Agentic Productivity Tools

1 Token

(per SoV)

SoV Analysis

Turn complex SOVs into decision-ready property insight

The Weav.ai Agentic Productivity Tool for SOV Analysis helps insurance teams transform scattered property schedules, submission documents, and external data into a normalized, de-duplicated view of risk. Instead of manually reconciling rows, chasing missing attributes, or rebuilding comparisons at renewal, teams can quickly understand exposure, detect material changes, and evaluate each property against appetite and underwriting guidelines.

The tool combines property data from multiple sources and enriches it with key exposure and CAT attributes. It combines with connected external data sources to capture complete knowledge about the risk, and makes the results available for natural-language questions — so underwriters, risk teams, and portfolio managers can move from raw schedules to clearer, faster, more defensible decisions.

1 Token

(per Run)

Loss-run Analysis

Turn scattered loss history into a clear view of risk

Weav.ai's Agentic Productivity Tool for Loss-Run Analysis helps underwriting and claims teams transform prior loss runs from multiple carriers and lines of business into one consolidated, normalized loss history. Instead of manually reconciling inconsistent formats, re-keying claim details, or building trend analysis by hand, teams can quickly see loss patterns, evaluate reserve adequacy, and understand how an account measures against underwriting appetite.

The tool combines historical loss data with current policy claims activity, surfaces frequency and severity trends, and evaluates the full picture against your guidelines — so underwriters and claims professionals can move from raw loss runs to faster, clearer, more defensible decisions.

1 Token

(per Business)

Business Research Agent

Know the business. Know the risk.

Weav.ai's Business Research Agent is a deep research tool that determines the accurate risk profile of a business and recommends the right classification codes — NAICS, SIC, Workers Comp, and General Liability. The agent researches the business across public web sources, builds a profile of its actual operations, and maps those operations to the appropriate industry and class codes rather than accepting what was submitted on the application.

Misclassification quietly distorts pricing, loss ratios, and claim outcomes — and it's often invisible until audit or after a claim. The Business Research Agent replaces manual searching and guesswork with documented, evidence-backed classification, so premiums reflect real exposure, insureds are treated consistently and fairly, and the reasoning behind every code recommendation is defensible.

1 Token

(per Report)

Payroll Agent

Payroll in. Exposure out.

Weav.ai's Payroll Agent helps premium audit and underwriting teams transform payroll reports from any provider into a normalized, state-by-state view of exposure. Instead of re-keying reports from different formats, manually sorting wages by class code, or chasing subcontractor detail across documents, teams get a consolidated exposure picture ready for audit and rating.

The agent reads reports from multiple payroll providers, breaks exposure out by state and class code, and separates wage types and subcontractor payments — so audits close faster and premium reflects the actual exposure.

1 Token

(per Summary)

Contextual Document Summation

Read the submission. Not every page.

Every submission, claim, and audit arrives with a stack of documents someone has to read. This agent generates contextual summaries of those documents — surfacing what matters for the decision at hand rather than producing a generic abstract that leaves you reading the source material anyway.

The agent processes documents across formats — PDFs, emails, spreadsheets, and attachments — extracts the information relevant to the specific workflow, and delivers the summary directly into your workbench and policy admin system. So the context is waiting where the work already happens, not buried in an inbox or a shared drive. The result is faster review cycles, fewer missed details, and more defensible decisions — because the summary is built for the decision, not just for reading.