AI Agents

Context

Built for insurance within the Claims Processing stack and owned by Claims, this agent focuses on a critical mid-stream step: turning damage photos and documents into structured information and draft repair cost estimates. It sits after intake and basic document checks and before final settlement decisions, helping adjusters move from “we have evidence” to “we have a usable estimate” without starting from a blank page every time.

What it does

The agent ingests the claim’s visual and documentary evidence—photos, repair quotes, adjuster reports, invoices, inspection documents—and extracts the key damage details that matter for assessment: what was damaged, to what extent, and which repair or replacement actions are implied. Based on these extracted details, it drafts a repair cost estimate aligned with your standard estimating structures, so handlers see a pre-populated, consistent view rather than having to type everything in manually. The draft can then be reviewed and adjusted by claims staff, but the heavy lifting of reading, interpreting, and structuring the evidence is done by the agent.

Core AI functions

The core capability is “vision + document extraction”: the agent uses computer vision to interpret images and link them to known damage types, and document extraction to pull relevant fields and figures from repair quotes, reports, and invoices. It then maps this combined signal into a structured estimate template, aligning items, quantities, and costs where available, so that downstream rules and payment checks can work on clean, consistent data instead of free-form text and ad hoc screenshots.

Problem solved

Manual estimate drafting is slow. Adjusters and handlers spend significant time reviewing photos and documents, retyping details into estimating tools or spreadsheets, and aligning them with internal formats. That not only drags out assessment time but also leads to variability in how similar damage is described and costed. By automating the extraction and first draft, the agent cuts this manual effort and reduces the inconsistency that comes from everyone “writing it their own way.”

Business impact

The main impact is accelerated assessments and more consistent estimates. Claims teams can move faster from evidence to decision-ready files, which shortens overall cycle time and improves the claimant experience. At the same time, using a standardised, system-generated structure for damage and costs makes it easier to compare similar claims, apply rules consistently, and reduce rework on misaligned or incomplete estimates. This is particularly valuable for Tier 1–2 insurers with higher volumes and complex repair patterns.

Integration and adjacent use cases

Integration complexity is medium: the agent needs access to the claim record and to the repositories where photos and documents are stored, plus a way to store extracted damage details and draft estimate structures back into the claims system so handlers and downstream rules can use them.

Common combinations in this stack:

• FNOL Intake & Triaging Agent to capture First Notice of Loss and route the file correctly before any detailed assessment;

• Claims Document Completeness Checker to make sure the required photos and documents are present and valid before damage extraction starts;

• Policy Terms & Coverage Validator (Claims) to confirm how policy coverages, limits, and deductibles apply to the type of damage identified;

• Eligibility & Threshold Checker (Claims) to apply deductibles, sub-limits, and other threshold rules on top of the drafted estimate before payment; and

• Fraud Risk Signal Agent (Claims) to flag suspicious patterns in claim behaviour and documentation across FNOL, evidence, and estimates so potential fraud or exaggeration can be reviewed by SIU.