AI agents & automation

What this is

Custom AI agent development and AI workflow automation for business: we build agents that make the repetitive judgment calls inside the workflows you already run — classify, extract, reconcile, draft, route — and the pipeline around them that lets those calls happen without a person touching every item. The system acts on its own where it can defend the decision, and hands the rest to a human with the evidence attached.

What makes this safe to run is the engineering around the model, not the model. Every pipeline we ship is confidence-gated, eval-tested on your real data, and audited end to end, because an automation that is confidently wrong is worse than none. That discipline is the product; the model is a replaceable part inside it.

The problem we keep solving

Most teams that want “AI in the workflow” already have the workflow. What they have is a queue of repetitive judgment calls — classify this ticket, extract these fields from that PDF, reconcile these two records, draft this reply — that a person grinds through all day. A chatbot doesn’t touch that work. What does is a system that makes those calls itself, knows when it isn’t sure, and hands the uncertain ones back to a human with the evidence attached.

The hard part isn’t getting a model to produce an answer. It’s getting it to be right often enough to trust, and to fail loudly when it isn’t. An automation that’s confidently wrong 5% of the time is worse than no automation, because nobody is checking. So the engineering question we actually answer is: how do you let the machine act on its own without it quietly making expensive mistakes?

How we build it

The spine of every pipeline we ship is the same three pieces: confidence gating, an evaluation backstop, and an audit trail end to end.

Confidence gating. Each decision the pipeline makes carries a calibrated confidence. Above the threshold, it acts. Below it, the item is escalated to a human queue with the model’s reasoning and the source data attached, so the reviewer decides in seconds instead of reconstructing the case from scratch. The threshold is per-decision, not global — a refund approval and a tag suggestion don’t get held to the same bar. Low human review, not no review. The full pattern — calibrated thresholds, multi-source agreement, closing gaps automatically instead of dumping every uncertainty on a queue — is written up in confidence-gated automation.

Evals as the backstop. Before any decision type goes live, we build a labeled evaluation set from your real data and measure precision and recall on it, the same way you’d test any other code path. That number is what sets the confidence threshold — we raise the bar until precision on the eval set clears the line the business needs, then automate only above it. The eval set is also the regression test: when a model or prompt changes, it reruns, and a drop blocks the change. We lean on Playwright for the end-to-end checks where a pipeline drives a real UI.

Audit trail, all the way through. Every automated action records its inputs, the model output, the confidence, the threshold it was checked against, and whether it acted or escalated. When someone asks six weeks later why the system did what it did, there’s an answer, not a shrug. This is also what makes the OWASP-style ownership checks enforceable: an automated actor still goes through the same server-side authorization as a human, and the trail proves it did.

The stack underneath is deliberately boring: Python and TypeScript for the agents, LLM APIs behind a thin adapter so a model swap is a config change, a queue between stages so a slow or failing step can’t take the pipeline down with it, and the whole thing deployed on infrastructure you already run. We wire into your existing systems rather than standing up a separate tool you’d have to babysit. And when a decision needs your own documents as evidence — a policy, a contract, a knowledge base — the retrieval layer is where most of the failures live, not the model; how we build that half is covered in RAG in production.

What you get out of it

The repetitive review queue shrinks to the cases that genuinely need a person. Throughput on those tasks stops being bounded by headcount. And because every action is logged with its confidence and its evidence, you can prove what the system decided and why — to an auditor, a customer, or yourself.

The closest thing to a reference build is our mortgage document extraction work: an OCR and LLM pipeline that auto-fills a field only when two independent sources agree at high confidence, and goes back to the borrower — automatically — for anything it can’t defend. No staff re-keying, and no guessed values reaching a credit file.

If you want a sense of whether this is worth doing now: more and more B2B buying is moving through AI agents. The operational version of that shift is the same one — work that used to need a human in the loop on every item increasingly doesn’t, as long as the system is honest about its own uncertainty. That honesty is the whole job.