Decisions
Agents that decide, and show their work
Your business makes thousands of small judgment calls a day: which bucket a document belongs in, whether two records are the same order, what a first reply should say. We put agents inside your system of record to make those calls, follow the rules you would follow, and log the reasoning behind each one so a person can audit it after the fact.
The problem
What this costs you today
- 01
A person opens each incoming invoice, reads the line items, decides which account and job it belongs to, and re-types the result into another screen, hundreds of times a week.
- 02
Two systems disagree about the same order, and someone spends an afternoon reconciling them line by line before anyone will trust the number.
- 03
The rules for classifying, routing, and approving a case live in one experienced employee's head, so when they are out sick the work either stops or gets it wrong.
- 04
You are sitting on piles of PDFs, scanned forms, and free-text emails that carry the answer you need, and nothing you run can read them, so a human is the only interface to your own data.
- 05
Every case that waits for a person to look at it, categorize it, and pass it along adds a delay your customer feels, and the backlog only grows as volume does.
What we build
AI and intelligent automation, built around you
Classifying and routing what comes in
An agent reads the incoming thing, the email, the form, the invoice, the ticket, and decides where it goes and who owns it next. It works from the same categories and exceptions your team uses, so a case that is slightly unusual is flagged rather than forced into the wrong bucket.
Reconciling records that should match but do not
The agent compares the same order, payment, or shipment across the systems that hold it and works out where they diverge. It resolves the differences it is confident about and surfaces the rest with the discrepancy already spelled out, so a person confirms instead of hunting.
Drafting first passes a person signs off
Replies, summaries, status notes, and internal reports come back as a draft written against your real records, not a blank box someone still has to fill. Your team edits and approves rather than composing from scratch, and nothing goes out until a person releases it where that matters.
Deciding the routine cases, escalating the rest
For approvals, scoring, and triage that follow a pattern, the agent makes the call against your written rules and a confidence threshold you set. The clear cases move immediately, the borderline ones land in front of a human with the reasoning attached, and the line between the two is yours to move.
Working through the backlog you never had time for
The same agents can be pointed at the archive of documents and records that has been piling up because reading it by hand was never worth anyone's week. What was dead weight becomes structured data your other systems can finally use.
The intelligence layer
Agents that know how your business runs
A chat window bolted to the side of your software knows nothing about your operation. We build the platform and the model context underneath it together, so the agents work inside your data rather than guessing at it from the outside.
The agent works inside your data, not from a text box
We build a custom MCP server that exposes your real systems, the CRM, the ERP, the document store, to the agent as first-class tools. It reads and writes the actual system of record and follows the operator's rules, rather than guessing at an answer from whatever someone happened to paste into a chat window.
Every decision carries its reasoning
Each call the agent makes is logged with the inputs it saw, the rule it applied, and a confidence score, so a decision made at 2am can be reconstructed and defended weeks later. This is what makes it safe to hand judgment to software: you are not trusting a black box, you are reading its work.
The first operator to run this way tends to keep the market
An operation your size can now run its decisions on a system built entirely around the way it works, which was not possible before agents could reach into the real data. The point is not that AI is new. It is that whoever in your market does this first moves at the speed of software while everyone else is still reading documents by hand.
How it goes
From the first call to the platform
- 01
We map the decision
We sit with the people who make the call today and separate the routine cases from the ones that need judgment, and we mark exactly where a human has to stay in the loop.
- 02
We prove it on your real data
We test the agent against your actual documents and records first, so you see measured accuracy on your own cases before any full build is committed to.
- 03
We give the agent your systems as tools
We build the MCP server that connects it to your systems of record, wire in the review steps and the audit log, and set the confidence thresholds for what runs on its own.
- 04
We raise the threshold as it earns trust
We run the agent alongside your team in a controlled rollout, tune its output against the corrections people make, then widen what it handles unattended and move to the next decision.
After launch
It does not end at go-live
The routine decisions get made the moment work arrives, and only the genuinely ambiguous cases reach a person, with the agent's reasoning already attached so the review takes seconds. We watch the accuracy, tighten the rules against the corrections your team makes, and widen what the agent is trusted to handle on its own as the record of good calls builds up. Because we own and run the platform, teaching it the next decision to make is simply the ongoing work, not a new project you have to scope from scratch. The system gets better at your business the longer we run it.
Questions
The questions worth asking early
- How is this different from the workflow automation you also build?
- Workflow automation moves work between steps: it routes the approval, fires the notification, carries the data across. This is about the judgment at a step, the call a person currently has to make about what something is, where it belongs, or whether it is right. The two fit together, but this one replaces reading and deciding, not plumbing.
- We tried a generic AI tool and it just gave us a demo. Why is this different?
- A generic tool lives in its own tab and guesses from whatever text you feed it, with no access to the records the decision actually depends on. We build the agent a custom MCP server so it reads and writes your real system of record and follows your rules directly. It decides inside your data instead of talking about it from the outside.
- How do we know we can trust a decision the agent made?
- Every decision comes with a confidence score, the inputs it read, and the rule it applied, all written to an audit log you can review. You set the confidence threshold, so the clear cases run unattended and anything below the line waits for a person. Nothing important happens silently, and any call can be reconstructed after the fact.
- Will this replace our people?
- It takes the repetitive judgment calls off their plate, the reading, sorting, and reconciling that fills their day, so they spend their time on the ambiguous cases and the exceptions that genuinely need a person. Humans stay in control of approvals and anything the agent is not confident about. The work that needs a person gets more of one, not less.
- What happens as our data and rules change over time?
- Inputs shift, edge cases appear, and the rules you want enforced evolve, so an agent left untended drifts. Because we own and run the platform, monitoring the accuracy, updating the rules, and extending the agent to new decisions is the standing work of the partnership. You are not filing a change order to keep it correct.
The first conversation costs an hour.
We take on a small number of partnerships, because we carry the engineering risk on every one. The first call is where we both find out whether this is one of them.