This case study documents the transformation of a legacy, SQL-dependent public health analytics system into a production-grade Natural Language Analytics Platform using AWS Generative AI and the AWS Strands Agents SDK. The solution democratized access to health data by enabling non-technical government officials to ask questions in plain language and receive instant, actionable insights—without relying on analysts, SQL skills, or manual report generation. Query response times were reduced from hours to seconds, directly improving decision velocity during public health operations and emergencies.
Client Overview:
This public health NGO operates a critical healthcare analytics system that enables government officials to access health data for informed policy decisions. The platform processes over 200 monthly data requests from district collectors, IAS officers, and field officers managing vaccination programs, disease surveillance, and resource allocation. The system manages 5+ million citizen health records across 50+ districts and serves government officials making decisions that directly impact public health outcomes. With healthcare resource allocation of ₹10+ crores annually dependent on timely reporting, the platform’s accessibility directly impacts disease prevention and emergency response. The legacy system was generating 304 monthly support tickets and requiring 4-6 hours per routine query, creating dangerous delays during health emergencies.
Business Challenge
The NGO’s legacy analytics environment created severe operational bottlenecks that directly impacted public health outcomes and regulatory compliance.
Core Challenges
- Technical Skill Barrier:
85% of government officials lacked SQL or analytics expertise. Only ~15% specialized analysts could generate reports, creating constant bottlenecks for ~480 users.
- Slow Decision-Making:
Routine questions such as “Show vaccination coverage by district” required 2–4 hours of SQL work followed by 2–3 hours of manual formatting across 200+ monthly data requests.
- Emergency Response Delays:
During disease outbreaks and disaster scenarios, delays of 2–3 days in accessing insights increased the risk of 20–30% wider disease spread.
- Heavy IT Dependency:
The analytics team spent 60–70% of their time handling routine data requests instead of strategic program optimization.
- Data Underutilization:
Despite maintaining health records for 5+ million citizens, less than 30% of available data was actively used.
- Poor User Experience:
Field officers avoided complex BI tools and relied on outdated Excel reports or manual data collection.
- Compliance Pressure:
Government reporting deadlines were met only 65% of the time, putting ₹10+ Crores of annual funding at risk.
Quantified Business Impact
- Operational Inefficiency: ₹40 Lakhs annually in lost productivity
- Public Health Risk: 2–3 day response delays during outbreaks
- Resource Misallocation: 15–20% inefficiency in vaccines, medicines, and staffing
- Funding Risk: ₹10+ Crores tied to delayed compliance reporting
- Poor Data ROI: <30% utilization despite heavy investment in data systems
Why Traditional Solutions Failed
- Commercial BI Tools (Tableau, Power BI):
Required SQL expertise, steep learning curves, and ₹15–20L annual licensing, unsuitable for non-technical government users.
- Custom NLP Platforms:
Required 6–9 months, 3–4 ML engineers, ₹80L+ implementation cost, and high operational overhead.
- Console-Based Bedrock Agents:
Less accuracy, High Latency, and manual deployments—unsuitable for production-grade enterprise use.
- Amazon Q Business:
Limited customization and concerns around exposing confidential health data.
Implementation Plan
- Architecture & Use-Case Design
Designed a production-grade, code-first GenAI architecture aligned with government compliance and enterprise standards - Use-Case Prioritization
Identified and prioritized natural language analytics scenarios for government officials and field officers - Multi-Agent System Development
Built a multi-agent GenAI system using AWS Strands Agents SDK with specialized agents for validation, SQL generation, execution, insights, visualization, and follow-ups - Secure Data Integration
Integrated securely with on-prem PostgreSQL database without data migration or exposure - Serverless Agent Execution
Implemented Amazon Bedrock Agent Core for scalable, serverless execution of AI agents - CI/CD & Code Governance
Established Git-based version control, CI/CD pipelines, and automated testing with 85% code coverage - User Acceptance Validation
Conducted UAT with government officials across multiple test cycles to validate accuracy and usability - User Enablement & Rollout
Executed phased onboarding and training to drive adoption across departments - Monitoring & Compliance Operations
Enabled real-time monitoring, logging, audit trails, and compliance reporting for operational stability
Solution
Application Architecture
The solution was implemented as a production-grade natural language analytics platform powered by a multi-agent architecture. Government officials interact with the system through a web-based UI hosted on ECS Fargate, submitting queries in plain language. Requests are routed via API Gateway to Amazon Bedrock Agent Core, which orchestrates multiple AWS Strands agents. These agents validate queries, generate PostgreSQL SQL, execute queries securely against on-prem database, derive insights, generate visualizations, and suggest relevant follow-up questions. The architecture supports multi-turn conversations and concurrent users and returns structured insights, charts, and recommendations in under 20 seconds.
AWS Infrastructure Implementation
The platform leverages a serverless-first AWS architecture to ensure scalability, reliability, and cost efficiency. ECS Fargate hosts the frontend application, while API Gateway provides secure HTTPS endpoints with throttling and access controls. Amazon Bedrock and Bedrock Agent Core execute AI agents at scale. Health data remains within on-prem PostgreSQL. Visual outputs are stored in Amazon S3 with pre-signed URLs. EventBridge enables event-driven workflows, while CloudWatch and CloudTrail provide centralized monitoring, logging, and audit trails. All data is encrypted using AWS KMS, ensuring compliance with government data protection requirements.
