01 Business signals
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
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.
Priorities
- Service level
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- Cost
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- Carbon
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Data points become a forecast, constraints cut out the feasible region, and the decision lands on its best vertex. Change the priorities to see the decision move.
THE DECISION JOURNEY
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.
02 ML forecast
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
03 OR & constraints
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
04 Explanation
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
05 Human review
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
Illustrative workflow · synthetic inputs · Conceptual illustration of the workflow. No customer results or live AI inference are shown.
Decision replay
Watch one decision run, from data to approval.
Pick a decision agent: it reads the data, forecasts, solves with HiGHS, explains the plan and then waits for you to approve it or send it back. The solve runs in your browser and the time is real; the data is illustrative.
Illustrative data · solved in your browser · not a client case
This is an example log produced at build time. With JavaScript, you can re-solve and approve in your browser.
Observe Reading example data: opening stock 600 units, the supplier ships at most 700 a week, each order costs $1,200, holding costs $1.50 per unit and week.
Forecast Median demand over the next 8 weeks is 380–610 units, with a promotion in weeks 4–6; the forecast error grows with the horizon, from 10% to 21% of demand.
Forecast At a 95% service level, safety stock is 69–168 units a week.
Solve Solving a mixed-integer program with HiGHS: 24 variables, 24 constraints.
Solve Optimal plan found (computed at build time by the same solver) · gap at most 0.3%
Plan 6 orders (weeks 2–7), total cost $9,591: ordering $7,200, holding $2,391.
Explain The supplier’s capacity is the plan’s bottleneck (weeks at the limit: 6–7). One more unit of weekly supplier capacity saves $4.50.
Approve Waiting for approval by the purchasing manager.
Capabilities
- 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.
Industries
- Manufacturing & supply chains For operations, planning and procurement leaders: align order economics, machine and material constraints, supplier variability and multi-site inventory.
- Energy & infrastructure For asset operators and energy managers: evaluate storage, distributed generation, demand response and low-carbon investment under technical and commercial constraints.
- Retail & direct-to-consumer For commercial and operations leaders: coordinate demand, pricing, staffing, inventory and working capital across channels and locations.
- Finance & corporate treasury For CFOs, treasurers and risk teams: stress-test liquidity, cash conversion and portfolio exposures with explicit assumptions and reviewable recommendations.
Solution Center
- Enterprise Decision Engine (opens in a new tab) Which operating levers across inventory, working capital, pricing and supplier risk should we act on first, and what is each worth?
- Battery storage multi-market trading optimizer (opens in a new tab) How should a grid-scale battery divide its capacity across day-ahead, balancing reserve and intraday markets while protecting asset life?
- Decarbonization investment & procurement optimizer (opens in a new tab) Which mix of efficiency projects, renewable power and certificates meets our Scope 1 and 2 targets at the lowest lifecycle cost over five years?
- Tokyo rental acquisition decision (opens in a new tab) What is the most we should pay for a Tokyo apartment building, how much should we borrow, and which assumption breaks the deal first?
- Fund tracing & AML graph intelligence (opens in a new tab) Where is the suspicious money now and how much can still be frozen, which accounts should be reported or frozen, and which belong to victims?
Data foundations for decision intelligence
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
Our approach
A disciplined path from insight to execution.
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Frame the decision
Agree on business owners, data boundaries, constraints and the current performance baseline.
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Validate in shadow mode
Compare model recommendations against existing decisions before changing live operations.
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Integrate with control
Connect agreed systems, approval gates and exception handling with your operating teams.
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Monitor value over time
Track agreed KPIs, review model behavior and adapt as business conditions change.
Perspectives
- 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.
Technical leadership
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.
Dr. YuLi Tsai · CTO & Co-Founder
About AEVOStart 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.