AI Deployment
Your team is pasting client docs into ChatGPT. They're not telling you.
Off-the-shelf AI is a text box. Your work is files, handoffs, rules, and bad-day exceptions. I start with the repeated job that costs time or trust, put the model inside that workflow, and measure whether the work improves. If the data cannot leave your perimeter, the system is built around that constraint from day one.
What Usually Looks Normal Until I Point At It
AI pilots that look great in a demo and never reach production
Off-the-shelf AI that almost fits, so the team works around it instead of with it
A staff member quietly pasting privileged docs into ChatGPT to keep up
No benchmark or audit trail to prove the system actually works
How I Build It
Start with the business outcome and the metric that proves it
Model selection by task: classification, retrieval, generation, vision, or multi-modal
Local or cloud inference matched to your data-residency needs
Integration-first design that wires AI into the tool the team already uses
Outcomes
Working system in 1–4 weeks with a measurable win
Prospect dossiers cross-reference every call, thread, and record before writing to one buyer.
Technology Stack
Capabilities
Deterministic Retrieval
When accuracy matters, semantic search is not enough. I combine classification routers, citation graphs, and small-to-big chunking so the system retrieves exactly what the query requires.
How It Works
- Classify the query into dimensions that map to known sources
- Retrieve from full-text, vector, and citation-graph indices
Outcomes
- 100% of the relevant law found on the bar exam — the $1,200-a-seat incumbent lost the head-to-head.
- No hallucinated citations
Model Selection & Local Inference
I do not default to the biggest API. I match the model to the task — classification, retrieval, generation, or vision — and deploy on hardware you control when data residency matters.
How It Works
- Task decomposition: classify, retrieve, generate, or see
- Model benchmarking on your data, not the vendor leaderboard
Outcomes
- Runtime cost drops from thousands to dollars per month
- Data stays inside your perimeter
Workflow Integration
AI in a silo is useless. I build REST endpoints, webhooks, and Slack/CRM connectors so the model's output becomes part of the existing workflow — with fallback to humans when confidence is low.
How It Works
- API-first design: RESTful endpoints for your applications
- Trigger AI from your system events via webhooks
Outcomes
- AI embedded in existing workflows
- Human review only where it adds value
Persuasion & Outreach Systems
It builds a dossier on the prospect — every call, thread, and record you have, cross-referenced — and writes the letter for that one buyer. Trained on every sales book you have heard of, and the declassified interrogation science you have not.
How It Works
- Ingest every touchpoint: calls, threads, CRM records, public signals
- Ground the writing in a curated influence corpus — primary sources, not marketing blog summaries
Outcomes
- Outreach written for one buyer, not a segment
- A working dossier the rep reads before the call
Related Case Studies
The research is public.
Benchmarks run on machines I own, measured against frontier systems — with the failed approaches reported next to the results. Read it before you take my word for anything.