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Turning thousands of scattered warranty claim records into clear, conversational insight — so adjusters spend less time digging and more time deciding.
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Solution Type
Insurance and warranty companies process hundreds of claims spanning both structured datasets and unstructured documents — repair invoices, warranty contracts, inspection reports, and damage photographs. Adjusters routinely wrestle with fragmented data asking questions like "Is the alternator covered?", "What's the claim balance?", "Any prior claims on this vehicle?"
To solve this, Augmantis built the AI Claim Analysis Co-Pilot powered by Amazon Bedrock Foundation Models, Agentic AI Workflows, and a Hybrid RAG architecture. The result: adjusters can ask questions conversationally, receive verified answers from both structured and unstructured sources, trigger backend business logic autonomously, and get fact-grounded insights — without touching a single report or database query.
Autonomously executes backend Python functions based on adjuster intent — validating coverage, calculating reimbursements, and retrieving claim history.
Simultaneously queries structured enterprise databases and unstructured document stores to generate complete, context-rich responses.
Every answer is grounded in verified enterprise data. The AI never assumes or fabricates — if information isn't in the data, it says so.
Built on AWS Lambda, Amazon EventBridge, and Amazon S3 Vectors — scales automatically while eliminating 90%+ of infrastructure costs vs. managed vector databases.
Four complex technical problems had to be solved before this could work at enterprise scale.
Automobile warranty agreements and auto repair bills contain nested tables, inconsistent grids, handwritten remarks, checkboxes, signatures, and damage photographs. Conventional OCR extracts text but can't comprehend document layout — mismatching information, missing checkbox values, and ignoring handwritten notes.
Augmantis deployed Multimodal Foundation Models on Amazon Bedrock with Native Foundation Model Parsing and Hierarchical Chunking. The model interprets complex grids, handwriting, checkmarks, and damage photographs while maintaining document structure through spatial-aware prompting — achieving near 100% extraction accuracy.
LLMs are designed to be helpful — even when information is missing. In warranty claims, an AI might conclude that "Alternator" in a warranty document is covered based on general automotive knowledge, not the actual contract. This can lead to incorrect eligibility, reimbursement amounts, and repair approvals.
Augmantis implemented a Fact-First AI Architecture where every response is grounded in authenticated enterprise data before any AI reasoning occurs — coverage limits, authorized components, MSRP limits, claim balances, and repair receipts. If the data isn't there, the AI responds: "I cannot determine that from the contract."
Early architectures used managed vector databases like Amazon OpenSearch Serverless for RAG. Effective — but at ~$700/month even when idle. This made the solution economically unfeasible for PoC environments and variable enterprise workloads.
Augmantis adopted Hybrid-RAG using Amazon Bedrock Knowledge Bases and Amazon S3 Vectors. Semantic embeddings in Amazon S3 enable serverless document retrieval with zero dedicated vector infrastructure. Folder-based vector filtering via x-amz-bedrock-kb-source-uri provides claim-level isolation — reducing AI infrastructure costs by over 90%.
The Co-Pilot serves multiple dealerships on a shared enterprise platform. Dealer A must never access Dealer B's claims. Relying on LLMs to enforce data access is a security risk — LLMs cannot provide deterministic, auditable security guarantees for enterprise data.
A Deterministic Security Wrapper under Zero-Trust architecture intercepts every authenticated request and auto-injects the dealer's unique ID into every database query — before data ever reaches the Foundation Model. AWS IAM, Bedrock Guardrails (prompt injection protection), and immutable audit trails complete the security layer.
A cloud-native platform powered by Agentic AI, Hybrid-RAG, Multimodal Document Intelligence, and a fully serverless AWS architecture — built for accuracy, security, and scale.
Amazon Bedrock Multimodal Foundation Models power natural language understanding, document reasoning, vision, and conversational AI. The Bedrock Converse API enables Agentic Tool Calling — the AI autonomously invokes backend Python functions based on adjuster intent: retrieving claim records, validating warranty coverage, calculating reimbursement limits, and searching claim history before generating any response.
