Why the number is always late
Steer the business on models that stay current
Aevah builds and maintains the pipeline behind your financial models, then puts scenario modelling in the hands of the people accountable for the number.
Measures worth agreeing
- Time from signal to responsible decision
- Manual effort to assemble the decision
- Unresolved assumptions and exceptions
- Action completion against the decision
What that looks like in the cycle
- Models go stale between manual refreshes
- Scenarios drift from the evidence that produced them
- Plans and briefs are rebuilt by hand every cycle
- Actions disconnected from the evidence that justified them
What Aevah operates
The pipeline runs itself, so the model in front of you is current.
Aevah connects the sources, conforms the identity between them, engineers the features, selects and trains the models, and refreshes them as inputs update. Your teams work at the decision, not the plumbing.
- 01
Connect
ERP, POS, sales, promotion, cost, and syndicated market data are brought into one governed estate rather than reconciled in spreadsheets.
- 02
Conform
Internal product, customer, and location keys are resolved to their external equivalents, so spend and market response can be judged against the same identity.
- 03
Engineer
Feature engineering is automated against the conformed estate, with confounds, measurement windows, and data-quality guardrails applied as part of the work.
- 04
Model
Model selection and training are automated, with version, training window, and measured performance retained alongside every output.
- 05
Refresh
Models are retrained and rescored as inputs update, so the scenario a finance owner runs reflects current data rather than the last manual rebuild.
- 06
Simulate
Model outputs feed live scenario modelling, so the people accountable for the number can test a decision before committing to it.
What finance gets
Four decisions you can put a name against.
Each one is a bounded starting point you can evaluate on its own, against one recurring decision rather than a platform rollout.
Capability
Plan variance and profitability
Forecast against actual for units, spend, and revenue, with budget remaining and item-level cost carried through to contribution rather than stopping at gross.
Inspect the use case →Capability
Forecasting and guarded scenarios
Forward views with the assumptions, limits, revisions, and decision ownership kept visible, so a scenario can be defended rather than only presented.
Inspect the use case →Capability
Promotion and price response
Separate genuine lift from discounting, and incremental demand from demand that simply moved, with the confounds that would distort the read surfaced rather than buried.
Inspect the use case →Capability
Refreshable executive brief
Source-linked context, decisions, and open questions refresh without losing the narrative, so the leadership view is current without being rebuilt.
Inspect the use case →Capacity, not headcount
The team you do not have to hire
Standing this up in-house means data ingestion from client environments and warehouses, feature engineering and scoring in Python, production pipelines and scheduled refreshes, backend APIs, front-end integration, and cloud deployment and monitoring. That is a full-stack data and ML engineering function, not an analyst. Aevah operates it, and your existing finance and commercial teams stay the owners of the decisions.
- Data engineering
- Feature engineering
- Model training
- Pipeline operations
- Deployment and monitoring
A bounded starting point
Begin with one decision, not the whole function.
One recurring financial decision with a deadline, an accountable owner, a defined evidence set, and observable follow-through.
Boundaries that stay explicit
- Aevah does not replace the ledger, the consolidation system, or the accountable reviewer.
- Scope, source readiness, and definitions are agreed before a first decision is instrumented.
- A scenario is a proposal. The finance owner retains authority for what is accepted and acted on.
- Model coverage, refresh cadence, and source scope are confirmed for the agreed operating scope.
Written for finance leaders
How we think about AI in the finance function.
CFO
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 →CFO
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 →A practical next step
Bring us one recurring finance decision.
We will compare the decision, the data behind it, the assumptions it carries, the owner who signs it, and what would make the next cycle defensible.
Compare a recurring finance decision