots wired to Slack, Notion, Linear
• Background workers with retries, observability, and audit trails

Outcome

Hours of manual ops work, deleted from your week.

I design retrieval pipelines that actually return the right chunks — with permissioning, citations, and grounded answers your team can trust in front of customers.

• Hybrid search (semantic + keyword) tuned to your corpus
• Permission-aware retrieval and per-user scoping
• Citations, freshness, and source-of-truth guardrails
• Evaluation harness for answer quality

Outcome

A copilot your team trusts because it shows its work.

Multi-model routing, fallbacks, streaming, structured outputs, and tool calls — productionized with the boring stuff that actually matters: timeouts, retries, secrets, and logs.

• Provider-agnostic routing (OpenAI, Anthropic, xAI, Google)
• Streaming + tool-use patterns in your stack
• Cost & latency budgets enforced per route
• Secret management and audit logging

Outcome

AI features that don't fall over when traffic shows up.

Most AI bugs are silent. I build eval suites and tracing so your team can ship changes confidently and see, in seconds, why a response went sideways.

• Golden datasets + LLM-as-judge scoring
• Trace-level debugging across tool calls
• Drift detection across model versions
• CI gates so bad prompts never ship

Outcome

AI features you can refactor without holding your breath.

Before AI goes near customers, I review prompts, tools, and data flows for prompt injection, PII leakage, and policy violations — and put guardrails in place that hold up.

• Prompt injection & jailbreak red-teaming
• PII redaction and data-flow review
• Policy and content moderation layers
• SOC 2-friendly logging patterns

Outcome

Ship AI without showing up in a postmortem.