The Large Behavior Model.
A pretrained AI model of how organizations actually work. It reads approved work metadata and answers the questions your org chart cannot: who owns this, who has capacity, which document is the truth, where risk is forming.
Agents are smart. They just don't know the building.
The context gap
Companies are putting AI agents into more workflows, but agents still lack the context human operators use every day: who knows what, who is trusted, who has capacity, who owns the latest answer, and where risk is forming.
Rebuilt manually, everywhere
Today that context is reconstructed by hand in every company and every workflow: routing tables, escalation configs, tribal knowledge. It breaks every reorg.
Work behavior can be modeled
Work events form sequences, and a model can learn from them the way a language model learns from sequences of words. With enough breadth, it learns patterns that carry over to new companies.
The deployment path. Ordinary collaboration data becomes a first map of how work moves; existing network analysis and peer feedback make the results more precise but are not required to begin.
Six things simple activity counts miss
Not a social graph. Not a productivity score. A model of work behavior that adjusts for role and activity level, so the busiest or most connected person is not mistaken for the expert.
Expertise
Who gets pulled into tickets, incidents, and design reviews when the team needs the answer that unblocks work.
Influence
Whose involvement actually moves a decision: when they weigh in, things get built, delayed, or rolled back.
Source-of-truth
Which runbook, doc, or ticket people actually open before shipping, debugging, or answering customers.
Disengagement & attrition
Who stops reviewing, misses standups, skips planning, or quietly hands off work, months before they formally leave.
Agent routing
Where an agent should send a failed deploy, an unclear ticket, a support escalation, or a question no doc answers.
Integration risk
Which team, system, or single person quietly holds a migration, reorg, or acquisition plan together.
One model of work behavior, many uses. Each use case reads the same model; validation includes new-company tests, public external data, and controlled simulation.
Validated against real outcomes
Every result is tested against simpler methods and checked to make sure the answer does not come from accidental clues in the data.
| Capability | Result | Why it matters |
|---|---|---|
| Hidden influence: sway beyond the title | 2.12x better than chance | Surfaces the people whose real sway their title does not explain: risk, compliance, and diligence |
| Works on a company it has never seen | r ≈ 0.50, about 0.25 after adjustment | The model keeps working on a new company without custom training |
| Attrition early warning: who may leave | 0.94 AUC | Flags likely departures months before standard HR reports |
| Same model, flat or steep org | 0.94 in both org shapes | Steep hierarchy to flat org, with no extra tuning |
| Which document is the truth | 0.88 AUC | Stops people and agents using stale knowledge |
| Quiet disengagement, caught early | 0.83 AUC | Detects withdrawal before it becomes binary |
| Who can cover a role | 3.2x better than chance | Succession planning and operating resilience |
About 517,000 emails, roughly 170 mapped employees, 1999 to 2002, where who held power and who was prosecuted is court record. Reading communication patterns alone, without being told who was later charged or convicted, the model:
- Ranks the indicted insiders at more than twice the rate chance would produce, without being told any outcome.
- Detects the trading desk hardening into an insular ring through the California energy crisis, then dissolving afterward.
- Surfaces internal code names tied to the fraud about a year before public exposure, clustered around the employees later convicted.
A modern all-digital workplace leaves a far richer signal than phone-era email. For risk, compliance, and diligence teams, this is an early warning system that reads collusion and shadow authority from structure alone.
Tested on a real technology company of about 900 people the model had never trained on: fast-moving, socially driven, project-based, under heavy delivery pressure. It read only the company's permissioned calendars and Slack. From those two signals alone, the model rebuilt the company's operating map:
- About 70 go-to experts the company truly relies on, plus about 100 hidden experts, genuinely relied on, with titles that understate their real role, that a title chart alone would miss.
- About 35 key-person risks whose exit would break a live workflow, concentrated in senior specialists rather than junior staff.
- Backup depth: about 70% of people have a peer who could step in, but about 25 critical roles have no real backup, and roughly a quarter of the priority projects depend on a single person.
- About 85 cross-team bridges holding otherwise separate groups together, who also carried the highest overload and burnout risk, and about 7 clusters at risk of becoming isolated silos.
Two checks compared the model's answers with facts it was never shown: job function, identified from work patterns alone, and departures, predicted from past calendar behavior and confirmed against independent external records. No surveys, no documents, no answer sheets handed in.
One model. Every use case is a product.
Connect permissioned data once, evaluate the model on your own organization, then use any capability through the REST API or expose it to your agents over MCP.
Agent routing
Send a task, question, or exception to the person who actually knows it and has capacity.
Workforce-risk monitoring
Likely departures and disengagement, months before they show up in HR.
Knowledge governance
Which document is the source of truth and who owns it.
Change management
Who actually drives or blocks a rollout.
Compliance & risk
Shadow authority and collusion patterns read from structure alone.
M&A diligence
How work actually moves beneath the org chart: integration risk and knowledge continuity.
Evaluate it on your own organization
Start with the free evaluation: connect calendar metadata, get one full diagnosis back over MCP, judge the results before you pay anything. The full design brief and validation protocol are available for technical diligence.
Start the evaluation See pricing