AI in the one step that limits your team's output.

The usual pattern: a chat tool goes out to everyone, a few people use it well, and the queue looks the same a quarter later. Nobody decided which step the AI was for, so nobody can say whether it worked.

Output in most operations is set by one step. The intake inbox someone answers by hand. The review that waits on one senior person. The month-end report that takes two days to assemble. The follow-up that slips when the week gets busy. Speeding up anything else leaves the queue where it was.

So the work starts with that step, not with a model.

Six steps, in this order. Each one ends in something you can read or test before the next one starts.

1. 01
   Find the bottleneck. We trace one workflow from what arrives to what leaves, and find the single step that limits output. If that step does not work by hand yet, we fix the process first. AI makes a broken process fail faster.
2. 02
   Break the goal into base actions. "Faster claims intake" becomes what a person actually does: read the document, classify it, pull the fields, check them against the policy, route it. Each action can be tested on its own, and some never need a model at all.
3. 03
   Build into that step only. The AI takes the actions it does reliably, inside the systems you already run: your SQL Server, your SAP, your inbox. A person keeps the decisions that need one. The rest of the process does not change.
4. 04
   Deploy in your accounts. Model access, data, logs, and code live in your cloud account and your repository, under your agreements with the model provider. Nothing runs on ours, and nothing is copied out.
5. 05
   Evaluate with repeated tests and human review. A fixed set of real cases with known right answers runs against every change and every model update. A person on your team reviews a sample of live output on a schedule. The step is compared against the baseline taken before we built.
6. 06
   Turn what your best people do into shared process. The checks your most experienced reviewer runs from memory get written down, built into the step, and tested, so the whole team works to the same standard. We encode a process the business has already proven. We do not invent one.

Then it is handed off like any system we build: documentation, tests, runbooks, and the evaluation set, so it runs without us.

In claims, compliance, and finance, the inputs to a model are often the most sensitive data the business holds: member records, health information, payment details, audit files. Sending them to a model is a data-handling decision before it is a technical one.

• Onshore, under US jurisdiction. All work is done in Puerto Rico. No subcontractors, no offshore processing.
• HIPAA-aware from the first design. We map which fields reach a model, keep health information out where the step does not need it, and use model services covered by your agreements, including a BAA where one is required.
• Your accounts, your logs. Prompts, outputs, and access logs stay in your environment, where your auditors can read them.
• Built next to the systems of record. We have spent ten years on SAP, SQL Server, and claims systems in production. The AI is wired into the ledger and the reports, not into a separate chat window.
• One accountable engineer. The person who designed the data flow is the person who answers the auditor's question about it.

We also run AI in production on our own account: the coach in Ignia reads each user's history before it answers, and we maintain it through model updates ourselves.

AI work uses the same engagement models as everything else we do. Most of it starts with the audit.

Pricing, length, and what a statement of work contains are on how we work.