AI Transformation
The transformation replaced SQL-dependent, analyst-driven reporting with a code-first GenAI platform built using AWS Strands Agents SDK. Multiple specialized agents were defined entirely in Python, version-controlled in Git, and fully testable. Amazon Nova Micro models were used for speed-critical tasks such as validation, SQL generation, execution, and follow-up reasoning, while Amazon Nova Pro handled complex analytics, insights, and visualization logic. Prompting strategies were implemented as code rather than console configurations, enabling A/B testing, versioning, and continuous optimization. Bedrock Guardrails enforced PII protection, safety constraints, and responsible AI practices throughout the system.
Benefits and Key Value Realised
| Metric | Before | After | Improvement |
|---|---|---|---|
| Query Response Time | 4–6 hours | < 20 seconds | ~95% faster |
| User Adoption Rate | 15% (SQL experts only) | 78% (all users) | 420% increase |
| Data Utilization | ~30% | 75%+ | 150% improvement |
| Support Requests | 200+ per month | ~12 per month | ~70% reduction |
| Emergency Response Time | 2–3 day delays | Near real-time | Critical delay eliminated |
| Reporting Compliance | 65% on-time | 100% on-time | Full compliance achieved |
| Concurrent Users Supported | ~150 | 500+ | 233% increase |
| User Satisfaction Score | 6.2 / 10 | 8.6 / 10 | Significant improvement |
| Operational Productivity Loss | ₹40L annually | Eliminated | ₹40L savings |
Customer Quote:
“This natural language analytics platform has revolutionized how we work with public health data. Previously, getting even simple insights required days of waiting. Now, district collectors and field officers can ask questions in plain language and receive instant, actionable insights with visualizations. The code-first approach with Strands has enabled our technical team to make updates rapidly. The adoption rate among non-technical government officials has exceeded all our expectations. This is exactly the kind of AI-powered solution that delivers measurable impact on public health outcomes.”
– Chief Technology Officer, Digital Health NGO
Realized Customer Values
Operational Excellence
The implementation dramatically improved operational efficiency by eliminating manual, SQL-dependent workflows and enabling real-time access to public health data through natural language queries. Query response times were reduced from 4–6 hours to under 20 seconds, allowing government officials to make faster, evidence-based decisions without analyst dependency. Support requests dropped from over 200 per month to approximately 12, freeing the analytics team to focus on strategic initiatives instead of routine reporting. Automated workflows, intelligent routing, and standardized outputs significantly reduced friction across departments, resulting in higher productivity, improved user experience, and consistent service reliability.
Innovation Acceleration
By adopting a code-first GenAI architecture using AWS Strands Agents SDK, the organization established a scalable foundation for continuous innovation. The multi-agent design enabled rapid iteration, testing, and deployment of new analytical capabilities without disrupting production systems. Prompt logic and agent behavior were version-controlled and optimized through A/B testing, improving routing accuracy to 94%. This approach allowed the NGO to quickly introduce advanced features such as automated KPI generation, context-aware follow-up questions, and dynamic visualizations—capabilities that were previously infeasible in the legacy environment.
Cost Optimization
The GenAI transformation delivered substantial cost efficiencies by eliminating productivity losses and avoiding expensive alternative solutions. Automating analytics workflows removed approximately ₹40 Lakhs in annual operational waste caused by manual reporting delays and analyst bottlenecks. The organization avoided the need for a custom NLP build, which would have required ₹80L+ in upfront costs and significant ongoing maintenance. Additionally, reduced support demand and improved analyst utilization allowed existing teams to deliver higher value without additional staffing, achieving positive ROI within the first year of deployment.
Security and Governance
Security and governance were embedded into the platform by design to meet stringent government and public health requirements. Health data remained within secure VPC and on-prem environments, with no data migration or exposure to external systems. Encryption at rest and in transit, IAM least-privilege access, MFA, and comprehensive audit logging ensured compliance with data protection standards. Bedrock Guardrails enforced responsible AI practices by preventing PII leakage, unsafe operations, and hallucinations, while CloudTrail provided full traceability for regulatory audits and accountability.
Public Health Impact
The most significant value realized was the direct improvement in public health outcomes driven by faster, data-informed decision-making. Real-time access to insights eliminated 2–3 day delays during disease outbreaks and emergency scenarios, enabling quicker interventions and reducing the risk of wider disease spread by an estimated 20–30%. Improved data utilization—from 30% to over 75%—enhanced the accuracy of healthcare resource allocation, vaccination planning, and program monitoring. The platform also ensured 100% on-time government reporting, safeguarding ₹10+ Crores in annual funding and strengthening trust between the NGO and state health authorities.
Conclusion
This case study demonstrates how a code-first, multi-agent GenAI platform built using AWS Strands Agents SDK can transform public health analytics at scale. By replacing SQL-dependent, manual reporting processes with natural language access to data, the organization empowered government officials to make faster, more informed decisions while maintaining strict security, compliance, and data confidentiality standards. The solution delivered measurable outcomes, including 95% faster insights, 78% user adoption, significant reduction in operational overhead, and improved emergency response readiness across multiple states. Most importantly, the platform established a scalable, production-grade blueprint for applying Generative AI in regulated government and healthcare environments, proving that GenAI can drive real-world impact when implemented with rigor, governance, and enterprise-grade engineering.
