Engram
Engram builds full-stack intelligence systems that combine custom models, persistent memory, and agent orchestration to accelerate research and engineering workflows. Its platform includes purpose-built models like Engram-VQ and Engram-SQL, a unified API with sub-100ms latency, and tools for deep research, provenance tracking, and autonomous background agents. The company emphasizes verifiable, auditable AI decisions through replayable runs and lineage tracing.
- Artificial Intelligence
- AI Agents
- Data & Analytics
- Developer Tools
- Enterprise Software
- Hardware
- Software Only
Funding
Founders
Product
Problem
Research and engineering organizations face slow, opaque AI systems that struggle with long-horizon reasoning, lack persistent memory, and produce unverifiable outputs. This limits R&D throughput, makes auditability difficult, and prevents reliable deployment in complex, real-world environments.
Solution
Engram provides an integrated full-stack AI platform that combines custom hardware, specialized models, persistent memory, and agent orchestration into a coherent system. The platform includes purpose-built models like Engram-VQ for multi-step reasoning and Engram-SQL for schema-aware query generation, all accessible via a unified API. It features a Monad agent runtime for autonomous task execution, an Engram-Locus memory system for long-horizon state management, and a Decision Engine that ensures provenance and deterministic replay. This architecture compresses research cycles by enabling parallel compute, deep research with relevance scoring, and background agents that continuously update knowledge bases.
Target Audience
Primary customers are research institutions, engineering teams, and regulated enterprises that need reliable, auditable AI systems for complex reasoning, document synthesis, and domain-specific applications.
Features
- Engram-VQ recurrent model with fast/slow memory and surprise-gated meta-gating, achieving 78.2% MMLU and 82.4% GSM8K
- Engram-SQL natural language to SQL model with schema awareness, scoring 79.1% on Spider and 58.3% on BIRD benchmarks
- Multi-Doc Reasoner supporting up to 32 documents simultaneously with automatic citation tracking and conflict resolution
- Monad agent runtime for orchestration, delegation, and async execution with headless API access
- Decision Engine providing provenance, deterministic replay, and lineage tracing for auditable AI decisions
- Engram-Locus memory system with hierarchical episodic memory and surprise-gated consolidation for persistent state
- Unified API with sub-100ms p95 latency, SDKs for Python, TypeScript, and Go, and self-hosted deployment options
- Specialized embedding models for clinical (PubMedQA: 78.3%) and cybersecurity (CyberBench: 81.2%) domains