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Applied machine learning

Machine learning with a decision on the other side.

We have shipped production ML since before generative AI became a category. Models are selected for the decision, the available data, and the cost of being wrong—not for how impressive the vocabulary sounds.

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Business outcomes

What the work can change.

  • 01Forecast demand, staffing, inventory, or cash
  • 02Prioritize leads, products, or operational actions
  • 03Estimate incrementality and marketing contribution
  • 04Find useful customer, store, or product segments
Delivery

How engineering moves the work.

  1. 1Define the decision and error costs
  2. 2Build transparent baselines before adding complexity
  3. 3Backtest against real historical decisions
  4. 4Deploy monitoring, review, and override paths
Representative work

Proof in production context.

Applied machine learning

$300M bidding and recommendation engine

A production ranking and bidding-optimization system designed around real-time decisions for a national auction platform.

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Applied machine learning

ZIP-code lead prioritization

A regression system using public demographic data to help a home-services call center prioritize incoming leads.

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Applied machine learning

Commodity demand forecasting

A multi-model supply-and-demand forecasting system for one of the country’s leading avocado suppliers, evaluated against real purchasing decisions.

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Start with the real problem

Talk directly with an engineer who can help build it.

Bring the current system, the desired outcome, and whatever uncertainty remains. We will help identify the smallest credible path to production.

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