Enterprise Knowledge Distillation
Connect raw Jira, Slack, and internal PDFs to our pipeline to generate hyper-compressed context files for agent consumption.
1. Solving the Context Window Trap
Large language models cannot natively ingest 10 years of Slack messages, fragmented Jira tickets, and dusty Confluence wikis without hallucinating or running out of context. Instead of just passing raw text to the models, we distill it.
2. The Distillation Pipeline
Our pipeline hooks into your company's data streams, parses the raw human communication, and strips away the noise. It uses a tiered multi-model ensemble to compress sprawling comment threads into dense, factual summaries of architectural decisions.
3. Structured Knowledge Graphs
The compressed data is then mapped into structured, bidirectional knowledge graphs. This allows agents to understand not just what the codebase looks like today, but the historical context of why specific decisions were made three years ago.
4. The .mem Standard
These outputs are stored in our open-source .mem file format. These files act as the persistent, localized brain for your agent swarms, ensuring every AI has instant, hallucination-free recall of your company's single source of truth.