A pipeline that resolves messy inbound leads against real company data, scores them against a transparent ICP model, and routes them — so an SDR never has to eyeball 400 form fills a week.
Inbound funnels drown sales teams in volume. Most of it is noise — students, competitors, wrong-fit companies — and buried inside is the handful of real buyers who go stale while triage takes three days.
This is the exact problem I own at Tipalti every week, running point on AP-automation deal qualification. So I built the system I wished existed: one that scores leads against a rubric a RevOps lead could read and retune — without asking anyone to triage by hand.
Deterministic where possible, agentic where valuable.
Scoring is SQL — auditable, testable, retunable without touching code. Judgment — resolving an ambiguous company name, reading intent from a vague message — is the LLM's job, and only where it earns its keep. Everywhere else, the AI-first instinct is the wrong instinct.
A lead flows left to right. Solid stages are deterministic. One step — and only one — hands off to a local LLM, and only when the answer is genuinely ambiguous.
The rubric wasn't set once and left alone. A hand-audit against real judgment calls found company size overriding strong buying signals — a CFO with budget approved shouldn't score lower just because their company is large. I rebuilt the scorer around that finding, which exposed and fixed a second gap. The rubric that shipped is the one that survived being wrong twice.
Injection detection, spam filtering, persona classification: deterministic, on purpose. The LLM only runs where the task is genuinely ambiguous — disambiguating a company name with two plausible matches, or reading intent from a message that could honestly go either way. Nowhere else.
Every phase turned up a real bug. None showed up by accident — they showed up because I went looking, and the fixes are documented alongside the code.
A text-cleaning step erased the exact detail meant to prevent an ambiguous match — the fix that broke the fix.
An SEC data-extraction gap silently failed for every foreign-incorporated public company. No error — just missing data.
A similarity feature looked plausible but was quietly comparing companies with nothing in common.
Highlighted: the fix that lets a CFO with approved budget outrank a large-company size penalty. Placeholder fragment — the shipped rubric lives in the repo.