Built for your business.
Evolving with evidence.
More than an AI workflow. We connect your data, tools and quality standards so the system can find failures, propose changes and test what belongs in the next version.
Your business sets the standard.
Evidence decides what changes.
The first extraction loses a critical qualifier. The output is not yet ready for delivery.
A model gives you capability.
A harness makes it fit.
Different data. Different tools. Different definitions of “right”. We design these into a working system, rather than asking your business to adapt to a generic agent.
What goes into a harnessWork worth getting right.
Start with a repeatable, data-intensive task whose outcome a person can evaluate.
Documents into usable data
Extract records, preserve their sources and surface missing or contradictory information.
Knowledge into a working process
Bring authorized reference material and tools into a workflow with explicit review points.
Corrections into better versions
Capture recurring failures, test proposed changes and retain a way back.
A correction is not yet
an improvement.
Synthetic procurement example. These are illustrative steps, not a live run or a measured customer result.
15 business days
The document specifies a unit.
15 days
The extraction loses that qualifier.
Preserve the unit
A proposed rule addresses this error.
Test before release
Check independent samples and regressions.
One task.
A clear way to judge it.
Define the job and acceptance criteria first. Use the pilot to decide what deserves to scale.
A task-specific workflow
Inputs, tools, permissions and human review points.
An evaluation baseline
Representative samples, failure categories and checks.
A version improvement plan
Candidate tests, release decisions and rollback boundaries.
Proposed deliverables. Final scope depends on the assessed scenario and agreement.
Bring us a real task.
First, agree on what a good result looks like. Then work out how to get there.