Know the inputs.
Identify the sources, definitions, and planning period behind the scenario.
Aevah Decision Pre-flight
Ask why.
Then ask
what if.
Change assumptions. Compare likely outcomes. Evaluate the next move before committing your budget, capacity, or business.
An interactive illustration. No email required.
One capability. Decisions across the business.
Pricing · Promotions · Distribution · Production · Inventory
Explore Decision Pre-flight
Every important decision
has assumptions.
Test them before they become outcomes.
Change the plan. Compare the impact on contribution and demand. See the constraint before it becomes a commitment.
ADecision Pre-flightSummit Market · sample account
01 / Change an assumption
Which promotion
should we run?
Test a different window or investment. Check the plan against available supply.
02 / See the tradeoffs
What changes
before you commit?
Within capacityProjected contribution$669,300+$51,300 vs. current plan
Forecast demand44,700 cases2,700 additional cases
Contribution after promotion spendCurrent plan vs. your scenario
1,300 cases of capacity remain
This scenario fits the supply assumption. Your team still reviews the commercial case.
Inspect the assumptions and calculation
- Source
- Synthetic promotion and unit-economics assumptions. No customer data.
- Demand
- 34,000 underlying cases + 8,000 × √(spend ÷ $180,000) × timing factor. Week 7 = 1; week 9 = 1.3375. Rounded to whole cases.
- Contribution
- Demand × $19 contribution per case, less promotion spend. Unit economics are held constant.
- Capacity
- A feasibility check against demand; changing capacity does not create demand.
- Limits
- This illustrative model assumes diminishing response to spend and a stronger week 9. It is not a validated forecast or causal estimate. No probability range is implied.
03 / Your team reviews. Your team decides.
Interactive illustration · Synthetic dataChanges stay in this page. No operational action is taken.
Ask why. Change the assumptions. Test what happens next.
Explore the decision workflowFrom executive review to analyst drill-down
Decisions you
can defend.
A useful scenario makes its reasoning inspectable. Know what went into the answer, what could change it, and who owns the next move.
Explore Security & Trust ↗Challenge the assumptions.
See what changed, what stayed constant, and where the model has limits.
Understand the boundaries.
Evaluate budget, capacity, and service constraints alongside uncertainty supported by the analysis.
Keep the decision accountable.
Agree the reviewer, approval requirements, and measures for comparing expectations with actual outcomes.
Operational Data Science · The foundation underneath
You focus on the decision.
We manage the analytical work.
Your business doesn’t operate in a closed dataset. Relevant external signals belong alongside your operational data.
The operating reality.
Sales · Product costs · Promotions
Inventory · Production · Finance
The conditions that change.
Commodity costs · Fuel prices
Weather · Relevant market signals
Aevah
Context.
Models.
Constraints.
Connect and prepare the inputs. Build and validate the analysis. Keep the working experience current.
Decision Pre-flight
Options your team
can evaluate.
- Compare likely outcomes
- Inspect the tradeoffs
- Choose with evidence
Your people retain authority.
Sources and analytical methods are selected and validated for the agreed decision.
See what powers Pre-flight →First Flight · How you start
Start with one
consequential decision.
Decision Pre-flight is what your team does with Aevah. First Flight is how we establish the first working scope together.
Frame the decision.
Name the owner, alternatives, business constraints, and measures that would make the work worthwhile.
Establish the foundation.
Agree the relevant sources, analytical methods, access, deployment, and review requirements.
Evaluate the result.
Validate the working experience, assess adoption and operating evidence, and decide what to refine or expand.
The data, models, timeline, and responsibilities are agreed for your use case and readiness.
Explore First Flight →The conversation continues
Ideas worth taking
back to your team.
Perspectives, practical guides, and questions for leaders putting data and AI to work.
All insights & blog posts
The Governance Gap That Turns AI from Asset to Liability
Ungoverned AI in finance is not just an IT risk. It is a fiduciary one. When a board asks how an AI-driven financial recommendation was made, the answer either traces cleanly to governed data — or it does not.
Read the insight ↗
The 90-Day Standard: How to Hold AI Vendors Accountable to Outcomes
If a vendor cannot show you a measurable result in 90 days, ask why. Then ask it again. The 18-month implementation timeline is not a technical requirement — it is a commercial structure that protects the vendor, not the buyer.
Read the insight ↗
What Happens to the Finance Function When AI Actually Works
Not a vision piece. A practical look at what changes — operationally, relationally, and strategically — in the finance functions that got the implementation right.
Read the insight ↗Turn the thinking into a next step.
Explore briefs, playbooks, and evaluation guides to use with your team.
Before the next commitment