Conversation Graph Engine for AI
Turn flat message logs into explainable, navigable structures
When conversations drift, we branch 🌳
The Problem
AI conversations are stuck as flat lists. When topics diverge mid-conversation, context gets lost. Token limits grow to millions, but structure is still missing.
[09:15] User: Tell me about AI agents
[09:16] AI: Here's an overview...
[09:18] User: Actually, what about pricing?
[09:19] AI: Our pricing starts at...
[09:22] User: Back to agents, how do they work?
Topics naturally diverge
Mid-conversation topic shifts create confusion in flat message lists
Context gets lost
Long threads lose important context from earlier discussions
No temporal understanding
AI needs to remember weeks-old discussions with proper relevance decay
No explainability
Routing decisions are opaque with no deterministic behavior
As context windows grow to 1M+ tokens, you need structure, not just capacity
The Solution
Graph-native memory infrastructure that understands how conversations actually work
Automatic graph formation
Conversations branch naturally when topics diverge, maintaining context in each thread
Semantic memory with time decay
Understands relevance decay over time, prioritizing recent and important context
Dual-axis intelligence
Tracks both semantic content (what) and conversational dynamics (how)
Full observability
Deterministic, explainable, production-ready with complete audit trails
How It Works
Simple 4-step flow that transforms flat conversations into navigable graphs
Detect Drift
Dual-axis analysis detects when topics diverge OR conversational patterns shift
Analyze message content and conversation patterns to identify topic shifts
Branch Context
Automatic topic separation
Create new conversation branches when significant drift is detected
Cluster & Route
Route messages with temporal relevance - recent related topics stay accessible
Intelligently route messages considering temporal relevance
Merge & Reconnect
When topics reconverge
Automatically merge branches when conversations come back together
Sub-second orchestration with deterministic behavior and complete audit trails
Key Features
Built for production, designed for scale
Graph-Native Memory
- Transform linear logs into navigable graphs
- Every message knows where it belongs
- Temporal understanding of relevance
- Natural topic organization
Dual-Axis Intelligence
- Semantic drift: What you're discussing
- Functional drift: How you're discussing it
- Recognizes when you shift from asking questions to making decisions - even if the topic hasn't changed
- Branches that match human thinking
Production Infrastructure
- Sub-second orchestration
- Deterministic behavior
- Complete audit trails
- Enterprise-grade reliability
Use Cases
From startups to enterprise, DriftOS powers intelligent conversation management
AI Product Teams
Build AI assistants that remember what was discussed last week - not just what's in the last 10 messages
Agent Frameworks
Multi-agent coordination with graph-based memory enabling agents to collaborate without context collision
Messaging Platforms
Intelligent threading that automatically organizes conversations as topics naturally evolve and diverge
Enterprise AI
Compliance-ready audit trails with full explainability and deterministic behavior for regulated industries
Technical Highlights
Production-grade infrastructure built for reliability and scale
Status & Traction
Building the future of conversation memory infrastructure
Roadmap
Our journey to transform conversation infrastructure
Private Beta
Launch with design partners
- Closed beta program
- Design partner onboarding
- Performance optimization
Public API
General availability release
- Public API launch
- SDKs for major languages
- Comprehensive documentation
Enterprise SaaS
Full platform launch
- Enterprise features
- Advanced analytics
- White-label options
Multi-modal
Beyond text conversations
- Image support
- Audio processing
- Video understanding
Ready to give your AI real memory?
Join the waitlist for early access to DriftOS