AI Evaluation & Modern Data Platform for Digital Health Provider

Challenge

A rapidly expanding digital health provider was facing critical challenges in their AI operations and data infrastructure. Despite having sophisticated AI models powering their patient engagement platform, they lacked systematic ways to evaluate performance, manage data pipelines, and deploy updates reliably.

The problem:

• No standardized framework for evaluating AI model performance
• Legacy data infrastructure struggling with growing data volumes
• Manual deployment processes causing delays and errors
• Limited visibility into model behavior in production
• Fragmented tools and inconsistent practices across teams
• Inability to quickly iterate and improve AI capabilities

These challenges were creating bottlenecks in their product development cycle and risking the quality of patient interactions.

Our Solution

We built a comprehensive AI evaluation framework and modernized their entire data platform, enabling systematic testing, faster iteration, and reliable deployment of AI capabilities.

Phase 1: AI Evaluation Framework (6 weeks)

Built a sophisticated testing and evaluation system that brings software engineering rigor to AI development:

Automated Testing Pipeline:

• Implemented automated test suites for all AI models
• Created synthetic test datasets covering edge cases
• Built regression testing to catch performance degradation
• Established baseline metrics and performance thresholds
• Integrated evaluation into CI/CD pipeline

Evaluation Metrics Dashboard:

• Real-time monitoring of model accuracy, latency, and reliability
• A/B testing framework for comparing model versions
• User feedback integration for continuous improvement
• Automated alerting for performance anomalies
• Historical trend analysis and performance tracking

Impact: Reduced evaluation time by 40%, enabled data-driven decision making for model improvements, and caught potential issues before production deployment.

Phase 2: Data Platform Modernisation (8 weeks)

Transformed their data infrastructure to handle scale and enable faster insights:

Modern Data Pipeline:

• Migrated from legacy systems to cloud-native architecture
• Implemented Apache Airflow for workflow orchestration
• Built real-time and batch processing pipelines
• Created data quality monitoring and validation
• Established data governance and compliance controls

Data Warehouse & Analytics:

• Deployed cloud data warehouse for analytics (Snowflake/BigQuery)
• Optimized ETL pipelines for 60% faster processing
• Created data marts for different team needs
• Built self-service analytics capabilities
• Implemented data cataloging and discovery

Impact: 99.9% platform uptime, 60% faster data processing, and enabled teams to access insights independently.

Phase 3: Infrastructure & Deployment (6 weeks)

Created reliable, automated deployment infrastructure:

MLOps Platform:

• Built model versioning and registry system
• Implemented automated model deployment pipelines
• Created blue-green deployment for zero-downtime updates
• Established rollback mechanisms for quick recovery
• Built environment parity (dev, staging, production)

Monitoring & Observability:

• Implemented comprehensive logging and tracing
• Created custom dashboards for model performance
• Set up alerting for infrastructure and model issues
• Built cost monitoring and optimization
• Established on-call procedures and runbooks

Impact: Reduced deployment time from weeks to hours, eliminated production incidents caused by manual errors.

Phase 4: LLM Evaluation Framework

Developed specialized evaluation for Large Language Models:

Quality Assurance:

• Created evaluation datasets for medical use cases
• Implemented automated testing for response quality
• Built safety checks for medical misinformation
• Established human review workflows for edge cases
• Created feedback loops for continuous improvement

Performance Optimization:

• Benchmarked different LLM options for cost/quality tradeoffs
• Implemented prompt engineering best practices
• Built caching strategies to reduce costs
• Created fallback mechanisms for reliability
• Monitored token usage and costs

Impact: Improved model response quality, reduced LLM costs by 35%, and ensured consistent patient experience.

How It Works

The integrated system creates a robust AI development and deployment lifecycle:

1. Development: Data scientists develop models with access to clean, validated data
2. Evaluation: Automated testing runs comprehensive evaluations before deployment
3. Deployment: CI/CD pipeline deploys models with proper versioning and monitoring
4. Monitoring: Real-time dashboards track performance and alert on anomalies
5. Iteration: Feedback loops enable continuous improvement based on production data

Technical Architecture

Data Sources → ETL Pipeline → Data Warehouse
                                    ↓
            AI Models ← Training Data & Features
                 ↓
        Evaluation Framework → Test Suites
                 ↓
        Model Registry → Deployment Pipeline
                 ↓
        Production (Blue/Green) → Monitoring

Key technical decisions:

• Cloud-native architecture for scalability
• Infrastructure as code for reproducibility
• Automated testing at every stage
• Comprehensive monitoring and observability
• Security and compliance by design

Outcomes

Development Velocity:

• Reduced evaluation time by 40%
• Deployment time reduced from weeks to hours
• Enabled faster iteration on AI capabilities

Reliability:

• 99.9% platform uptime
• Zero production incidents from deployments
• Automated rollback for quick recovery

Performance:

• 60% faster data processing
• 35% reduction in LLM costs
• Improved model accuracy through systematic evaluation

Team Enablement:

• Self-service analytics for all teams
• Clear processes and best practices
• Improved collaboration between data and engineering teams

What the Client Said

"This transformation has been game-changing. We went from guessing about model performance to having complete visibility. Our team can now iterate quickly with confidence that we're improving patient outcomes."

— VP of Engineering

Tech Stack

• Data Pipeline: Apache Airflow, dbt, Python
• Data Warehouse: Snowflake/BigQuery
• ML Platform: MLflow, DVC for version control
• Cloud Infrastructure: AWS/GCP (Terraform for IaC)
• Monitoring: Datadog, custom evaluation dashboards
• CI/CD: GitHub Actions, automated testing frameworks
• LLM: GPT-4, Claude (with evaluation framework)

Key Learnings

1. Evaluation is critical: You can't improve what you don't measure systematically
2. Automation pays off: Manual processes don't scale and introduce errors
3. Platform thinking: Integrated systems are more powerful than point solutions
4. Observability matters: Understanding production behavior is essential
5. Culture shift: Moving from ad-hoc to systematic requires buy-in and training

Future Roadmap

The client is now expanding the platform to:

• Real-time model retraining based on production feedback
• Advanced experimentation framework for multivariate testing
• Federated learning across multiple data sources
• Automated model optimization and hyperparameter tuning

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This project took 20 weeks from kickoff to full production deployment, with ongoing support for optimization and new capabilities.