We build automation that knows what it doesn't know — validated at every boundary, gated where it matters, and instrumented so you can see what it actually did.
An automation that crashes gets fixed the same day. One that keeps running while producing subtly wrong output — a misclassified invoice, a duplicated booking, an enrichment that overwrites good data with bad — can run for weeks before anyone notices, and by then the cleanup costs more than the automation saved.
So we build the boring parts first: schema validation at every boundary, idempotency on anything that writes, dead-letter queues for what fails, and confidence thresholds that route uncertain cases to a person instead of guessing. The model is one component in a system, not the system.
Invoices, contracts, ID documents and forms parsed into structured fields your systems can actually consume — with a confidence threshold that routes the uncertain ones to a human instead of guessing.
Classify, route, draft and escalate. The model proposes, your rules dispose — so a misread never silently sends the wrong reply to a customer.
Lead and record enrichment from multiple sources, deduplicated and reconciled. Provider-based, so a source can be swapped without rewriting the pipeline.
Recurring reporting pulled from your live data, not pasted into a prompt. Scheduled, versioned and diffable against last period.
The unglamorous half: CRM, ERP, calendar, billing and messaging wired together with retries, idempotency keys and dead-letter queues.
An approval surface for anything consequential. Bulk-reviewable, keyboard-driven, and fast enough that the gate doesn't become the bottleneck.
For a linear five-step workflow you should — it will be cheaper and faster than hiring us. The point where those tools stop paying off is branching logic, real error handling, state that has to survive a restart, and anything where a silent failure costs money. We are usually called in after a no-code automation has been quietly wrong for a month.
Consequential actions go behind an explicit gate — either a deterministic rule or a human approval step. Model output is treated as a proposal, validated with Zod at the boundary, and anything failing validation is queued rather than executed. Every action is logged with its inputs so a bad outcome can be traced rather than guessed at.
We instrument token spend per workflow run and report it alongside throughput, because the failure mode nobody warns you about is an automation that works beautifully and costs more than the salary it replaced. Where volume justifies it we route cheaper models to the classification steps and reserve the expensive ones for generation.
You own the code and the provider accounts, and it runs on your infrastructure. We build on standard TypeScript, Postgres and a queue — no proprietary runtime you would need us to maintain.
One workflow in production in two to four weeks, deliberately narrow. Automating a single high-volume process well teaches us more about your operation than a six-month discovery phase, and it pays for itself while we scope the next one.
Tell us the process that eats the most hours. We'll scope it, build it, and put it in production before committing you to anything larger.
