Organizations run on context.
Most software destroys it.
Daat reconstructs how a company actually operates from fragmented signals.
Context infrastructure for AI systems.
See how it worksTwo people hash out a decision on Slack.
Both file it differently in Notion.
Three weeks later an agent has no idea it happened.
That's not a tool problem. That's a context problem.
Every company runs on shared understanding that lives in conversations, decisions, and relationships. Not in any single system. Software captures fragments. Nobody captures the whole.
When AI agents operate on top of that fragmentation, they're fast but blind.
The next shift in AI isn't better models.
It's giving those models access to what's actually happening.
Context is the missing layer. Without it, agents can execute but they can't understand. With it, the entire stack changes.
That's the gap Daat fills.
How it works
Fragmented signals
This is how companies actually operate. Not in systems. In conversations, decisions, and moments that scatter across every tool and disappear.
hey can someone update the Q2 forecast? jake said the enterprise deal finally closed
Decision: moving to monthly billing cycle starting May. Owner: Sarah. Approved by leadership.
Weekly sync, product + eng, recurring, Thursdays 10am
Invoice #1042 · Acme Corp · $24,000 · Overdue 14 days
Comprehension layer
One signal at a time. No assumptions across sources. No organizational claims without evidence. The system extracts only what is present, preserves what is uncertain, and refuses to flatten what it does not fully understand.
Structured context
Not a summary. Not a dashboard. A faithful reconstruction of what happened, who was involved, what was decided, and what it means. In a form any system can reason with.
This is the layer that lets AI move with how a company actually operates. Not more intelligence. The context to use it.
What exists today
The ingestion layer
- Slack, Notion, QuickBooks, Google Workspace connected
- Every raw signal stored untouched with full provenance
- Source-specific metadata preserved in dedicated tables
- Nothing normalized, nothing lost
The comprehension layer
- Each signal processed independently by Claude
- Extracts workflows, decisions, people, tools, and states
- Every extraction grounded back to the source signal
- Uncertainty preserved explicitly
- Null over weak inference
These two layers are built and running across Slack, Notion, QuickBooks, and Google Workspace. What comes next is the harder problem: building a living map of how a company actually operates from thousands of individual signal extractions, and exposing that map as something other systems can reason with.
The direction is infrastructure, not interface. A context layer that sits underneath every tool, every agent, every workflow, quietly building a living map of how a company actually operates. Queryable. Reliable. Always current. The closer analogy is Stripe than Notion. You import it. It works. You stop thinking about it.
We are building toward an API that any system can call to understand what is happening inside a company, without anyone having to maintain it manually. That is the product. That is where this is going.