AWS AI Customer Case Study
AI-Powered Regulatory Compliance & Litigation Intelligence Agent
Revolutions.ai helped LITT build a multi-agent legal and regulatory intelligence platform on Amazon Bedrock that unifies regulatory monitoring, litigation analytics, contract intelligence, and autonomous legal-request triage.
Customer: LITT
Industry: Legal Technology / RegTech
Market: Enterprise
Partner: Revolutions.ai
Engagement at a glance
- Supervisor-led multi-agent orchestration with four specialist agents.
- Regulatory coverage across SEBI, RBI, MCA, GST, Income Tax, FEMA, IRDAI, and DPDP.
- Contract intelligence across 5,000+ agreements.
- Auditable decision traces, 150+ validation rules, and human review for high-risk matters.
Customer context
Transforming fragmented legal and regulatory intelligence into one execution workspace
LITT supports law firms, company secretaries, CFOs, and regulated organizations operating across India’s complex legal and compliance landscape. Its clients needed faster research, continuous monitoring, stronger litigation visibility, and better control over large contract portfolios.
Key business challenge
- Regulatory intelligence was fragmented across eight regulator portals.
- Compliance research took 4–6 hours per query.
- Litigation analysis required manual review of 40–50 court orders.
- More than 5,000 agreements lacked structured tracking for CPI triggers, renewals, and conflicts.
- Legal teams handled 40–60 daily requests through Slack, Teams, and email without triage automation.
- An average of 12 regulatory filing deadlines were missed each quarter.
Business and technical objectives
- Reduce compliance research to under ten minutes with full source citations.
- Monitor regulatory changes with less than 15-minute alert latency.
- Automate more than 60% of routine legal requests.
- Identify contract revenue leakage and recovery opportunities.
- Maintain less than 2% error rate through 150+ validation rules.
- Keep all processing inside the customer’s AWS environment with no model-training retention.
Partner solution
A legal department operating model implemented through Amazon Bedrock Agents
Revolutions.ai designed a supervisor-specialist architecture that mirrors the structure of a high-performance legal team. The supervisor coordinates specialist agents, shares context, enforces validation rules, and creates decision traces linking every conclusion to source documents.
Regulatory Compliance Agent
Monitors eight regulators, extracts circulars and amendments, maps obligations to entities, and maintains a live compliance calendar.
Litigation Intelligence Agent
Builds entity-level litigation dossiers with bench, judge, counsel, precedent, and outcome intelligence.
Contract Intelligence Agent
Indexes 5,000+ agreements and identifies CPI drift, renewal cliffs, and cross-contract conflicts.
Agent Face
Triages requests from Slack, Teams, and email, executes routine playbooks, and escalates judgment-intensive matters.
Supervisor Agent
Coordinates cross-domain workflows, enforces 150+ validation rules, and generates auditable decision traces.
Human Review Layer
Routes matters below 85% confidence and novel regulatory interpretations to qualified legal professionals.
Domain-specific tools
- Regulatory filing ingestion with Amazon S3 and Amazon Textract.
- Litigation docket search through Amazon OpenSearch Serverless.
- Contract clause analysis using Amazon Bedrock Knowledge Bases.
- Intent classification, urgency scoring, and structured legal briefs.
- Decision-trace generation with citation and provision validation.
Foundation model approach
Claude Sonnet 4 was selected for primary legal reasoning, cross-regulation analysis, tool use, and structured outputs. Claude Opus 4 was reserved for exceptional cases involving conflicting precedents, novel interpretations, or multi-jurisdictional complexity.
Technical architecture
High-availability and scalable AWS architecture
The platform uses an event-driven, serverless-first design with Amazon Bedrock Agents, Amazon DynamoDB, AWS Step Functions, Amazon OpenSearch Serverless, multi-AZ AWS Lambda, and integrated security and observability services.
Technology stack
AWS services used
AWS service
How it is used
Amazon Bedrock Agents
Coordinates the supervisor and specialist agents and manages cross-domain orchestration.
Amazon Bedrock — Claude Sonnet 4
Primary reasoning model for legal analysis, regulatory interpretation, and structured outputs.
Amazon Bedrock — Claude Opus 4
Escalation model for novel interpretations and conflicting precedents.
Amazon Bedrock Knowledge Bases
Stores regulatory, litigation, contract, and playbook knowledge collections.
