PREECURSOR
The work

The kind of work we do

Illustrative examples of the systems we build and the outcomes they're designed to produce — the cycle time, cost, time-to-repair, and hours-returned levers AI can actually move. Examples, not specific client results.

Example engagements

The scenarios below are illustrative examples of the kind of work we do — not specific delivered client results.

Financial institutionsAn underwriting copilot that drafts and checks credit memosAn illustration of the kind of system we build: an agent that drafts and checks credit memos against policy in real time, with every figure cited back to source.
Industrial goodsPredictive operations that turn downtime into scheduled workAn illustration of sensor-grounded models and a maintenance copilot that turn unplanned downtime into scheduled work across a plant network.
Healthcare & life sciencesClinical documentation that gives clinicians back their eveningsAn illustration of ambient, grounded note generation designed to return time to clinicians without compromising coding accuracy or the chart.
TelecommunicationsA network-ops copilot that quiets the NOC and finds the real faultAn illustration of anomaly detection and a root-cause agent designed to cut through alarm storms and get crews to the real fault faster.
Outcomes

We define the outcome before we build the system.

Every engagement opens with a single agreed metric and a candid view of what moving it is worth. That number governs scope, sequencing, and what we count as done — so the work pays for itself in a line of the P&L you already watch, not in a slide we made up. The examples above show the kind of system that follows.

Methodology

How an engagement runs

01
Diagnose
We start from the metric you already track — cycle time, downtime, loss ratio, churn — and work backward to the system that moves it, scored on value and feasibility before a line of code.
02
Build
Our engineers ship production-grade retrieval, agents, and eval harnesses in weeks, not quarters — instrumented from the first sprint so you can see exactly what they do and what they are worth.
03
Scale
We harden the system for everyday operations: latency, cost, monitoring, and rollback, proven against the number you agreed to move and built to hold as usage climbs.
04
Enable
We redesign the workflow around the system and hand it over documented and owned, so the gains stick after we leave instead of decaying back to the old way.
What to expect
The deliverable is a running system in production, not a strategy document and a separate vendor to build it. We own the outcome end to end.
We ship, not slide-deck
Every engagement opens with a single agreed metric and a candid view of what moving it is worth — so the work is judged on the number, not the polish of the readout.
The metric is the deliverable
We ship the eval harnesses and monitoring that let you change models and prompts without holding your breath — quality is measured, not asserted.
Eval discipline, built in
Sectors served

We bring sector judgment to every build — pairing people who understand your industry with engineers who put production AI in front of your customers and teams.

Financial institutionsHealthcare & life sciencesIndustrial goodsTelecommunicationsInsuranceEnergy & utilitiesPublic sectorPrivate equity

What number do you need to move?

Bring us the metric. We will tell you, candidly, whether AI is the right lever — and if it is, what we would build first, how long it takes, and what it is worth.

Read our thinking