Forecasting and planning
Estimate demand, workload, or resource needs from historical patterns. Compare predictions with a straightforward baseline before relying on them.
AI & automation / Applied machine learning
Put your data to a useful test.
Forecast demand, classify incoming work, or flag unusual behavior. We build and evaluate machine-learning systems around a specific decision—not a promise that more AI will solve everything.
Based in Bloomington, Illinois. Working with teams across the United States.
Where it fits
A model is only useful if it improves a decision.
Machine learning makes sense when patterns are too complex for a simple rule and you have data that can support a reliable test. We start by checking both. Sometimes the right answer is a better data pipeline or a clear rule, not a new model.
Classification and prioritization
Sort documents, support requests, or operational events into useful categories, with confidence thresholds and a review path for ambiguous cases.
Anomaly detection
Surface readings or transactions that deserve attention. Tune alerts around the cost of missed issues and unnecessary interruptions.
What we deliver
Evidence before wider rollout.
We do not promise a target accuracy before evaluating representative data. Deployment depends on the test results, the cost of errors, and whether an operator can intervene when needed.
- A data-readiness review covering gaps, permissions, and usable labels
- A baseline and a bounded model experiment with agreed evaluation criteria
- An integration plan for putting predictions into the actual workflow
- Monitoring, review thresholds, and a handover for maintaining the model
How we work
Clear steps. Working progress.
Start with a free fit conversation. We will discuss the requirement, decide what needs a closer look, and quote the next useful scope privately.
Define the decision
Specify who will use the output, which data they can access, and what a mistake would cost.
Test against a baseline
Separate evaluation data from development data and compare with the current process or a simple rule.
Integrate and observe
Roll out to a limited workflow first. Monitor changing data, error patterns, and whether people can act on the result.
Before we start
A few useful answers.
Is this the same as adding a chatbot?
No. A chatbot is an interface. Machine learning may power forecasts, classifications, or recommendations without a chat screen. We choose the approach based on the task.
What if our data is not ready?
We will identify the specific gaps before proposing a model build. Data collection, labeling, or integration may be a smaller and more useful first engagement.
Tell us what you want to make better.
You do not need a finished brief. Bring the idea, the existing system, or the part of the work that is not working.