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.
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 →no fixed price list — every engagement is scoped and quoted after a short conversation, 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
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.
You leave with a concrete plan either way — the call is free and the plan is yours.