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.
Ranges typical to the pattern, not audited figures for a named account.
Directional pattern: manual intake time often drops on the order of 60 to 75 percent once confidence thresholds stabilise for the highest-volume message classes.
Directional pattern: extraction precision on money and date fields commonly reaches the high nineties on evaluated samples, with human review concentrated on rare document types.
Directional pattern: average queue time for exception review falls because operators stop rekeying and start adjudicating.
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.
Operations saw more than forty thousand adjuster-related messages per month across regional teams. High-value facts lived in forwarded threads, PDFs, and photos. Manual intake teams were fast but inconsistent, and straight-through automation was blocked by variant layouts and ambiguous policy references.
We built a staged pipeline: normalize email and attachments, classify intent, extract entities with layout-aware models, validate with deterministic rules, then route low-confidence bundles to a review queue sized to SLAs. Humans adjudicate exceptions; the system learns which exception classes are worth new rules versus better prompts.
Customer-controlled object storage for payloads, queue workers for ingestion, vision-language models for PDFs and images, and a warehouse write path with immutable raw artifacts. Model calls are logged with hashes of inputs for reproducibility.
Same disclaimer applies. Anonymised composites with directional outcomes.
AI 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 patternIntelligent Document Processing
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.
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.
It is an illustrative pattern composited from engagements in this shape. We do not name clients without permission; references are available under NDA when applicable.
In this pattern, a Rapid POC on a bounded slice of traffic typically lands in 14 days once access is in place. Long enough to measure precision, not long enough to boil the ocean.
Hardening: drift monitors, expanded variant coverage, formal change management for model updates, and deeper integrations into claims cores.
Yes. The best POCs choose one message class with painful volume and clear ground truth.
A short note is enough. We will reply within one business day with a Rapid POC scoping call.
