The first platform where live transactional data flows directly into the model. Governed AI agents act on current reality - monitoring supply chains, detecting variance, recalibrating forecasts - while you focus on decisions, not data wrangling.
Connect sales, operations, and finance in one unified model. Driver-based planning with automatic roll-ups. AI can scaffold the entire IBP structure from a single description.
Describe your business model in plain English and AI scaffolds complete modules, line items, formulas, dimensions, and reports. From idea to working model in a single conversation.
Adjust pricing, allocations, or resource plans and see the financial impact propagate across every dimension instantly. Optimise in real-time, not in retrospect.
Create unlimited what-if scenarios with one click. Compare best/worst/base cases side by side and merge the winning plan back in seconds.
Connect your CRM and see pipeline changes flow into revenue forecasts in real-time. No more monthly data dumps - your plan always reflects the latest deal stages and close dates.
Consolidate across subsidiaries with currency conversion, eliminations, and minority interest calculations.
SKU-level forecasting across thousands of products, channels, and geographies. Live sales data feeds keep forecasts current without manual updates.
State-based models track customers through lifecycle stages with probabilities calibrated from actual retention data. Subscription tier migration projects recurring revenue with revenue-weighted state transitions, producing forecasts that adapt to macro shifts.
Headcount planning with salary bands, benefits, and allocations. Bottom-up expense modeling.
Scheduled agents can refresh approved external signals, simulate across supply chain models, and produce probability-weighted risk reports before your team arrives.
Model cash positions across entities and currencies. Agents fetch live FX rates and yield curves, update hedging assumptions, and run LP optimisation for minimum-cost borrowing strategies.
Evaluate capital projects across sites with optimization for portfolio selection subject to budget, regulatory, and resource constraints.
Model loss reserves across lines of business and development periods. Monte Carlo generates reserve distributions with confidence intervals for actuarial requirements.
Multiple autonomous agents coordinate through shared memory: demand agents write forecasts, supply agents optimise inventory, finance agents project cash, and risk agents simulate the combined plan.
Model capacity, material requirements, and order backlogs. Optimization produces feasible production schedules that minimise changeover costs while meeting delivery commitments.
Programme budgets across departments with mandatory compliance guardrails, cell-level departmental security, multi-level approval workflows, and sovereign air-gapped deployment.
What would have to go wrong to break this plan? Define the thresholds that matter - minimum cash balance, maximum leverage, revenue floor - and the engine works backward to identify exactly which assumptions would breach them. Quantify the conditions that would break your plan before the board asks.
Test any decision against a live replica of your business before committing real resources. New market entry, restructuring, pricing changes - simulate the full impact against real operational data. Agents run scenarios continuously and surface risks before they reach the P&L.
The engine is designed for high-volume planning updates and efficient model storage. Adding analytical capacity means adding a governed agent, not another manual handoff. One FP&A professional can cover more ground because agents help monitor variances, adjust forecasts, assemble reports, and flag risks around the clock.
Working with select organisations in pilot