Extraction pipeline
LLM-driven extraction from unstructured claims documents into usable structured data.
Case Study · Claimo
How an AI claims workflow processed $50M+ and transformed Claimo's operating economics.
The Challenge
Claimo handles high-volume claim submissions for consumers seeking banking refunds. Each claim required manual extraction, interpretation, and validation against policy requirements.
Review cycles were slow, expensive, and difficult to scale. A single key processing step took roughly 10 minutes per claim. At volume, this created operational drag that limited throughput and margin.
The Solution
LLM-driven pipeline converts unstructured claim artifacts into validated, structured outputs.
Outputs checked against compliance and policy requirements automatically.
Edge cases routed to human review, keeping quality high without full manual handling.
What Avicenna Shipped
LLM-driven extraction from unstructured claims documents into usable structured data.
Automated checks against policy and compliance requirements before downstream processing.
Prompt tuning and regression testing to maintain reliability as volume scaled.
Review tools for exception handling and edge-case oversight without breaking flow.
"The AI solution that Avicenna implemented transformed our claims processing capabilities. Since launching internally, our output has multiplied and we have processed over $50M back to Aussies."
Nathan Mortlock, CEO & Founder, Claimo
Your Workflow
We can map your workflow, identify where AI fits, and ship a production system—not another pilot.