AI Research at the
Edge of What's Possible
Our research team advances the state of the art in agentic systems, sovereign deployment, and enterprise-scale AI infrastructure. Published findings drive every system we build.
Research Focus Areas
Agentic Systems
Multi-agent planning, coordination protocols, tool use, and long-horizon task execution.
Sovereign Deployment
Air-gapped inference, on-premise serving, model weight security, and data isolation.
Retrieval Architecture
Hybrid dense-sparse RAG, dynamic chunking, citation grounding, and hallucination reduction.
Memory Systems
Episodic, semantic, and procedural memory for persistent agent continuity across sessions.
Inference Optimization
Quantization, speculative decoding, batching strategies, and hardware-aware compilation.
AI Security
Adversarial robustness, prompt injection defense, red-teaming methodologies, and audit systems.
Publications & Reports
Hierarchical Multi-Agent Planning for Long-Horizon Enterprise Tasks
We introduce a novel planning architecture enabling agent networks to decompose and autonomously execute complex multi-step workflows across distributed enterprise systems with minimal human oversight.
NexusRAG: Sovereign Retrieval-Augmented Generation at Enterprise Scale
An open framework for deploying RAG pipelines entirely on-premise, with adaptive chunking, hybrid dense-sparse retrieval, and hallucination mitigation designed for regulated industry environments.
Evaluating Local LLM Inference: Latency, Quality, and Cost at 7B–70B Scale
A comprehensive evaluation framework measuring performance tradeoffs of quantized local models across hardware configurations from edge devices to full data center deployments.
Memory Architecture for Persistent Agentic Systems in Production Environments
Exploring episodic, semantic, and procedural memory designs that give AI agents genuine continuity across sessions, enabling autonomous operation over extended real-world timeframes.
Speculative Decoding for On-Premise LLM Serving: A Practical Guide
Detailed implementation guide for applying speculative decoding with draft models to reduce inference latency by up to 3x without quality degradation on quantized local models.
The Case for AI Sovereignty: Why Enterprise Data Must Never Leave the Perimeter
A strategic and technical argument for why the next wave of enterprise AI adoption requires complete organizational ownership of the intelligence layer, from weights to runtime.
Research With Us
We're looking for researchers passionate about agentic systems, inference optimization, and the frontier of autonomous AI.
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