Prompt Engineering
Designing reliable instructions for LLMs to ensure consistent, accurate business outcomes.
Read moreAvailable for new projects
Automate your growth with custom AI systems.
I help businesses ship reliable AI features — prompt engineering, agent workflows, RAG, and automations that actually move metrics.
+30
AI features shipped
6 yrs
engineering
3 days
median first ship
100%
production-grounded
Services
Practical AI engineering for real businesses
I focus on the AI features that move metrics — not demos. These are the engagements I take most often.
AI Workflow Automation
Connecting disparate tools and LLMs into seamless, end-to-end automated processes.
Read moreInternal Knowledge Assistants (RAG)
Building secure chatbots that converse with your proprietary data to boost team efficiency.
Read moreAPI Orchestration
Integrating state-of-the-art AI models into your existing software infrastructure securely.
Read moreSelected work
Featured projects
A few representative engagements across finance, e-commerce, and operations.
Finance
Problem
A mid-market finance team was rekeying invoices from PDFs into NetSuite — eight FTEs, daily, with a 3% error rate.
Solution
An extraction pipeline using GPT-4 Vision + structured outputs, validated against a deterministic rules engine, with a human-in-the-loop UI for low-confidence rows.
Result
90% reductionin processing time
E-Commerce
Problem
A DTC brand was paying for tier-1 support tickets that were mostly answered by their own help center, just buried under bad search.
Solution
A context-aware RAG chatbot trained on product docs, returns/shipping policy, and order data — with grounded answers and citations, plus a graceful handoff to humans.
Result
45% deflectionof support tickets
Operations
Problem
Engineering, ops, and customer success each had their own wikis. Nobody could find anything; people pinged each other instead of searching.
Solution
A semantic search engine across all three corpora with permission-aware retrieval, plus a Slack bot that answers natural-language questions with citations.
Result
3h saved/weekper employee
How it works
A six-step process — boring on purpose
The exciting part of AI is the model. The dependable part is the process around it.
- 01
Discover
We map the workflow you actually want changed and pick the smallest, highest-leverage AI surface to ship first.
- 02
Design
I draft prompts, schemas, and a thin architecture — with explicit acceptance criteria and a clear cost/latency budget.
- 03
Build
Production code from day one — typed, tested, observable, and wired into the tools your team already uses.
- 04
Test
Eval suites and red-team prompts catch regressions before they ship. We measure quality, not vibes.
- 05
Deploy
Vercel, AWS, or your stack — secrets, logs, and rollouts handled. Zero-downtime cutover from your old workflow.
- 06
Optimize
Once it's live, the data is the moat. I tune prompts, retrieval, and routing against real traffic, not synthetic demos.
Toolbox
Skills & tools I reach for
Provider-agnostic by default. The right answer depends on your data and budget.
LLM & Agents
- OpenAI
- Anthropic
- xAI / Grok
- Google Gemini
- Mistral
- Groq
- Tool use & function calling
- Multi-step agents
Retrieval & Data
- Pinecone
- pgvector
- Weaviate
- Hybrid search
- Embedding evaluation
- Chunking strategies
- Document parsing
Evals & Observability
- LLM-as-judge
- Golden datasets
- Langfuse
- OpenTelemetry tracing
- Cost & latency budgeting
- Drift detection
Engineering
- TypeScript / Node
- Python / FastAPI
- Next.js / Vercel
- Postgres
- Temporal / queues
- AWS / Cloudflare Workers
Trust
What clients say
“Shipped our first real AI feature in three weeks. It hasn't been the lead story in a postmortem once — that's a first for us.”
Maya R.
VP Engineering · Series B fintech
“He treated prompts like code. Eval suites, regressions, the whole thing. Our quality stopped being a vibe.”
Daniel K.
Head of Product · DTC e-commerce
“We were ready to hire two ops people. Instead we hired him for a month and the work just… disappeared.”
Priya S.
COO · Healthtech startup
Engagement options
Three ways to work together
From a 90-minute strategy call to a monthly optimization retainer. Honest pricing — no platinum-bundle nonsense.
Strategy Session
$750
one-time
A 90-minute working session for teams who know AI fits somewhere but want a senior second opinion before they spend.
- Architecture & vendor recommendations
- Prompt + retrieval review (if you have one)
- Written follow-up with prioritized next steps
MVP Build
$8k–$25k
per engagement
A focused 2–6 week build that takes a single workflow from idea to production — typed, evaluated, deployed.
- Discovery, design, and a shippable build
- Eval suite + observability included
- Vercel / AWS deploy + handoff doc
AI Optimization Retainer
$3.5k+
per month
Once it's live, the data is the moat. I keep your AI features improving against real traffic instead of decaying.
- Weekly eval review & tuning
- Prompt + retrieval improvements
- Slack channel + monthly readout
Let's build
Have an AI feature that needs to ship without falling over?
Tell me what you're trying to automate. I'll come back with whether it's a 2-week build, a 2-month build, or honestly not a good fit.