Live demo available
Linear context replay causes silent context corruption. DriftOS solves this.
How It Works
Four steps from message to optimal context
1
Detect
Dual-axis drift detection
- Semantic drift via embedding similarity
- Functional drift via conversational mode analysis
- Implicit anaphora detection (6 categories)
- OOV compound reference detection
- All local - spaCy + transformers.js
2
Route
BRANCH / STAY / ROUTE
- Every message gets a routing decision
- <100ms latency, no LLM calls
- Configurable thresholds with hysteresis
- Refractory periods prevent oscillation
3
Assemble
Build optimal context
- Walk the graph, don't dump history
- Current branch + related clusters
- Tail routes for cross-branch context
- 20 relevant messages, not 1000
4
Explain
Full audit trail
- Every decision logged with reasons
- Similarity scores + confidence values
- Boost multipliers with explanations
- Built for compliance requirements
Quick Start
Get started with a few lines of code
Under the Hood
Real engineering, not just marketing
Use Cases
From chatbots to enterprise AI
AI Assistants
Users switch topics mid-conversation. Your bot forgets everything.
Agent Orchestration
Agent #3 doesn't know what Agent #1 decided. Context lost.
Messaging Apps
Scroll through 200 messages to find that one decision? Never again.
Enterprise AI
Auditor asks why the AI said that. You shrug.
Open Source
Core routing and embedding functionality available under MIT license
Enterprise features (advanced NLP, compliance tools) available separately
Get in Touch
Interested in DriftOS for your project? Let us know.