Revenue and cash forecasting
Turn pipeline, invoices, seasonality, and payment history into a forecast owners can review before decisions get expensive.
Predictive analytics consulting Indianapolis
Predictive analytics that helps operators decide sooner.
Indy AI Consulting helps Indianapolis companies use forecasting, scoring, anomaly detection, and decision dashboards to catch risk before it becomes a missed target.
The useful version
Prediction is only valuable when it changes a decision.
A predictive model is not the goal. The goal is a better operating rhythm: know which invoice may slip, which lead needs attention, which ticket may escalate, or which expense looks wrong.
Good predictive analytics starts with the decision, then works backward to the data, model, dashboard, and human review step.
Bad fit signals
- No one can name the decision the prediction will change
- The data source is unknown, inaccessible, or owned by no one
- The team wants a dashboard but will not change the workflow
- A wrong prediction would create legal, financial, or customer risk without review
Use cases
Predictive analytics projects worth testing first.
Lead and customer scoring
Rank leads, accounts, or customers by likely fit, urgency, churn risk, or next-best action using the data you already collect.
Operations risk signals
Flag orders, tickets, vendors, projects, or locations that look likely to miss a target before the miss shows up in a report.
Staffing and demand planning
Use history, seasonality, and known events to plan staffing, inventory, service capacity, or support coverage.
Anomaly detection
Spot spend drift, unusual transactions, duplicate work, outlier calls, or process changes that deserve human review.
Decision dashboards
Build focused dashboards that show what changed, why it matters, and which decision needs an owner.
How it works
Start with a decision, not a data science wish list.
I help teams pick one forecast, score, or warning signal, test it against real outcomes, and decide whether it deserves a place in the weekly workflow.
- 1
Define the decision the prediction should improve.
- 2
Audit the data sources, owners, gaps, and cleanup needed.
- 3
Build a baseline model or scoring workflow that can be tested.
- 4
Compare predictions against real outcomes and decide what belongs in production.
Start with one decision
What would you act on sooner if you could see it coming?
Send the decision, the data you have, and what happens when the signal arrives too late.
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