MarutAI | The Science of Autonomy

The challenge

Claims arrive as unstructured documents with variable formats. Manual review is slow, adjudication is inconsistent across adjusters, and compliance requirements add processing overhead.

The approach

Models extract claim details, assess validity against policy terms, and route decisions within compliance constraints. The process twin models the claims workflow to ensure routing and adjudication stay within stated operational bounds.

Expected outcome

Route complete claims sooner and send exceptions to review under the configured compliance rules.

The challenge

Risk evaluation depends on large, heterogeneous data sets and evolving regulatory requirements. Manual underwriting is time-intensive and subject to inconsistency across analysts.

The approach

A process twin models the risk portfolio from historical loss data and applicant profiles. Decision models assess applicant risk under stated actuarial and regulatory assumptions.

Expected outcome

Apply a consistent risk assessment process while preserving the assumptions and constraint decisions used in each assessment.

The challenge

Policy documents are unstructured and vary by carrier, jurisdiction, and line of business. Extracting structured data manually does not scale.

The approach

Models extract structured fields from policy documents with schema validation and confidence scoring. Outputs are validated against expected document structures before entering downstream systems.

Expected outcome

Convert policy documents into validated, normalized fields for downstream systems.