Where automation pays
We look for high-volume, rule-shaped, low-variance steps first — invoice and claim intake, contract clause extraction, ticket triage, reconciliation. Those are measurable, and measurable is what gets funded a second time.
- Document extraction with structured output schemas and field-level confidence
- Classification and routing over email, tickets and forms
- Reconciliation between systems that disagree
- Human-in-the-loop review queues for anything below the confidence threshold
Designing for the failure case
An automation that is right 92% of the time and silent about the other 8% is worse than no automation. Every workflow we ship declares a confidence threshold, routes uncertain cases to a person, and records the correction as training signal.
- Per-field confidence scoring, not per-document
- Explicit escalation paths and SLA timers on review queues
- Corrections captured and fed back into evaluation sets
- Straight-through-processing rate reported as the headline metric
Measurement
We baseline the manual process — time per item, error rate, backlog — before automating, so the after-number means something.
What you get
- Baseline measurement of the current manual process
- Automation pipeline with confidence thresholds and review queue
- Operator dashboard showing straight-through-processing rate
- Feedback loop that turns corrections into evaluation data
How the engagement runs
Two-week baseline and pilot on a single workflow, then expansion once the straight-through rate is proven.