Radically Efficient AI Agents
Our unified agent inference stack leaves bloated multi-agent frameworks behind. Experience deeper reasoning, intelligent tool use, and simple configuration with unmatched efficiency.
Introducing TIMThe Thread Inference Model
Our team at MIT designed our agentic inference engine, TIM, from the kernel up to tackle multi-step agentic tasks with significantly improved memory efficiency, faster inference, and support for a virtually unlimited context window.
We've found it's an incredibly effective system for many use cases, including:
AI Search
Multi-step reasoning across vast knowledge bases with intelligent context management and memory efficiency.
Complex Workflows
Automated subtask generation and completion with intelligent context pruning for enterprise workflows.
Research & Analysis
Deep multi-step reasoning across documents and data sources with persistent memory management.
Enterprise Knowledge Q&A
Intelligent query processing across organizational knowledge bases with contextual understanding and memory retention.
Legal Discovery & Review
Comprehensive document analysis with persistent memory for complex legal reasoning and cross-reference validation.
Genomic Data Analysis
Multi-step genomic sequence analysis with unlimited context for complex bioinformatics workflows and pattern recognition.
TIM enables breakthrough performance in agentic use cases that require sustained reasoning and memory.
Be the First to Try It
Be among the first to access our capable and efficient AI agent inference stack.