Some industries don't need a generic AI agent. They need one built for their documents, their systems, and their compliance posture.

Capabilities

1. 01

   Industry document classification

   Classifies each incoming document by type before extraction begins — ACORD form number, loss run format, financial document, schedule — so the right extraction model fires on the right document.

2. 02

   Dual-model cross-validation

   Primary extraction model runs first; a second model cross-validates every field below the confidence threshold before any data commits to the downstream system.

3. 03

   ACORD form extraction (insurance)

   Full field extraction from ACORD 125, 126, 127, 130, 131, and 137 forms mapped to the client's Salesforce or CRM object schema.

4. 04

   Loss run parsing

   Processes loss run documents across 40+ carrier formats in PDF and Excel, extracting structured loss history per line of business.

5. 05

   Carrier and system API forwarding

   Extracted and validated records flow to downstream carrier APIs, CRM systems (Salesforce), or underwriting platforms — replacing manual re-entry.

6. 06

   Human-in-the-loop exception handling

   Low-confidence items surface in a review interface for human confirmation before the record is finalized — keeping the agent in the workflow without removing human judgment.

7. 07

   Industry-tuned confidence thresholds

   Confidence requirements are calibrated to the industry's accuracy standards — financial document extraction for insurance carries different thresholds than logistics manifests.

How it works

1. 01

   Document intake and classification

   Industry documents arrive via email or API. Each document is classified by type before extraction begins — wrong classification triggers wrong extraction.

2. 02

   Primary AI extraction

   The primary model extracts all configured fields from each document type, producing a structured record mapped to the downstream system schema.

3. 03

   Dual-model confidence validation

   Every extracted field is scored for confidence. Fields below the threshold trigger a second model cross-validation pass before the data is accepted.

4. 04

   Cross-document validation

   Extracted data is cross-referenced across the submission's multiple documents — a named insured that differs between the ACORD and the loss run gets flagged.

5. 05

   System population and exception routing

   Validated records post to the downstream system. Low-confidence items route to the human review interface rather than committing automatically.

Inputs & Outputs

Use cases

• →

  Use this when your industry's document processing requires accuracy standards that general-purpose AI extraction doesn't meet without industry-specific tuning.

• →

  Use this when the same data from incoming documents is being manually re-entered across multiple systems — and errors in that re-entry create downstream reconciliation problems.

• →

  Use this when document volume is scaling faster than administrative headcount can absorb.

• →

  Use this when renewal tracking or compliance deadlines are managed manually and missed events represent meaningful revenue or regulatory exposure.

• →

  Use this when real-time visibility into your pipeline — submissions in flight, renewals due, quotes pending — doesn't exist because the data is stuck in document form.

Linked engagement

Related works