Input 01
Human knowledge
Customer conversations, supplier warnings, store observations and planning judgment.
Context Intelligence
Turn new information into what models can use.
People, business systems and connected agents supply evidence. RabbitHawk structures it as uncertain beliefs that forecasting and optimization models can use.
Evidence beyond structured history
Sources of new context
Input 02
Connected business signals
Approved email, documents, internal systems, calendars and operational updates.
Input 03
Agent-detected external signals
News, public notices, disruptions and market events linked to the relevant business scope.
Input 04
Product intelligence
Descriptions, imagery, attributes, analogues, audience hypotheses, and sparse-history evidence.
The shared engine
From evidence to outcome
01 / Listen
Receive natural-language knowledge, documents, observations, and structured signals.
02 / Ask
Resolve ambiguity with questions chosen for their expected information value.
03 / Formalize
Classify the planning object and represent valid evidence as a versioned, uncertain belief with scope, timing, and provenance.
04 / Compute
Use statistical inference, forecasting, and optimization models, not the language model, to update beliefs, estimate possible futures, and calculate decisions.
05 / Govern
Preserve versions, assumptions, approvals, rationales, and the option to make no change.
06 / Learn
Compare beliefs, forecasts, decisions, and outcomes before proposing governed updates to models and policies.
Semantic governance
Change the right planning object, not whichever number is easiest to edit
“We need more revenue,” “the plant can make 5,000 units,” and “the customer plans to buy less” belong to different planning objects.
- Expected-demand evidence
- Form a contextual prior and selectively reforecast when the evidence is material.
- Target or objective
- Keep expected demand unchanged; evaluate the target as a separate planning object.
- Commitment
- Record the promise, its source, and reliability without treating it as certain demand.
- Constraint
- Apply it to the constrained plan or optimizer, not unconstrained expected demand.
- Scenario
- Explore a possible state without silently publishing it as the forecast.
- Policy or optimization input
- Route it to the relevant decision model and preserve its decision lineage.
- Data correction
- Correct the underlying record through a governed data-quality workflow.
- Duplicate, weak, or immaterial signal
- Record, monitor, or preserve the current forecast.
Not every new signal deserves a new forecast.
The technical boundary
Language intelligence and numerical intelligence do different work
Language AI
Interprets, classifies, retrieves, asks, structures, and explains. It exposes evidence, assumptions, ambiguity, and missing information.
Forecasting and optimization models
Estimate priors, posteriors and predictive distributions, reconcile forecasts, and optimize decisions. The language model does not invent the final forecast.
One engine, two products
Context Intelligence works inside both RabbitHawk products
Forecasting & Optimization
Context informs the forecast, scenarios, goals, constraints, and optimizers inside a RabbitHawk decision loop.
Explore the productForecast Interventions
Context is evaluated against an imported baseline before RabbitHawk selectively reforecasts and governs publication.
Explore the productShow us what changes between forecast runs
Bring the new information people act on and the workflow it moves through.
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