Fig. 001 — The Job Description
You wouldn't hire someone without defining the job — yet that's how most companies adopt AI. I work the other way, and it's why my automations stick.
Nothing in the built world is accidental — someone surveyed, drafted, and built it to tolerance.
Every hire inherits the pile and adds to it. That's why hours go to work a machine should finish in seconds.
I survey how your company actually works, draft the system that should exist, and build it — with AI doing the reading, routing, extracting, and deciding that used to eat your team's day.
Every engagement produces a working system, not a slide deck. These are the sheets in the set.
Put AI inside the processes your team runs by hand — reading documents, triaging inboxes, answering customers, extracting data, and making the routine calls — with humans kept on the exceptions.
See how it worksScripts, workers, schedulers, and connected workflows that replace repetitive manual operations end to end.
See how it worksProduction-grade scraping and data systems that turn scattered market, product, and pricing data into usable intelligence.
See how it worksMake the platforms you already pay for finally talk to each other — ERPs, carriers, storefronts, databases, internal tools.
See how it worksOrder processing, inventory checks, catalog workflows, and fulfillment coordination — the backend of your store, running itself.
See how it worksFour operations most companies run by hand — as found, and as re-engineered. If one of these looks like your Tuesday, we should talk.
Every order touches a human. Errors ride along. Nothing happens after 6pm.
Humans touch the ~5% that need judgment. The rest just flows — nights and weekends included.
Your best people spend their day on questions a system could answer.
Customers get answers at 2am. Your team handles the 10% that deserve a person.
A document a machine can read, read by people — slowly, with typos.
The pile disappears. Your bookkeeper reviews exceptions, not envelopes.
Your visibility into the business is a weekly art project.
Monday's meeting starts from the same numbers — nobody built them by hand.
Documented builds, with the numbers left in. Full drawings available in the case studies.
A multi-session platform where AI drafts and handles support chats — refunds, replacements, price matches — with structured logging and a training-data feedback loop.
A purchasing engine, multi-carrier tracking, operator desktop, and chat automation orchestrated across multiple servers as one coordinated system.
A hardened scraping pipeline that survived 89 Cloudflare blocks to deliver analyst-ready importer data at a 4.88/5 confidence score.
You've got three ways to fix an operational bottleneck. Two of them are how it usually goes wrong.
I map how the work actually flows today — including the parts that only live in someone's head.
You get a drawing of the system that should exist: what AI decides, what software moves, what humans keep.
Automation, integrations, AI pipelines, and tooling — engineered for production, not for demos.
The system runs against real work, side by side with the old way, until the numbers say it's better.
It ships, it runs, and it keeps getting extended as your operation grows.
Long-form write-ups on production scraping, browser automation, AI extraction pipelines, and the back-office systems that hold them together.
How to deduplicate company records scraped from dozens of sources into one canonical row per real-world company — the match ladder, fill-blanks-only merging, provenance rows that never get destroyed, and the counting bugs that bite everyone who builds one of these.
Read the article →A production architecture for B2B contact enrichment — free website extraction first, credit-gated API reveals second, premium sources last, with score gating, no-double-spend guards, and upward-only verification status so every credit lands on a company worth calling.
Read the article →How to detect purchase intent from what companies do in public — job postings across 12 ATS platforms, LLM-classified news monitoring, customs import records, and domain-health checks — and why signals should rank your call queue but never touch your lead score.
Read the article →Tell me what's slow, manual, or held together with copy-paste. I'll draft the system that should exist instead — then build it.
No pitch decks, no sales calls — just a straight answer on what should be built and what it would take.