Amazon Bedrock Guardrails
Protects legal privilege, redacts PII, enforces denied topics, and validates grounding.
Amazon S3
Stores documents with versioning, Object Lock, and archival lifecycle policies.
Amazon Textract
Extracts structured content from filings, annual reports, court orders, and scanned legal documents.
Amazon OpenSearch Serverless
Supports semantic and keyword search across regulatory and litigation corpora.
Amazon DynamoDB
Stores compliance scores, calendars, exposure profiles, and decision logs.
AWS Lambda
Executes agent tools, ingestion processes, validation rules, and catalog actions.
AWS Step Functions
Coordinates long-running workflows, retries, approvals, and agent task sequences.
Amazon EventBridge
Triggers regulatory monitoring, contract re-examination, and workflow events.
Amazon SNS
Delivers alerts, escalations, and regulatory notifications.
Amazon API Gateway
Exposes secure APIs for dashboard, triage, and workflow access.
Amazon Cognito
Provides institutional authentication with MFA and federation support.
Amazon CloudWatch
Monitors agent sessions, tool calls, validation pass rates, and cost-per-query.
AWS KMS, IAM, CloudTrail, Config, and GuardDuty
Provide encryption, identity controls, audit logging, configuration governance, and threat detection.
Outcomes and business impact
Measurable impact within twelve weeks of production deployment
8 min
Compliance research time
62%
Routine requests automated
0
Filing deadline misses
₹4.2 Cr
Contract revenue recovered in Q1
<2%
4 min Median request resolution
<2%
Agent error rate
<15 min
Regulatory alert latency
100%
Decision trace auditability
- Senior lawyers shifted approximately 80% of their time toward strategic advisory.
- Agent Face provided capacity equivalent to three to four junior associates.
- Penalty exposure was reduced by eliminating missed filing deadlines.
- Litigation predictive accuracy reached 78% for supported analytical scenarios.
Security, reliability, and responsible AI
Controls designed for privileged legal and regulatory workloads
Security and account governance
- Dedicated AWS account per institutional client.
- Per-tenant AWS KMS keys and IAM-enforced data isolation.
- Private service endpoints with no client data traversing the public internet.
- STS temporary credentials and least-privilege access.
- CloudTrail, AWS Config, and GuardDuty for governance and auditability.
Operations and reliability
- Multi-AZ deployment across stateful services.
- Amazon DynamoDB Global Tables for low-RPO disaster recovery.
- Amazon S3 versioning and Object Lock for durable document retention.
- Dead-letter queues, retries, and circuit breakers for failed integrations.
- Provisioned concurrency for low-latency Agent Face responses.
Responsible AI controls
- Legal privilege safeguards through Bedrock Guardrails and privilege tags.
- PII redaction before litigation analytics to reduce demographic bias.
- Grounding checks at a 0.80 threshold.
- Denied topics prevent legal advice, liability decisions, and investment advice.
- Human review for low-confidence and novel legal interpretations.
Partner contribution
- Multi-agent architecture and model evaluation.
- Prompt engineering and domain-specific knowledge base design.
- 150+ rule validation framework and decision-trace system.
- Security architecture, CI/CD, observability, training, and handover.
Supporting evidence
Audit-ready artifacts maintained for the engagement
Detailed design, configuration, security, operational, and test evidence can be maintained privately for AWS technical validation and customer governance.
Architecture diagram
Bedrock agents, multi-AZ Lambda, data layer, integrations, and security controls.
Detailed design document
Requirements mapping, agent design, data flows, security, and non-functional controls.
Configuration document
Environment, service settings, thresholds, IAM roles, and deployment values.
Agent test evidence
Tool-call tests, validation rules, grounding, confidence thresholds, and escalation scenarios.
Security evidence
Privilege controls, IAM, AWS KMS, private endpoints, logging, and data isolation.
Operations evidence
CloudWatch dashboards, alarms, retries, DLQs, and incident runbooks.
Responsible AI controls
Guardrails, denied topics, bias mitigation, human review, and decision traces.
Cost model
Model usage, serverless service estimates, storage assumptions, and value analysis.
Customer acceptance
Training, handover, acceptance criteria, milestone feedback, and support model.Training, handover, acceptance criteria, milestone feedback, and support model.