Overview
HANA’s Adaptive Reasoning Models represent a breakthrough in large-scale inference optimization and real-time clinical decision support. Built on our service architecture, these models dynamically adjust their reasoning processes based on clinical context, computational constraints, and performance requirements. The system leverages multi-tiered processing, candidate generation and verification, and self-calibration to deliver high performance while maintaining accuracy and reliability in production voice conversations.Core Architecture
Multi-Tiered Processing System
HANA’s adaptive reasoning system employs a sophisticated multi-tiered architecture designed for real-time clinical analysis and decision support: Processing Intervals:- Immediate checks (< 500ms): Response validation, entity grounding, safety filter — runs during every conversation turn
- Turn-level checks (1-3 seconds): Context coherence, goal tracking, sentiment analysis — runs between conversation turns
- Post-call analysis (async): Full transcript review, clinical accuracy audit, protocol compliance, quality scoring
- Support for thousands of concurrent streaming voice sessions
- CPU-optimized conversation engine for low-latency response generation
- GPU-accelerated reasoning engine for pre-call planning and complex clinical analysis
- Memory-efficient design with minimal RAM per active conversation session
Candidate Generation and Verification
HANA’s conversation engine uses a generate-and-verify architecture for response selection:- A fast, lightweight model generates candidate responses for the next conversation turn
- The reasoning engine evaluates and ranks candidates against clinical protocol constraints, entity grounding, and safety rules
- The highest-scoring candidate is selected and delivered via streaming voice synthesis
- Self-calibration adjusts selection weights during runtime based on conversation context
- Fast model generates multiple candidate responses based on current conversation state
- Each candidate is verified against: protocol constraints, entity grounding, safety rules, and clinical appropriateness
- Optimal candidate selected and delivered via streaming voice synthesis
- Verification results feed back into calibration for future selections
Infrastructure & Cost Analysis
Current Usage & Projections
Baseline Costs:- Current LLM API usage for reasoning engine: variable based on conversation volume
- Voice synthesis and recognition: per-minute telephony and processing costs
- Infrastructure: cloud compute, storage, and networking
- Optimized model routing reduces inference costs by 40-60% compared to single-model approaches
- Cached reasoning plans for common patient profiles reduce redundant computation
- Auto-scaling to zero during low-volume periods eliminates idle compute waste
Hardware Configuration
Production Setup:- Kubernetes clusters with auto-scaling node pools
- GPU nodes for reasoning engine inference (pre-call planning, post-call analysis)
- CPU-optimized nodes for real-time conversation engine (latency-critical path)
- Dedicated telephony infrastructure with geographic redundancy
Model Performance & Optimization
Overview
In the field of compute optimization, model routing, caching strategies, and batch processing optimization are crucial for enhancing performance. For cost optimization, intelligent complexity-based routing, reasoning plan reuse, and resource sharing across concurrent sessions are essential to efficiently manage resources and reduce expenses.Technical Implementation
Memory Management:- Conversation state maintained per active session with minimal memory footprint
- EHR data cached with configurable TTL to reduce redundant API calls
- Reasoning plans stored and reused for similar patient profiles
- Efficient embedding storage and retrieval for patient context
- Dynamic model routing based on conversation complexity (simple → fast model, complex → full reasoning pipeline)
- Protocol-specific optimization: pre-computed conversation plans for common clinical workflows
- Streaming voice synthesis begins before full response is composed (parallel processing)
Performance Metrics
Conversation Engine:- Response latency: < 800ms p95 (time from patient speech end to HANA speech start)
- Concurrent sessions: scales horizontally based on demand
- Voice synthesis quality: natural conversational pacing with dynamic prosody
- Pre-call plan generation: < 3 seconds per patient
- Post-call analysis: < 30 seconds per conversation
- Clinical entity extraction accuracy: > 97% (measured across internal evaluation datasets; validated per protocol during onboarding)
- Protocol compliance rate: > 97% (measured via automated post-call evaluation pipeline)
Performance Optimization Strategies
Adaptive Processing: Dynamic Resource Allocation:- Automatic scaling based on call volume and time-of-day patterns
- Context-aware compute allocation (complex protocols get more reasoning resources)
- Priority-based processing queues for urgent clinical conversations
- Lightweight models for simple conversation turns (confirmations, acknowledgments)
- Full reasoning pipeline for clinical branching decisions and entity extraction
- Specialized models for assessment instrument scoring (PHQ-9, GAD-7, etc.)
- Optimized inference for specific clinical conversation patterns
- Pre-computed response segments for standard protocol language
- Batch processing for post-call analysis across multiple conversations
- Intelligent model routing based on complexity requirements
- Caching strategies for repeated reasoning patterns (same protocol + similar patient profile)
- Resource sharing across concurrent sessions with priority queuing
Data Evaluation & Quality Assurance
Evaluation Framework
Dataset Management:- Comprehensive evaluation dataset of scored production conversations, growing continuously
- Data cleaning removes invalid or incomplete entries before evaluation
- Continuous evaluation with systematic processing for quality stability
- LLM-as-judge integration for automated quality assessment
- Reference-free evaluations without requiring golden standard transcripts
- Separate evaluation tracks for high-quality examples and failure mode identification
- Auto-optimization system to improve quality scores over time
- Continuous feedback loop for model and protocol improvement
- Performance tracking across different clinical protocols and patient demographics