Applied AI Research Lab
Architecting cognition.
Accelerating R&D.
We build AI systems that augment human intelligence—compressing research cycles, expanding the scope of tractable problems, and enabling breakthroughs in science and engineering.
Our approach
Systems that turn capability into work
Frontier models are improving quickly, but most real failures are not "the model can't think" failures. They are systems failures: loss of state, brittle tool use, no verification, and humans forced into synchronous babysitting.
We build architectures where models sit inside an operating loop: persistent memory, tool runtimes, autonomous execution, and verifiable decisions. Each run leaves the system more capable than before.
Persistent state
Survives across sessions, tasks, and users
Tool-native execution
Work in runtimes, not chat transcripts
Verifiable decisions
Replayable runs, provenance, early error catching
Learning through use
Improves from interaction, not just retrains
Each cycle compounds state, knowledge, and capability
Decision Engine
Provenance
Agents
Monad
Memory
Engram-Locus
Models
Engram-VQ
Hardware
Custom compute
What we build
Full-stack intelligence
We build the full stack because the biggest gains are emergent: hardware affects latency, latency affects agent design, agent design affects memory needs, memory affects verification. We design the interfaces so the system improves with use.
Replay, provenance, policy, safe delegation
Orchestration, delegation, async execution, tool routing
Long-horizon state, structured knowledge, retrieval with decay
Specialised reasoning, retrieval, synthesis modules
Predictable latency, cost control, HW-aware serving
Monad
Accelerate research,
not just tasks
Research velocity is bottlenecked by cognitive overhead—context switching, information retrieval, experiment management. Monad removes that friction, letting you focus on the work that matters.
Monad Workbench
Your R&D command center
Orchestrate research across domains from a single interface. Spin up compute, pull in literature with relevance scoring, run experiments in parallel, and let background agents keep your knowledge base current. Everything connects—documents, experiments, and insights form a living knowledge graph.
Memory-augmented architectures show 3.2x improvement on multi-hop reasoning tasks. This conflicts with prior assumption in hypothesis.md line 47.
Deep Research
Source validation with relevance and novelty scoring—surfaces what matters, filters noise
Headless Runtime
Run Monad without the UI, integrate into your stack, or connect to existing products via API
Parallel Compute
Spin up virtual environments, provision GPUs, run multiple tasks concurrently
Autonomous Agents
Background processes for knowledge updates, experiment monitoring, and continuous research
Our Products
Specialized systems, orchestrated together
We deploy a garden of purpose-built systems—agent runtimes, memory architectures, and specialized models—each optimized for specific tasks, working together to solve problems no single model could.
GOVERNANCE & AUDIT
Decision provenance, traceability, and audit infrastructure for regulated environments
Decision lineage, exception monitoring, and governance records
Multi-source reconciliation with conflict detection
AGENT SYSTEMS
Orchestration and execution frameworks for complex multi-step tasks
R&D workbench with deep research and background agents
Full research assistant with tool orchestration
MEMORY SYSTEMS
Persistent state management across sessions and contexts
Hierarchical episodic memory with surprise-gated consolidation
Versioned environment tracking with rollback
SPECIALIZED MODELS
Purpose-built models for specific reasoning and retrieval tasks
Recurrent model with fast/slow memory
Cross-document synthesis
Applied domains
Where intelligence is tested
Build with us
Deploy our systems, collaborate on research, or join the team.