Turn on the vendor’s AI feature
Choose it when
Your application vendor offers the exact task, and its data terms are acceptable.
What it costs
Their model, their controls, their data handling. Fine when it fits; opaque when it does not.
Add AI to the software you already run.
Your team stops typing what documents say and starts checking it. We build the reading, sorting, or drafting step into the application people already use, with data boundaries in writing and accuracy measured before rollout.
You know this is you when
- A team reads documents and types what they say into a system.
- Requests arrive as free text and someone sorts them.
- Staff draft the same kinds of responses every day.
- The board has asked what the company is doing with AI, and the honest answer is a pilot nobody uses.
- A vendor has offered an AI feature and nobody can say whether it would hold up.
Why it is happening now
The useful place for AI in most businesses is inside an existing workflow, doing a repeated reading, sorting, or drafting task, with a person reviewing what matters. Added there, it removes typing and speeds decisions. Added as a separate tool, it becomes one more place to look. What makes the difference is the engineering around the model: data boundaries, review, exceptions, evaluation, and monitoring.
- Documents, requests
- Model stepextract · classify · draft
- confidentSystem of recordsampled for accuracyuncertainReview queueperson decides
What we would do first
The first steps, in order.
- 01
Pick one task with a measurable outcome
Repeated, with representative data and a clear definition of correct.
- 02
Agree on the data boundaries
What the model may see, where it runs, and what never leaves your systems, in writing.
- 03
Prove it on your data
Accuracy measured on real examples before rollout, with the result written down.
- 04
Build it in, with review
We put the AI step inside the workflow, send uncertain cases to a person, give exceptions an owner, and monitor after launch.
What it looks like after
The task runs inside the application people already use. Confident cases are handled and sampled; uncertain ones reach a person with the original alongside. Accuracy, volume, and cost are visible. The team does the reviewing and the judgment, not the typing.
Title company
Processors check extracted contract details instead of typing them into several places.
At a title company, purchase contracts, payoff letters, and lender instructions arrive as PDFs, and processors key the same details into the production system, the closing checklist, and email templates. A reading step added inside the production system extracts the fields, matches them to the file, and sends anything uncertain to a processor with the original document alongside. Accuracy is measured on past files before anyone relies on it, and the data boundaries are agreed in writing. A later step might draft routine closing emails from the checked fields.
The path from here
What we would build, and for whom.
The service
AI development and integrationDocument reading, routing, and drafting with review and controls.
ReadWhat we build
Document workflows with AI reviewInvoices, forms, and contracts read by a model and posted to the system of record, with a person checking only the uncertain cases.
ReadBy industry
Financial services and insurance agenciesEvery renewal queued by date with an owner, client documents collected through a portal, and carrier commission statements matched to policies.
ReadA guide
Should you buy software, connect your tools, or build something custom?Written for the decision, not the sale.
ReadQuestions
Before you decide.
What companies in this situation ask us first.
Which tasks are worth it?
Repeated tasks with representative data and a clear definition of correct: reading documents, sorting requests, drafting responses, answering from approved records.
Where does our data go?
We agree on it in writing before anything is built: what a model may see, where it is processed, and what stays inside your systems.
How do we know it is accurate?
It is measured on your documents before rollout and monitored after. The review threshold is yours to set.
Can it go inside our existing application?
That is how we prefer to build it: an AI step inside the tool people already use, not a separate one to switch to.
Describe the task.
Tell us the situation and the systems around it.We’ll come back with what we would do first, and what it would take.