Building the Memory Layer
The technical foundations for intelligent assistance: persistent memory systems, multi-agent orchestration, and ultra-efficient inference. Three research pillars. One goal: make AI that actually works at enterprise scale.
Research Pillar
Deep Memory Architecture
Research Pillar
Concurrent Transaction Orchestration
Research Pillars
Deep Memory Architecture
Building AI systems with persistent, contextual memory that learn and adapt over time. Research into knowledge representation, retrieval, and long-term context maintenance.
Intelligent Orchestration
Agentic systems that coordinate multiple tasks intelligently. Research into multi-agent collaboration, task planning, and autonomous decision-making under uncertainty.
Ultra-Efficient Optimization
Making AI inference dramatically more cost-effective through model compression, quantization, and architectural innovations. Research into efficiency without sacrificing capability.
Research Principles
Publish Open, Ship Fast
Research findings go public. Production systems ship to users within weeks, not years. Transparency builds trust. Speed compounds advantage.
Solve for Scale from Day One
Prototypes that work on 10 users fail at 10,000. We design for enterprise scale—latency, cost, reliability—before writing the first line of code.
Optimize for Cost, Not Just Performance
A model that's 2% more accurate but 10x more expensive isn't progress. We target dramatic efficiency gains—10-100x cost reduction—so economics actually work.
Measure Impact in Real Workflows
Benchmarks lie. User workflows tell the truth. Does memory retrieval actually improve task completion? Does orchestration reduce context-switching overhead? That's what we measure.
Publications
Groundbreaking research in progress
Our research team is hard at work. Publications coming soon.