A BPO servicing insurance claims had no systematic way to tell which claims needed a human decision.
The claims team now spends its time on genuine exceptions instead of routine evidence reading.
- The problem
- A BPO servicing insurance claims had no systematic way to tell which claims needed a human decision.
- What we built
- An AI layer reading claims evidence and routing by confidence score.
- The outcome
- The claims team now spends its time on genuine exceptions instead of routine evidence reading.
Case studies are anonymised at the client's request. Outcomes describe what changed operationally rather than claiming attributed financial figures — we'd rather under-state a result than dress one up.
The product behind this
Turn high-volume manual review into an automated, confidence-scored queue.
Automated document interpretation, evidence reading, classification and case routing for high-volume manual operations queues.
More Workflow Automation case studies
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A large share of manual workload was reclassified as automation-ready or assisted-review.
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A payments processor's reconciliation queue grew manually with transaction volume.
Reconciliation queues are now triaged automatically, with only genuine exceptions reaching an analyst.
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Have the same problem?
Most engagements start as a single product on a single workflow, with a measurable result inside 8–12 weeks.