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.
The Engelbart Thesis
Augmenting human intelligence
In 1962, Douglas Engelbart proposed that the most important goal of computing was not automation, but augmentation—increasing humanity's capability to approach complex problems.
Engram exists to build on this thesis. We believe the next step toward more capable AI is not a single breakthrough model, but a shift in architecture—systems that remember, reason, and learn over time.
The research loop: each cycle faster, each iteration more informed
Who We Are
An applied AI research lab
We build full-stack intelligence systems—integrated architectures that combine models, memory, reasoning, and execution into coherent systems that operate over time.
Model scaling is converging. Frontier models are narrowing in capability, and open-weight alternatives are closing the gap. The limiting factor has shifted to how models are used: how they reason, how they remember, how they interact with tools, and how they improve through use. Engram focuses on that systems layer.
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
Architecture
Systems, not models
Intelligence emerges from architecture, not scale alone. We build layered systems where each component serves a distinct function—and where the whole is greater than the sum of its parts.
Environment
World state tracking with versioned history and constraint awareness
Memory
Three-tier hierarchy: working context, episodic traces, semantic knowledge
Reasoning
Iterative hypothesis generation, refinement, and verification
Execution
Tool orchestration with full audit trails and rollback capability
Information flows down, learning flows up
Our Products
Orchestration over scale
Rather than a single monolithic model, Engram deploys a garden of specialized systems—each optimized for specific tasks, orchestrated together to solve problems no individual model could.
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
Real-world deployment across demanding verticals provides the feedback that drives our research. Each domain shapes our systems—and our systems shape how work gets done.
Clinical Research
Protocol analysis, systematic review, regulatory documentation
Work with us
Whether you're looking to deploy our systems, collaborate on research, or join the team—we'd like to hear from you.
Architecting cognition.