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Machine learning that ends in a decision

Which customers are about to leave. What next month looks like. Which product to show this visitor. We build machine learning and analytics that turn the data you already collect into decisions you can act on, and we measure the model by the business number it moves.

Analytics that ends in a decision, not a dashboard

A prediction nobody acts on is a chart. We build the model and the workflow that uses it.

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A good fit if you

  • Have at least a year of sales, customer, or operational data
  • Make recurring decisions that depend on guessing what happens next
  • Want to know which customers are at risk before they're gone
  • Sell enough products or services that recommendations would change what people buy
  • Are tired of reports that describe last month without telling you what to do about it

Machine learning finds patterns in your history and uses them to predict what's likely next: which leads will close, which customers will churn, how much you'll sell in each location in each week, which offer a given person is most likely to accept. Advanced analytics is the broader discipline of turning that data into something a person can decide from. We do both, and we insist they end in a decision.

Most analytics projects produce a dashboard that gets admired for a week. We build the model, then we build what happens because of it: the alert that fires when a high-value customer goes quiet, the reorder quantity that updates itself, the recommendation that shows up on the product page. The model is worth exactly what the workflow around it earns.

We're honest about what the data can and can't support. If your history is too thin to forecast reliably, we say so and start with the data engineering that fixes it. A model built on bad data is worse than no model, because people trust it.

What we build with your data

Each of these is a working system that produces a number your team uses, not a notebook that produces a chart.

Tools and stack we work in

  • Python
  • scikit-learn
  • XGBoost
  • PyTorch
  • BigQuery
  • Snowflake
  • dbt
  • Looker
  • Power BI
  • Metabase
  • Vertex AI
  • AWS SageMaker

We build in the cloud you already use and hand over models with documentation your team can maintain. Nothing depends on a tool only we can operate.

  • Demand and revenue forecasting

    Weekly or monthly forecasts by product, location, or channel, with confidence ranges so you know how much to trust each one.

  • Churn and retention prediction

    Which customers, patients, or policyholders are likely to leave in the next period, ranked, with the reasons the model weighs most.

  • Lead scoring

    Which inquiries are most likely to close, so the best ones get called first and the ad budget follows the leads that convert.

  • Recommendation systems

    What to show, offer, or upsell to a given customer, on your site, in email, or in the hands of your sales team.

  • Customer lifetime value modeling

    What a customer is worth over time by segment and source, so acquisition spend can be sized to reality.

  • Anomaly detection

    Unusual transactions, costs, or usage flagged the day they happen rather than at month end.

  • Pricing and margin analytics

    Where price is leaving money on the table, where discounts aren't buying anything, and what the elasticity actually looks like.

Where it changes the numbers

Machine learning earns its keep where the same decision is made often and each one is worth something.

Insurance agencies

Renewal risk scored by policyholder so retention effort goes where it saves a policy, and lead scoring that ranks inquiries by likelihood to bind.

HVAC and home services

Seasonal demand forecasting by service area for crew scheduling and ad pacing, plus maintenance-plan churn prediction.

E-commerce

Product recommendations, inventory forecasting by SKU, and customer lifetime value that reshapes how much you'll pay for a first purchase.

Medical and aesthetic practices

No-show prediction so schedules can be overbooked intelligently, and treatment-cycle timing for patient outreach.

Real estate

Lead scoring by likelihood to transact and price-band modeling that tells agents which listings to push to which buyers.

Restaurants and hospitality

Covers and revenue forecasting by day and daypart, labor planning against it, and event-driven demand alerts.

Why the model is only half the job

We came to machine learning from the marketing side.

We had campaign data, CRM data, and call data, and the question was never which algorithm to use. It was which leads to call first, how much a customer from this ZIP code was actually worth, and how much to spend to get another one. Those are ML problems, and they only matter if the answer changes what someone does on Monday.

So every engagement here ends with a workflow, not a deliverable. The forecast feeds the schedule. The churn score triggers the outreach. The lead score reorders the call list and reshapes the ad bids. We report on the business outcome, and if the model is accurate but nobody's using it, that's a failure we own.

How we work

  1. Discovery and data audit

    We learn how the business makes money, where the decision or the workflow actually breaks, and what data exists to fix it. If the data isn't there yet, that becomes step one.

  2. Roadmap and architecture

    One plan that names the outcome, the integrations, the guardrails, and the budget. Sequenced so the fastest win funds the longer build.

  3. Build and integrate

    We build inside your systems and your accounts, connect to the tools you already run, and test against real data before anything touches a customer.

  4. Measure and improve

    Monthly reviews on the business number the system was built to move, not on model accuracy in isolation. We keep tuning after launch.

The same standards as everything else we do

  • You own the code and the data

    Source, models, prompts, pipelines, and accounts are yours. If we ever part ways, everything we built stays with you.

  • Revenue model first

    We ask how the company makes money before we ask which model to use. AI that doesn't move a business number is a science project. It's how we work on everything.

  • No hidden fees

    Scope is written before it's priced. Cloud and API costs are passed through at cost, not marked up.

  • Senior people, always

    The engineer who scopes your system is the one who builds it. No handoff to a junior pod after the contract.

  • Guardrails by default

    Human review where decisions carry risk, logging on every automated action, and a kill switch you control.

  • Three languages

    Systems that read, write, and talk in English, Spanish, and Portuguese, because your customers do. See where we operate.

Questions we get asked

It depends on the question. Forecasting usually wants at least two years of history to capture seasonality. Churn and lead scoring can work with a few thousand records. If the data isn't there yet, we start with data engineering and tell you when the model becomes viable rather than building one that can't be trusted.

Your data already knows. Let's ask it.

Thirty minutes on the decision you make most often and the data you have to make it with. You'll leave knowing whether a model would change the answer.

Book a strategy session ($350)
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