Claims intelligence exists across both structured databases and unstructured documents. The platform implements Hybrid-RAG that simultaneously queries: structured enterprise data (customer claim records, warranty registrations, coverage limits, claim balances, financial transactions, historical decisions) and unstructured enterprise documents (warranty contracts, mechanic repair invoices, inspection reports, damage photographs). By combining both sources into a single reasoning workflow, the AI generates responses supported by complete claim context.
Warranty contracts and repair invoices contain complex grids, nested tables, multi-column layouts, checkboxes, handwritten notes, signatures, and damage photographs. Augmantis implemented Native Foundation Model Parsing with Hierarchical Chunking — processing documents by visual hierarchy, not raw text. Spatial-aware prompts enable the model to interpret table structures as key-value pairs, detect checkmarks and crosses, understand handwritten annotations, and correlate damage photographs with textual claim evidence.
MongoDB Atlas provides a high-throughput NoSQL data layer for customer profiles, warranty registrations, claim history, repair records, financial limits, and historical decisions. Amazon S3 Vectors with Bedrock Knowledge Bases provides serverless semantic retrieval — zero dedicated vector infrastructure. BSON Decimal128 is used throughout the financial pipeline to eliminate floating-point rounding errors in reimbursement calculations, MSRP limits, deductible values, and subclaim totals. Dynamic folder-level semantic filtering ensures every retrieval is scoped to the active claim only.
Backend services run as isolated AWS Lambda microservices — document retrieval, contract validation, claim analysis, coverage verification, financial calculations, and AI orchestration each in their own function. Amazon EventBridge decouples long-running AI reasoning from the frontend, enabling asynchronous processing without blocking the user interface. Direct AWS Lambda Function URLs bypass API Gateway timeout limits for deep AI reasoning tasks — large document analysis, complex claim investigations, and extended AI processing.
Enterprise AI requires deterministic security, not probabilistic protection. Governance is embedded into every architectural layer.
Instead of relying on the LLM to determine access permissions, Augmantis implemented hardcoded agent-level database query filtering. Every authenticated request automatically injects the authorized Dealer ID into backend queries before any information reaches the AI.
AWS Bedrock Guardrails protect the Foundation Model before prompts reach the reasoning engine — automatically blocking threats and keeping conversations within approved business boundaries.
Every AI interaction is automatically appended to immutable enterprise activity logs — providing complete transparency for governance, compliance, and regulatory reporting.
AWS IAM enforces the Principle of Least Privilege across every AI micro-agent. Each microservice receives only the permissions necessary for its specific responsibility — combined with deterministic database filtering and serverless isolation for a complete Zero-Trust posture.
An instantaneous, conversational, and highly structured insights engine replaces fragmented data seeking.
The Hybrid-RAG architecture provides precise coverage answers in a matter of seconds by rapidly synthesizing 30-page unstructured contract PDFs with structured database records (financials, history).
A Fact-First prompt architecture and "smart intent routing" guarantee that the AI never experiences coverage hallucinations. It only uses uploaded S3 legal papers and confirmed MongoDB data.
The Copilot eliminates the need to open numerous tabs by directly embedding line-item breakdowns and flawlessly structured coverage summaries into the adjuster's dashboard through the generation of clean, dynamic Markdown.
Adjusters now access enterprise databases and unstructured claim documents in natural language — getting verified policy details, warranty information, repair history, and coverage limits in seconds.
Fact-First AI Architecture prevents hallucinations by anchoring every response to validated enterprise data — eliminating AI-generated assumptions from financial decisions.
Serverless computing and event-driven orchestration eliminated the need for dedicated servers, reducing operational burden and enabling automatic workload scaling.
Zero-Trust multi-tenant security architecture guarantees complete dealer data isolation — cross-tenant exposure is architecturally prevented, not just policy-controlled.
BSON Decimal128 financial precision throughout the pipeline ensures lossless reimbursement calculations, MSRP capping, and deductible processing — every cent accounted for.
Immutable audit trails on every AI interaction provide complete regulatory transparency, governance documentation, and compliance reporting out of the box.
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.
