This pattern is for revenue cycle teams where payer PDFs and faxes arrive in bulk and posting accuracy is non-negotiable. The goal is high straight-through processing with a tight human review surface on the fields that actually drive cash.
Ranges typical to the pattern, not audited figures for a named account.
Directional pattern: extraction accuracy on target document families often exceeds ~90% field-level precision on evaluated batches, with tighter controls on totals and payer IDs.
Directional pattern: manual touches per thousand pages typically fall sharply once clusters stabilize.
Directional pattern: supervisors spend less time finding “what went wrong” because lineage is explicit.
Illustrative engagements based on the patterns we deliver. Anonymized and composited. Real client references available under NDA.
The shape of the problem and how we ran it. Written for technical evaluators and business owners in one pass.
Operators posted payments and denials from heterogeneous payer formats. Rules engines covered common templates, but long-tail variants leaked to humans. Leadership needed higher automation without increasing denial write-offs from silent extraction errors.
We clustered document families, built per-family extraction recipes, and separated money fields from narrative fields for validation. Confidence thresholds routed only the risky tail to HITL. Posting exports were generated with checksums and immutable links to source pages.
Same disclaimer applies. Anonymised composites with directional outcomes.
Unstructured Data Pipelines
This pattern is for carriers where adjusters and third parties send facts as email threads and attachments, not as clean ACORD feeds. The goal is reliable structured records for routing, reserving, and downstream fraud checks, without asking adjusters to retype what they already wrote.
Read the patternAI Workflow Automation
This pattern fits teams where capacity checks and booking confirmations require logging into multiple carrier systems that were never meant to integrate cleanly. The goal is fewer clicks for operators, fewer missed slots, and a replayable record when a carrier UI changes.
Read the patternCustom AI Agents
This pattern fits SaaS teams where tier-1 tickets repeat: invoices, seat counts, plan mismatches, and refund policy questions. The agent reads account state with least privilege, proposes actions within policy, and escalates when confidence drops or a human must approve money movement.
Read the patternHonest, specific answers about scope, accuracy, security, and what production looks like. If something isn't covered here,ask us directly.
We design for healthcare privacy requirements. For SOC 2 or HIPAA production work we hand off to a compliance-ready partner with a clean architecture and documentation pack.
Humans own low-confidence and high-impact fields, rare templates, and policy judgment calls models should not take.
Cross-field arithmetic checks, payer-specific sanity bounds, and forced review queues for outlier totals.
A Rapid POC typically targets one payer family or one facility group with agreed metrics before scaling clusters.
A short note is enough. We will reply within one business day with a Rapid POC scoping call.
