Intake, triage, routing, RAG-backed answers — built on n8n, Make, or code-level LangGraph. AI on the reading side, humans on the writing side, until evals prove otherwise. Every run logged, every step auditable.
▸ the 45-second version — in my own voice
One scoped workflow end-to-end — intake → classify → route → act — on n8n/Make or LangGraph, in your accounts.
Placed where the risk is: before every send, before novel cases, or after-the-fact review — earned down with evidence, never assumed.
Schema-validated LLM steps, fallback labels on parse failure, raw inputs always logged beside decisions.
Your team re-runs, extends, and debugs it without me.
fixed scope · quoted after a week-1 risk map · your repo, your CI
I map your intake → decision → action flows, find the single highest-leverage automation, and hand you a plan with the risk gates drawn in — the fastest way to a concrete quote.
Start here — get a quote →One scoped workflow shipped end-to-end in your accounts — intake, classify, route, act — with human approval points and full run logs.
Get a quote →The full pipeline: multiple workflows, eval-gated AI steps, cost model, structured outputs, runbook and handoff.
Get a quote →fixed-price starting points from $497, plus a custom quote for larger or unusual builds — scoped in writing before we start, so you pay for your problem, not a package · every engagement ends with evidence you keep — and if the scoping shows I can’t help, I’ll say so and it costs nothing
Take inbound-request triage — an ops person reading every message, tagging it, and routing it by hand between an inbox, a spreadsheet, and the right queue. The classic time sink.
Manual copy-paste, inconsistent tagging, no audit trail, and the person is the bottleneck.
The automation reads, classifies, routes, and posts a summary to the human — sending zero AI-written customer messages, so the riskiest failure class is designed out. Monitoring + an eval catch drift.
Representative of the pattern — the exact steps, tools, and where a human stays in the loop are scoped with you.
Whichever your team can own after I leave. Rule of thumb: Make/Zapier for linear flows, n8n when you need branching and self-hosting, LangGraph when the logic is genuinely agentic.
Not until an eval suite proves it should. The most reliable automation I've shipped sends zero AI-written messages — it reads, classifies, routes, and alerts a human. That design deletes entire failure classes.
Part of the build is a cost model — per-run LLM spend tracked and budgeted, with cheaper models routed in where quality allows.
Scoped and quoted in writing after a short call — fixed scope, not open-ended hours. Most engagements start with a one-week audit that credits into the build if you continue.
Monitoring and alerts around the automation, plus an eval on the AI step — so if quality drifts or an integration changes, you find out before your customer does, not after.
The automation runs in your infrastructure and accounts, with docs and monitoring your team operates. Nothing is retained on my side after handoff.
You leave with a concrete plan either way — the call is free and the plan is yours.