What I build
Fixed-scope verticals so your team owns the system after hand-off — not another demo that dies in staging.
-
LLM apps
Product surfaces with prompts, evals, and guardrails — which means you ship a feature operators can monitor, not a notebook demo.
-
Agents
Tool-using workflows that plan, act, and recover — which means APIs get work done and humans still own risky escalations.
-
RAG
Grounded answers with citations you can audit — which means support and ops stop guessing whether the model made it up.
Selected work
Anonymized engagements — real constraints and deliverables, no invented ROI or fake testimonials.
-
Internal knowledge RAG
Constraint: cite sources on every answer · eval harness before launch
- Challenge
- A B2B support org’s scattered docs made answers slow and inconsistent across shifts.
- Approach
- Retrieval pipeline with chunking, indexing, and citation-aware answers over the existing ops wiki and runbooks.
- Outcome
- Operators get grounded Q&A with source links and an eval harness they re-run before each corpus update.
-
Support triage agent
Constraint: human handoff required · no silent ticket closes
- Challenge
- A high-volume support desk burned hours on repetitive routing and knowledge lookups.
- Approach
- Tool-using agent wired to ticket APIs, knowledge base, and explicit escalation rules.
- Outcome
- The agent drafts triage notes with context preserved; risky closes always escalate to a human.
-
LLM product surface
Constraint: regression evals in CI · observable failure modes
- Challenge
- A product team’s prototype prompts worked in notebooks but not as a reliable customer-facing experience.
- Approach
- Hardened prompts, added evals and guardrails, shipped a thin app layer with observability.
- Outcome
- Monitored LLM feature with CI regression checks and operator playbooks when the model fails.
How I work
A short path from first call to something your team can run — ceremony only where risk demands it.
-
Discovery
One focused session to lock the problem, constraints, and success checks — so I don’t build the wrong vertical.
-
Prototype
A thin slice with evals first — prove the approach before you fund a wide build.
-
Harden
Guardrails, observability, and ops paths — so on-call isn’t guessing when the model fails.
-
Hand-off
Docs, runbooks, and an optional retainer — your team owns the system after launch.
Engagements
Pick a starting shape — exact scope and price are set after discovery. No fabricated rate cards.
-
Discovery sprint
Starting at ~1 week, fixed fee
Problem framing, architecture options, success checks, and a written build plan with risks.
-
Build slice
Starting at one fixed-scope milestone
Ship one production vertical: RAG path, agent workflow, or LLM app surface — with hand-off docs.
-
Ongoing support
Starting at a monthly hours-based retainer
Evals, model updates, and iteration after launch — hours agreed up front, cancel anytime after the current month.
About
I’m Alan Redford Hayes — a freelance AI engineer focused on LLM apps, agents, and RAG. I ship systems operators can trust: clear failure modes, measurable quality, and hand-off that sticks. I don’t take chatbot skins with no evals, no ownership, or pressure for fabricated proof.
Get a fit check
Tell me what you’re shipping and what’s broken in prod today. I reply within 1 business day with a fit read, a rough timeline, and whether a discovery sprint is the right next step — no obligation.