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Production-Grade Therapeutic AI Platform

A HIPAA-compatible AI mental health platform with multi-phase therapeutic agents, real-time crisis detection, and complete data sovereignty deployed to client's AWS infrastructure.

~1.9s
First Token Response
<850ms
Crisis Detection
90%
Cost Reduction

The Challenge

Critical Requirements:

  • HIPAA Compliance: Healthcare data requires strict privacy and security controls
  • Real-Time Crisis Detection: Must identify suicidal ideation in <1 second
  • Multi-Phase Therapy: Adapt conversation style based on user's emotional state (Vent → Reflect → Reframe)
  • Low Latency: Streaming responses must feel natural (<2s first token)
  • Data Sovereignty: Patient data must stay within controlled environment

Our Solution

We built a production-grade therapeutic AI platform deployed entirely to the client's AWS infrastructure. The system uses a hybrid critical path architecture that separates blocking operations (crisis detection, phase transitions) from non-blocking background analysis (emotion detection, pattern recognition).

Technical Architecture

Multi-Phase Agent System

  • • Vent Phase: Empathetic listening, validation
  • • Reflect Phase: Pattern recognition, awareness
  • • Reframe Phase: Perspective shift, alternatives
  • • Automatic phase transition detection

Two-Tier Crisis Detection

  • • Tier 1: Regex patterns (<50ms)
  • • Tier 2: LLM contextual analysis (~800ms)
  • • 35+ crisis patterns (suicide, self-harm)
  • • Blocks normal flow, provides resources

Hybrid Prompt Handler

  • • Critical Path: Category + Phase (blocks)
  • • Background: Emotions + Patterns (parallel)
  • • 38% latency improvement
  • • Non-blocking enrichment

Dual Storage Architecture

  • • Redis: Session state (24hr TTL, <1ms)
  • • DynamoDB: Conversation history (permanent)
  • • Pattern tracking across conversations
  • • Fire-and-forget writes (non-blocking)

Technology Stack

Core Framework

  • Python 3.12 + FastAPI (async/await)
  • LangChain (agent orchestration)
  • Pydantic (type-safe schemas)

LLM Providers

  • Anthropic Claude (Haiku 4.5, Sonnet 4.5)
  • AWS Bedrock (failover)
  • Prompt caching (90% cost reduction)

Storage (Client AWS)

  • Redis (ElastiCache) - Session state
  • DynamoDB - Conversation history
  • All data stays in client AWS account

Key Features

  • Server-Sent Events (SSE) streaming
  • CBT pattern recognition (10 distortions)
  • Multi-thread conversation support

Results & Impact

~1.9s
First Token Response
Under 2s target for natural feel
<850ms
Crisis Detection
Under 1s target, life-saving speed
100%
Function Calling Success
Structured output reliability

Key Achievements

90% Cost Reduction Through Prompt Caching

Reduced monthly costs from $500 to ~$50 (at 1,000 daily users) by caching system prompts. 90% hit rate on production workloads.

38% Latency Improvement with Hybrid Architecture

Reduced Time to First Byte from 7.5s to 4.6s by running background analysis (emotions, patterns) in parallel while streaming therapeutic response.

Complete Data Sovereignty & HIPAA Compatibility

All patient data stored in client's AWS infrastructure (Redis ElastiCache + DynamoDB). Zero data leakage. Ready for HIPAA compliance audits.

Intelligent Multi-Phase Therapy System

Automatically adapts conversation style based on user's emotional state. Detects phase transitions (Vent → Reflect → Reframe) with 75% confidence threshold.

CBT Pattern Tracking Across Conversations

Detects 10 cognitive distortions (catastrophizing, mind reading, etc.) and tracks frequency over time. Helps users recognize recurring patterns.

Data Sovereignty: The Critical Differentiator

Unlike SaaS mental health platforms that store patient data on shared infrastructure, this platform is deployed entirely within the client's AWS account. This means:

  • Patient data never leaves their environment - stored in their Redis + DynamoDB
  • HIPAA compliance ready - no third-party data sharing concerns
  • Client owns all infrastructure - full control over data retention and deletion
  • No vendor lock-in - they control the entire system in their AWS account

Need AI Systems Built on Your Infrastructure?

We build production-grade AI solutions deployed entirely to your AWS account, GCP project, or Azure subscription. Your data stays yours.

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