The platform
Nebbos watches the work your organisation already does and turns it into foresight — predicting where things break, explaining why, and acting under your oversight. It all runs on one thing: the Operational Graph.
The mechanism
Underneath every answer is a living map of how your organisation actually operates — who depends on whom, how work moves between teams, where it tends to stall. Nebbos builds it from the events your tools already emit, keeps it current as the work changes, and reasons over it.
It's a temporal map — it holds not just how things stand today but how they got there, so Nebbos can see what's shifting, not only what is. And it stores structured signal — patterns, thresholds, relationships — never the raw contents of your messages. That's what makes its predictions specific to you, not generic.
End to end
Connectors read the events your tools already produce — tasks, messages, calendars, tickets, handoffs — and normalise them into one operational stream. Connect the stack you run; there's nothing new for teams to adopt.
Deterministic detectors run continuously and cost nothing to watch. Only when one fires does Nebbos spend a model call to reason about it — so prediction is both cheap and sharp.
Completion gap against capacity and a stalling velocity.
Overlapping deadline pressure across a dependency.
A sharp week-on-week fall with deadlines still live.
A cross-team handoff sitting past its threshold.
No activity where there should be, no leave on record.
One team's commitment that a dependent team can't meet.
A Pearl — one per department — works through the firing pattern against the graph and returns the cause in plain language with the evidence attached. Each prediction carries a confidence score and a "why" you can open.
Pearl proposes the next best action. Anything consequential pauses for a human checkpoint — autonomy is earned over time, bounded to what's been proven, and always reversible.
Every prediction, resolution and correction is written back to the Operational Graph. The system you run in month 24 has been shaped entirely around how your organisation works.
Spends tokens where they matter
Most AI products spend a model call on everything and hope the bill stays reasonable. Nebbos is built the other way around. The six deterministic detectors run continuously and cost nothing to watch — most of what happens never touches a model. Only when a pattern fires does Nebbos reason, and governance-tiered routing sends that reasoning to the right model at the right price. Swap your model next quarter; nothing downstream breaks.
Detection is cheap math, not inference. A model call is the exception, never the default.
Heavy reasoning goes to a capable model; routine work to a cheaper one. You set the policy.
Works across LLMs and providers — no vendor lock-in, no single bill holding you hostage.
Measured token reduction vs a model-call-per-event baseline: [ figure to be supplied from engineering, scoped to workload + baseline ].
Before it acts
When Nebbos proposes a consequential move, it can fork your current operational state, run the action forward against that copy, and check the result — in a sandbox that never touches the real thing. You see the predicted outcome before you approve. If the rehearsal looks wrong, nothing ever happened.
Built for people and agents
A clean view of what's happening, what's predicted, and the few decisions only you can make — with the reasoning one click away.
The same intelligence is available to your own agents and tools over a standard interface — so Nebbos's signal can drive workflows you already run.
Connect your stack and your first Pearl starts watching. Predictions begin as soon as there's enough signal.