Every company is being told to deploy agents. Most attempts die the same way: a prototype that impressed everyone, built on no real data, with no way to know if it is right, and no path from the demo to the real queue of work. The gap between an agent that demos and an agent that works is not the model. It is engineering, measurement and operation.
That gap is our product.
Different jobs, different shapes. What they share is the build discipline and the bar: production, measured, operated.
Review real cases and produce a verdict a human acts on. Fraud, claims, compliance, content, integrity.
Evaluate people or work against rubrics, consistently, at scale. Screening, interviews, grading, quality audits.
Hold long, structured conversations that resolve real business interactions, over an hour, on mission.
Turn document and data streams into reliable, structured, actionable output. Contracts, invoices, claims, records.
Turn raw operational signal into decision-ready analysis and per-audience reports from one source of truth.
Watch a live operation and surface what deviates, without crying wolf. Under explicit false-positive budgets.
The interviewer is an assessment agent and a conversation agent. The review agent feeds the analysis agent. Monitoring watches what the others produce. All of them get sharper standing on the Brain, and get built with the AI Software Delivery method.
When the agent's output has real consequences, it ships with a measurement contract: accuracy measured on your real cases, kept true in production.
We start in your operation, not a tool catalog: where volume meets structure, what data exists, what "correct" means, and what an error costs each way. Output: the use case, the success metric, an honest feasibility read.
The agent gets a written specification before it gets code: the job, the inputs, the policy it follows, the acceptance criteria, the edge cases, and what escalates to a human. Spec-driven, same as all our engineering.
Built with full code generation under senior direction, on the layered pattern that survives production: deterministic processing first, model reasoning second, validation and policy on top. The model is treated as an unreliable component.
Proportional to consequence. Low-stakes: golden cases and regression tests. Real consequence: the full measurement contract, ground truth from your cases, measured accuracy at an explicit operating point, sampled in production.
On your stack, integrated via API or MCP. What was validated is what ships: releases go out behind flags, gated by regression. Parity between what was measured and what runs.
Agents are run, not launched. Monitoring, drift detection, cost per case tracked next to quality, an operating routine with owners, iteration gated by regression. We operate it with you, or hand it off with the runbook.
No agents on data that does not exist.
No "autonomous" claims where a human should own the decision.
No accuracy promises before seeing your cases.
No pilots designed to demo well and die quietly.
Anyone can demo an agent. Almost nobody can tell you its false-positive rate. For agents whose output triggers real decisions, we apply a measurement discipline where the number is the product: ground truth from your real cases, an explicit precision/recall operating point set against your error costs, and accuracy measured before and after every change and kept true in production.
Ground truth from your cases. Hundreds of real cases, labeled by people you trust, with holdout discipline so the release score is computed on cases the agent was not tuned on.
The operating point is a business decision. Where the agent sits on the precision/recall curve is set with you, against your real error costs. Confidence is calibrated: a reported 90 percent is right about 9 times in 10.
The number stays true in production. Sampled measurement over live traffic, drift monitoring, stop gates, and cost per case tracked next to the quality metric.
More than 12 months operating agents in production across client engagements, under the same discipline we sell.
operating agents in production across client engagements.
on review agents, with double-digit F1 gains, at fractions of a cent per case.
of historical cases processed on demand, monitoring under explicit budgets.
Real interviews conducted by voice at production volume for a high-volume hiring operation. Every interview lands as an evidence-cited, auditable scorecard, and the engagement produced the interviewing methodology itself, encoded and protected by tests.
A multimodal agent reviewing examination sessions and producing evidenced verdicts. Detection quality improved by double digits (F1) with recall above 95 percent, on validated ground truth, at fractions of a cent per case.
Every agent we deploy runs on its own mini brain, the live context it needs to act well. Complete on its own, and already the first module of your Company Brain. Deploy a second, and they connect.
Learn about the Brain →Powered by the Neocortex. The intelligence layer that ships with every agent we build. It arrives knowing the job and keeps getting smarter with every deployment.
Meet the Neocortex →Bring the queue, the reviews, the conversations, or the reports that consume your team. In one working session we map where an agent pays for itself, what it takes to build, and how you will know it works. You leave with that picture whether or not we work together.