We design, build and operate AI agents end to end, for IT departments automating processes and SaaS teams putting AI inside what they sell.
A prototype runs on no real data, no one knows if it is right, and it never reaches the real work.
Voice and text agents for long, structured conversations: support, intake, sales, interviews. Each ends in a record someone can decide on, backed by exact quotes.
Conversational agents →Agents that evaluate data and evidence so people decide: document processing, research from your sources, case reviews, finance review, monitoring of a live operation.
Decision agents →Agents that run marketing and sales across your ad accounts, CRM and sales team: daily reports, lead attribution, approved paid media changes and chatbot audits.
Marketing and sales agents →The product owner's agent reads how the product works from its deployed code, and tracks what is broken, what was decided and who owns it. The product owner still sets priorities.
Product agents →Agents that carry workflows across your ERP, CRM, ticketing and internal systems: IT operations, help desk, finance, back-office. Exceptions are escalated to a person.
Bring us a process →A conversational agent's record feeds a decision agent, and a process agent acts on the verdict. All are built with the AI Software Factory's method.
Six steps, from finding the use case in your operation to running the agent every day.
We find the use case in your operation, the success metric and whether it is feasible.
A written specification before any code: the job, the acceptance criteria, and what escalates to a person.
Built with AI-generated code, directed by senior engineers of ours.
Testing matches the stakes: known-answer cases and regression tests for low stakes, an accuracy target agreed with you for real consequences.
On your stack, via API or MCP. What goes live is exactly what was validated, after regression tests pass.
Monitored daily, with alerts when quality drifts, cost per case next to quality and named owners. We run it with you, or hand your team the runbook.
For agents whose output triggers real decisions, the target is agreed with you and measured before and after every change.
Ground truth from your cases. The correct answer for hundreds of real cases, labeled by people you trust.
Tested on unseen cases. The release score uses cases the agent never saw while it was being tuned.
You choose the balance of errors. False alarms against missed cases, set against what each error costs you.
Confidence is calibrated. When the agent reports 90%, it is right about 9 times in 10.
Checked again in production. Samples of live cases are measured, and stop rules halt the agent if quality drops.
Agents that evaluate people. We measure that the agent treats every group consistently, and the person is told the process is AI-conducted.
Untrusted input is data, never instructions. Injection and manipulation are tested as named failure modes, the same way as the honest cases.
12+ months operating agents in production across client engagements, under the same method we offer you.
Fractions of a cent per case for review agents measured on cases they never saw while being tuned.
Entire historical backlogs processed in single unattended runs, and monitoring held to an agreed limit on false alarms.
Structured evaluation interviews by voice at production volume, well over an hour each. Each ends in an auditable scorecard that cites its evidence.
An agent reviews every case on a high-volume platform: verdict, evidence and a confidence figure. Built in months, supervised daily by senior engineers of ours.
A customer's operation run by agents, with a person approving every change. Hundreds of approved changes reached live accounts. Daily reports refuse to publish when numbers do not match.
A SaaS company's documentation no longer matched its code. The agent rebuilt how the product works from the deployed code, in thousands of cited facts. The manual now regenerates each release.
Each agent's record is useful on its own and adds to the same Company Brain, one connected record of how your company works.
Week 1 runs with a forward deployed engineer, a senior engineer of ours who works inside your team.
A 45-minute working session, then a week mapping the process on your real cases. You leave with the agent's specification and the agreed balance between false alarms and missed cases.
The pilot: one agent on one real queue, measured on your cases. It is invoiced when the agent reaches the accuracy target agreed with you.
The agent runs daily with named owners, measured monthly against where the process stood in week 1. Then you choose the next queue or agent.
Bring the queue, the reviews, the conversations or the reports that consume your team. In one 45-minute working session, we map where an agent pays for itself, what it takes to build, and what correct means for your cases.