Solution-first data & AI engineering
Some problems don't fit a single specialist. I bring the pieces that have to work together.
Migrating a legacy system, building an automated lead engine, making AI cheap and trustworthy — these need several skills working at once, not just one. I've built each of these, I lean on first-class tooling to automate the rest, and I'd rather show you than tell you. The demos below are live.
Each of these skills is easy to hire for on its own. The hard part — and where I focus — is getting the combination to work together.
It's one person — backed by a heavily automated toolchain that does the work of a team. For you that means one point of contact, fast turnarounds, and a lot shipped per dollar.
Data & System Migration
Moving off a legacy system is where projects die. I de-risk it end to end.
- Map Graph your current system so we know exactly what it does — try it live below.
- Move Pipelines that carry your data clean, bronze → gold, nothing dropped.
- Reconcile Match & dedup records when systems merge (a ~740k-link crosswalk shipped).
- Verify Event-sourced, point-in-time replay proves nothing was lost.
AI Lead-Gen & Marketing Automation
The system that finds and books your customers — the same one running this site.
- Track A first-party pixel on every visitor — no third-party cookies, no banner.
- Identify Resolve anonymous traffic to companies and score them against your ICP.
- Reach AI-personalized outreach written per lead — not templates.
- Book Straight to a booked evaluation call.
Migration · Step 1
Map any system in seconds
Every migration starts by understanding what you have. Paste a public GitHub repo — this maps its internal dependencies and surfaces the keystones (high blast radius), dead code, and cycles, live.
Lead-Gen · Identify
See any company's marketing stack
Paste a company URL — this detects the analytics, ad pixels, CRM, and tools loaded on their site. It's the same engine that turns an anonymous visitor into a scored lead.
AI Cost
Cut your AI bill, keep the quality
Most teams pipe everything to a frontier model. I run a self-optimizing fleet that routes each job to the cheapest model that still passes your quality bar — frontier only when it's actually needed.
typically 5–15× cheaper at the same quality bar
Table stakes, done right
And the standard AI, too
Grounded, cited retrieval is the baseline everyone expects — here it is, working on a public knowledge base. The value I add is above this: making it cheap, calibrated, and wired into your own systems.
Retrieval-augmented answers, grounded in a public knowledge base and cited. Your private corpus would never be exposed like this — the real engine runs server-side on your own data.
Proof
A financial-identifier crosswalk linking and de-duplicating entities across sources with calibrated confidence — the hard part of any data merge.
An event-sourced substrate with point-in-time rewind — every past state reconstructable, so a migration can prove nothing was lost.
A unified engine of typed tools for AI agents, pipelines, enrichment, and outreach — the machinery behind both pillars.
This site runs on the same stack I'd build for you. The demo above is live. The tracking and outreach behind it are the lead engine from Pillar 02.
Migrating off something, or need a pipeline of customers?
Grab a time below — tell me what you're moving or growing, and I'll tell you honestly what's worth doing.
Prefer a link? Open the scheduler → · or hire me on Upwork