Innovate
New Ideas
Develop
Quality Solutions
Deliver
On Time, Every Time
Drive Success
Business Growth
Automatically reviewing thousands of warranty claims in seconds—approving eligible requests instantly, detecting financial risks, and routing only complex cases for human review.
Client
Built By
Industry
Solution Type
Warranty and insurance companies receive a huge number of claims every day, but manual adjudication often delays approvals, raises operational expenses, and ultimately results in differences in financial decisions. To tackle such difficulties, Augmantis, sponsored by eComStreet, created an AI Claim Adjudicator that integrates deterministic business logic with cognitive AI to automate claim reviews while maintaining financial accuracy and regulatory compliance. The platform instantly approves eligible claims, flags high-risk cases for human review, validates repair costs, and maintains complete auditability.
Uses advanced LLM reasoning to compare the mechanic's reported failure with the requested replacement parts, automatically identifying repair requests that are inconsistent with the diagnosed issue before claim approval.
Analyzes invoices, damaged-part photos, and vehicle images to verify that the claimed component is visible, damaged, and accurately matches the submitted repair request.
Validates requested repair costs against live MSRP pricing APIs and automatically adjusts authorized reimbursements to approved pricing limits, ensuring consistent financial compliance and cost control.
Automatically detects high-risk repairs, aftermarket parts, or missing documentation, placing claims into a Pending Document status until all required supporting evidence is uploaded and verified.
Manual claim adjudication requires adjusters to validate coverage, repair costs, supporting documents, and damage evidence before approving claims. As volumes increase, these repetitive reviews slow settlements and make consistent, policy-driven decisions more difficult.
Large Language Models (LLMs) are effective at understanding documents but cannot be trusted for precise financial calculations, policy date validation, or enforcing strict reimbursement rules. Using AI alone for monetary approvals introduces unacceptable financial risk.
Augmantis developed a Hybrid Deterministic-Cognitive Engine that separates financial logic from AI reasoning.
Claims frequently include invoices, dashboard images, odometer photos, and damaged component images. Reviewing each image independently limits the AI's ability to detect inconsistencies or fraudulent submissions.
The platform implements Batch Multi-Modal Vision Analysis, where all uploaded images are retrieved simultaneously from Amazon S3 and analyzed within a single vision prompt. This enables the AI to:
The AI Claim Adjudicator combines deterministic business logic, multimodal AI, and enterprise policy rules to automatically evaluate warranty claims, approve eligible repairs, detect financial risks, and deliver explainable decisions within seconds.
Automatically evaluates routine, low-value warranty claims against contract terms, coverage rules, and eligibility criteria. Qualified claims are approved instantly without entering the manual review queue, reducing processing time while allowing adjusters to focus on complex exceptions.
Validates requested repair costs against industry-standard MSRP pricing APIs before authorizing reimbursement. If submitted repair charges exceed approved pricing thresholds, the system automatically caps payouts according to enterprise financial policies and warranty guidelines.
Processes invoices, damaged-part photographs, dashboard images, and supporting documents using multimodal AI. The system extracts structured information, verifies visual evidence, and confirms that the reported damage aligns with the claimed repair before adjudication.
Identifies high-risk claims, aftermarket components, or missing supporting documents during claim evaluation. Claims requiring additional verification are automatically moved to a pending status until the required documentation is uploaded and validated.
Captures every automated approval, denial, reimbursement adjustment, and business rule executed throughout the adjudication process. Each decision is permanently recorded within the claim history, ensuring complete transparency, regulatory compliance, and enterprise-grade auditability.
The solution is built on a scalable, cloud-native architecture designed for high-volume enterprise claims processing.
Amazon Bedrock Foundation Models power intelligent document understanding, contextual reasoning, and enterprise AI decision-making throughout the adjudication workflow, enabling accurate interpretation of warranty contracts and repair documents.
Agentic AI orchestrates the complete claim evaluation process by autonomously executing backend business logic, coordinating validation tasks, and invoking enterprise tools based on claim requirements.
A Python-based financial engine performs precise policy validation, reimbursement calculations, and pricing enforcement, ensuring mathematically accurate claim decisions without relying on probabilistic AI outputs.
Amazon S3 Batch Vision Processing analyzes invoices, damaged-part photographs, dashboard images, and supporting documents together to validate claim evidence and strengthen fraud detection.
Built on a cloud-native serverless architecture, the platform automatically scales with enterprise workloads while delivering high availability, operational efficiency, and cost-effective performance.
Every automated decision, financial adjustment, and claim action is permanently recorded to provide complete transparency, regulatory compliance, and end-to-end decision traceability.
An instantaneous, extremely accurate autonomous approval engine replaced laborious, repetitive manual reviews.
Event-driven architecture allows routine maintenance and low-risk claims to be fully verified, financially capped, and approved in seconds rather than days.
Deterministic rules ensure requested amounts never exceed MSRP and that aftermarket parts trigger mandatory document uploads, guaranteeing strict financial compliance.
Multi-modal vision AI continuously acts as a forensic investigator, silently cross-referencing requested parts against uploaded damage photos to catch inconsistencies instantly.
Automated approval of routine claims within seconds, significantly reducing manual intervention and accelerating straight-through claim processing.
Improved adjuster productivity by routing only complex or high-risk claims for manual review, allowing teams to focus on exception-based decision-making.
Reduced claim overpayments through automated MSRP enforcement, ensuring reimbursements consistently comply with enterprise pricing policies.
Enhanced fraud detection using multi-image forensic analysis, validating visual evidence across multiple claim documents and supporting photographs.
Strengthened compliance through immutable audit logs and explainable AI decisions, providing complete transparency for governance and regulatory reporting.
Delivered consistent, policy-driven adjudication across every claim, maintaining accurate decisions regardless of claim complexity or processing volume.
Every component chosen for enterprise reliability, security, and cost efficiency.
No sales process. No deck. No long-term contracts. Just a direct conversation about what we'd build for your claims operation — and a working AI system in 4 weeks.
Augmantis is the AI engineering division of eComstreet, built to bring production-grade agentic AI to US businesses. We design, build, and ship AI systems that handle real workflows — claims, operations, support, and beyond — using a US + India engineering team with 15+ years of delivery experience behind us.
We don't do demos. We do production.
