Better decisions. Stronger operations.
From production and supply chains to energy and cash flow, we help enterprises clarify trade-offs, build purpose-fit models and bring decisions into governed operational workflows.
The next move starts with a better decision.
Prediction is a beginning. Connecting priorities, constraints and action is where enterprise value takes shape.
From signals to accountable action.
A forecast becomes useful when it meets real constraints, reveals the trade-offs, and gives people a decision they can review.
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Start with the operating reality.
Bring orders, capacity, stock and business rules into one defined decision context. Check what is known, what is missing and who owns the data.
Orderbook · capacity · stock -
Understand what may change.
Forecasts describe possible demand and uncertainty. They inform the decision; a predicted number alone does not choose the operating plan.
Expected demand · uncertainty range -
Compare what is feasible.
Evaluate alternatives against capacity, availability, service and budget constraints. Make the cost of each trade-off visible before choosing a plan.
Feasible plans · explicit trade-offs -
Show the reasoning behind the plan.
Connect the recommendation to its inputs, assumptions and binding constraints. The team should be able to understand what would change the recommendation.
Rationale · assumptions · alternatives -
Keep responsibility with people.
Route the proposed plan to the responsible team, record approval or changes, and follow its outcomes. Human review remains part of the decision process.
Decision owner · approval · follow-up
Better decisions start with a shared view of the business.
Connect fragmented enterprise data to a governed lakehouse, then turn reusable data products into reviewed business actions.
Explore the data-to-decision journey →Industry context. Connected decisions.
Performance is a connected decision.
An order changes capacity. A stock decision ties up cash. An energy plan changes cost and carbon exposure. We make these connections explicit so teams can act on a shared view of value and risk.
Operations & production alignment
Connect order commitments, dynamic costs and finite capacity to make production plans that reflect real operating constraints.
Supply chain & procurement resilience
Balance service levels, inventory and supply risk across procurement, replenishment and allocation decisions.
Energy & low-carbon operations
Coordinate energy assets, demand and commercial constraints within a shared energy–carbon–finance model.
Cash flow & working capital
Bring finance and operations onto the same view of liquidity, inventory, receivables and payment timing.
Enterprise AI & decision governance
Move from an AI experiment to a governed workflow, with private deployment, human approvals and traceable execution.
Data governance & lakehouse foundations
Build the data foundation for enterprise decisions. AEVO connects governance, data lakes and Medallion pipelines to analytics, AI and decision optimization.
Make the trade-offs visible.
An urgent order. A constrained factory.
A manufacturer needs to assess a new order without disrupting existing commitments. Compare margin, material availability, machine capacity and delivery feasibility together. The output is a reviewable acceptance and scheduling recommendation, measured against delivery and contribution-margin baselines.
Inventory decisions are cash decisions.
A multi-location business needs service levels without unnecessary stock. Model demand and lead-time uncertainty, compare replenishment policies, and trace their impact on stockouts, inventory days and the cash conversion cycle before changing the operating policy.
Energy assets. One economic picture.
An operator considers coordinating solar, storage and flexible loads. Compare dispatch scenarios under tariffs, asset degradation and carbon objectives. Separate operating savings from investment assumptions, and validate feasibility before connecting asset controls.
Illustrative business scenarios and AI-generated editorial imagery; not published customer case studies.
A disciplined path from insight to execution.
Frame the decision
Agree on business owners, data boundaries, constraints and the current performance baseline.
Validate in shadow mode
Compare model recommendations against existing decisions before changing live operations.
Integrate with control
Connect agreed systems, approval gates and exception handling with your operating teams.
Monitor value over time
Track agreed KPIs, review model behavior and adapt as business conditions change.
Perspectives on better decisions.
Prediction is only the beginning of a decision.
A forecast estimates what may happen. A decision model compares what you can do under real constraints.
Working capital is an operating decision.
Connect inventory, receivables and payment timing before optimizing each financial metric in isolation.
Enterprise AI needs a decision boundary.
Define what may be recommended, approved and automated before connecting AI to live operations.
From a data lake to a decision that can be defended.
Medallion Architecture creates a useful separation between source evidence, shared entities and business data products. The next step is to connect those products to accountable decisions.
Decision intelligence, grounded in your business.
Machine learning estimates uncertainty. Operations research tests feasible choices. AI explains the reasoning and supports workflows. Governance connects the recommendation to an accountable action.
The public decision engine has eight modules and uses sample data. Production planning and energy optimization are additional enterprise capabilities. Demonstrations illustrate scenarios, not customer results.
The bridge from strategy to daily decisions.
Our role is to make operating choices explicit, test them against constraints, and embed the resulting models into your workflows. We work with your leadership, domain experts and technology teams; accountability and the business context remain central throughout delivery.
From business questions to decisions you can explore.
Explore the Enterprise Decision Engine with sample data. See how business priorities, constraints and recommended actions come together.
Decision EngineThe public decision engine has eight modules and uses sample data. Production planning and energy optimization are additional enterprise capabilities. Demonstrations illustrate scenarios, not customer results.
How the Decision Engine works →Demand & inventory
Explore how demand changes affect replenishment, shortage risks and inventory levels.
Working capital
Compare the effects of receivables, payables and inventory on the cash conversion cycle.
Supply chain risk
Review supplier concentration, delivery variability and sourcing alternatives.
Start with a decision that matters.
Tell us where cost, capacity, risk or cash is limiting performance. We will help define a focused evaluation before committing to a wider transformation.