The AI engine that powers our context layer
Attribution, propensity, next-best action, and forecasting, all computed on one model, on one context layer. This is the intelligence the agents take their next move from.
Your stack finally speaks one language. The Learning Engine turns that into an edge: it reconciles every attribution model into a single number, ranks every buyer and account by who's actually going to close, tells your team the next move before they have to guess, triggers agents to execute and builds a forecast that survives the board room, with the plays to hit it.
Attribution wars ended
MTA, MMX, and incrementality reconciled · because they sit on the same context layer.
Multi-touch attribution
Stage-weighted, identity-resolved · the contribution of every touch across the journey, not just first or last.
Marketing mix modeling
Saturation curves and marginal ROI per channel · time-series-aware, region-aware, refit weekly.
Incrementality testing
Geo holdouts, ghost-bid auctions, p-values you can take to the CFO.
One revenue model
No more marketing says one thing, finance says another. Every team defends the same number.
Propensity, explainable
Every buyer, account, and opportunity ranked · with the reasoning behind every score.
Account propensity
Daily score across your TAM · fit, intent, engagement, blended into one number per account.
Lead and opportunity scoring
Per-lead and per-opp probability of conversion · written back to your CRM with the reasoning.
Buyer stage signals
Inferred from behavior across the journey: awareness, consideration, intent, decision.
Explainability
Every score carries the top features that drove it · auditable, contestable, traceable.
Next-best action, shipped
The recommendation engine that proposes the move · agents run it under Safe Autonomy.
Next-best action
The recommendation engine that proposes the move agents run · email this lead, push spend here, alert this owner.
Confidence + lift
Every recommendation carries an expected lift, a confidence band, and the action it replaces.
Ranked per buyer
The engine ranks actions per buyer, per account, per cohort. Agents take the top ones.
Reversible by construction
Recommended moves run through Safe Autonomy · proposed, approved, reversible.
Defensible board-ready forecasts
Eight quarters out, daily health, the moves that close the gap before the quarter does.
Eight-quarter projections
Pipeline and bookings projected eight quarters out · with the confidence band, not a point estimate.
Daily pipeline health
Coverage, velocity, mix, distribution · scored every day, decomposable into the move that fixes it.
Gap recommendations
When the number is short, the engine surfaces the cross-application moves that close the gap most.
Built from the whole funnel
Propensity scores, demand gen potential, and pipeline in motion, rolled into one number, not a rep's gut check.
Governed by construction
Evals, calibration, hallucination flags · the same Responsible-AI standard your security team asks for.
Model evaluations
Quarterly evals against held-out outcomes · accuracy, calibration, drift on every model.
Hallucination flags
When generated text crosses a confidence threshold, it's flagged before it reaches your tools.
ISO 42001 by construction
Responsible AI program built in, not bolted on · the same standard procurement asks for.
Bring your own model
Plug Claude, GPT, Gemini, or your own model into the engine via MCP, governed the same way.
The cores it bridges
Context Engine
The harmonized truth the learning engine reads from · entity resolution, dedup, taxonomy.
ExploreAgents
The team that takes the recommendations and runs them across your live tools.
ExploreEnterprise Ready
Implementation rigor, governance partnership, compliance · across the whole platform.
ExploreThe engine proposes. The agents ship
Implementation included. One model on one context layer, and the agents act on what it learns, under your approval.