ML / Predictive Modeling
Custom machine learning models built for your data.
We build machine learning models that find patterns in your data and turn them into predictions, recommendations, and classifications — purpose-built for your specific problem, not generic SaaS integrations. Serving businesses across the United States.
91%
Avg. model precision
38%
Avg. outcome improvement
6wk
Concept to deploy
what it is
What is custom machine learning consulting?
Machine learning models learn patterns from your historical data and apply them to new situations — predicting what's likely to happen, classifying inputs, or recommending the right next action. Unlike rule-based systems, they improve as they see more data.
Custom ML models pair naturally with AI agents — once a model can predict or classify, an agent can act on those predictions automatically without human intervention.
the problem
Does this sound familiar?
You're reacting to problems instead of preventing them. Delivery failures, customer churn, inventory stockouts — you only find out after they've already cost you money.
Recommendations are generic or manual. You're showing the same products to every customer, or someone is hand-curating lists. Neither scales.
Classification is done by hand. Someone is reading emails, tickets, or applications and sorting them manually. That's a machine learning model waiting to be built.
Off-the-shelf ML tools don't fit your data. Generic platforms aren't trained on your specific patterns, your customer base, or your operational context.
what we build
What we deliver
Predictive machine learning models
Forecast what's likely to happen — churn, demand, failures, delays — so you can intervene before the cost is incurred.
Classification systems
Automatically categorize inputs — support tickets, applications, transactions, documents — with high precision.
Recommendation engines
Personalized product, content, or action recommendations based on user behavior and context.
Anomaly detection
Flag unusual patterns in your data — fraud signals, operational failures, quality issues — before they become incidents.
Demand forecasting models
Predict future demand by SKU, region, or channel to optimize inventory, staffing, and procurement.
how it works
How we build your machine learning model
Data audit & feasibility
We assess your historical data — volume, quality, label availability — and confirm whether a model is viable before committing to build.
Feature engineering
We identify and construct the input signals that carry the most predictive power for your specific problem.
Model training & evaluation
We train, tune, and evaluate against held-out test data — optimizing for the metric that matters to your business: precision, recall, or AUC.
Deploy & monitor
The model is deployed as an API or batch job, with monitoring for drift and scheduled retraining as new data accumulates.
tech stack
Technology we use
Modeling
scikit-learn / XGBoost
Language
Python
API
FastAPI
Data
PostgreSQL
Scheduling
Airflow
Hosting
AWS / Vercel
Common questions about machine learning consulting.
A custom ML model learns patterns from your historical data and applies them to new situations — predicting outcomes, classifying inputs, or recommending actions. Unlike generic SaaS tools, it's trained on your specific data and optimized for your business outcomes.
It depends on the problem type and signal strength in your data. During the data audit and feasibility phase, we evaluate your historical data — volume, quality, and label availability — and confirm whether a model is viable before committing to build.
Most custom machine learning models are built and deployed in approximately 6 weeks — from data audit through training, evaluation, and production deployment with API access.
Common use cases include predicting churn, demand, failures, or delays; classifying support tickets, documents, or transactions; personalizing product recommendations; detecting anomalies like fraud or quality issues; and forecasting demand by SKU, region, or channel.
We build with scikit-learn and XGBoost for modeling, Python for data processing, FastAPI for model serving, PostgreSQL for data storage, Apache Airflow for scheduling, and AWS or Vercel for hosting.
related work