Practical AI in the places it actually pays back
Document extraction, classification, drafting, and support triage built into your existing workflows - with evaluation and human review, not a demo that fails in production.
AI pilots stall because they are evaluated on impressive demos instead of on your messiest real inputs. The result is a tool nobody in operations trusts enough to rely on.
We pick narrow, high-volume tasks with a clear right answer, benchmark against your historical data, and ship with a human review queue that shrinks as measured accuracy earns it.
We rank candidate tasks by volume, tolerance for error, and how cleanly success can be measured. Some get rejected here - that is the point.
Before any build, we assemble a labelled set from your own history so accuracy claims are grounded in your data.
The workflow ships with review queues, confidence thresholds, cost controls, and full audit logging of every model call.
Accuracy is tracked over time, so model or prompt changes are a measured decision rather than a hopeful one.
Repetitive reading and drafting work handled automatically
Measured accuracy before anything reaches a customer
Human review kept exactly where judgement matters
Use-case assessment with expected accuracy and cost
Prototype evaluated against your real historical data
Production build with review queues and audit logging
Ongoing evaluation harness and monitoring
Engagement: Assessment first, then scoped build
Timeline: Assessment in 1 week, pilot in 4-6
Discuss This ServiceRelated Work
Financial services
A lending team read every application pack by hand. We built an extraction and classification workflow with a human review queue, benchmarked on two years of their own files.
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