Machine learning models that ship inside your webapp, not a slide deck

"Machine learning" arrives at most businesses in one of two costumes: a research project that produces a beautiful notebook and no production code, or an enterprise platform priced for enterprise budgets. Both miss where the value actually sits. The data already in your CRM, your order history, and your analytics knows things — which leads will close, which customers are about to leave, what next month's demand looks like. It needs a model on top and a way to put the answer in front of the person making the decision.

That last part is where we're different. We're a web engineering firm that builds ML, not an ML lab that tolerates web work.

What machine learning means in our hands

Models ship as product features, not deliverables. The end state of every engagement is a versioned API running inside your webapp — a score next to each lead in your dashboard, a forecast on the purchasing screen, a "customers also bought" rail on the product page. If the prediction never reaches a screen someone acts on, the model didn't ship.

Boring models first. For tabular business data, gradient boosting and regularised regression beat deep learning most of the time — and they're explainable, cheap to run, and easy to retrain. We reach for PyTorch when the problem genuinely needs it (text, images, sequences), not to decorate a proposal.

Monitoring is part of the build, not an upsell. Every deployed model ships with drift monitoring: input distributions, prediction distributions, and accuracy tracked against ground truth as it arrives. A model that silently degrades is worse than no model, because you stop questioning numbers that look official.

Stack and approach

• Modeling: Python, pandas, scikit-learn, XGBoost / LightGBM; PyTorch where the problem warrants it
• Serving: FastAPI services or Next.js route handlers, deployed alongside your app
• Data: Postgres / Supabase as the system of record, with scheduled jobs for feature refresh
• Evaluation: holdout sets, baseline comparison, and metrics chosen for the business decision — a churn model tuned for recall behaves very differently from one tuned for precision, and we pick with you, on purpose
• Monitoring: drift dashboards and alerting wired into the same admin you already use

Everything integrates naturally with the webapps we build — see /services/webapps — but we also deploy into existing stacks.

What this looks like in practice

Two typical engagement shapes:

Lead scoring for a service business. Your CRM holds a few years of inquiries — some closed, most didn't. We train a scoring model on that history and wire it into the inbound flow: every new lead lands with a score and the three factors driving it, so your team calls the likeliest closers first. Usually paired with a small CRM cleanup, because label quality decides model quality.

Demand forecasting for an ecommerce operation. Order history, seasonality, and promotions feed a weekly per-category forecast that appears on the purchasing screen next to current stock. The goal is fewer stockouts and less cash frozen in slow inventory — a natural companion to an ecommerce build.

Other common shapes: churn early-warning for subscription businesses, anomaly detection on transactions or operational metrics, and recommendation rails for catalogs large enough to need them.

Pricing

Machine learning work is scoped to the project. An honest quote depends on your data, the decision the model feeds, and where it needs to deploy — so we quote after a short conversation, not from a tier table. Contact us with a paragraph on what you're trying to predict and what data you have. Within two business days we'll tell you whether it's feasible, what shape the engagement takes, and — when ML isn't the right tool — that a simpler rules-based approach will serve you better.