Machine Learning Development Services

We build production machine learning trained on your data: predictive analytics and forecasting, fraud and anomaly detection, recommendation systems, computer vision, and NLP, plus the data engineering, deployment, monitoring, and retraining to keep them accurate. We start by helping you decide whether ML is even the right tool for your problem.

Classic machine learning focuses on models trained on your data to forecast, score, detect, and recommend. Generative AI and large language models (which create content and handle open-ended language) are a different discipline and are covered by our AI Development service. The two solve different problems, and we'll tell you which one you actually need.

For structured, tabular business data (forecasting demand, scoring risk, detecting fraud, recommending products), a trained ML model is more accurate, cheaper to run, and more interpretable than a large language model, which can't reliably predict from your data and often hallucinates. Use an LLM to generate or understand open-ended language; use classic ML for prediction. Most business problems are the second kind.

It depends on the problem, but data quality matters more than raw volume, because clean, well-labeled, and representative data beats a bigger, messier dataset. Part of our assessment is to tell you honestly whether your data is ready and to build the data pipeline if it isn't.

Most stall because there's no MLOps: no deployment pipeline, no monitoring, no clear ownership, so the model works in a notebook and dies before launch. We engineer for production from day one, with pipelines, versioning, monitoring, and retraining built in.

Models drift as the world and your data change, often silently, until a business metric drops. We incorporate data drift, prediction drift, and performance monitoring with automated retraining, so accuracy is maintained rather than assumed.

In business terms, agreed up front (churn reduced, fraud caught, forecast error cut, conversion lifted) not just an F1 score. Accuracy metrics matter for engineering, but the number that counts is the one on your dashboard.

ML Development is classic, predictive machine learning trained on your data. AI Development includes generative AI, LLMs, and custom model work. Building a new AI product is AI Product Development, and adding AI into existing systems is AI Integration. Many projects use more than one; we'll point you to the right mix.

Per industry averages, an ML feasibility or focused model project typically costs $40,000–$100,000, while a larger custom model or ML platform with data pipelines and MLOps runs into the low hundreds of thousands, plus ongoing monitoring and retraining. Data preparation is the biggest hidden cost driver if your data is siloed or unlabeled.

Custom work doesn't fit a fixed price list, so we scope your project and give a tailored estimate, and our scope generator can give you a quick ballpark estimate in the meantime.

A simple model can reach a working proof of concept in about 6–10 weeks; a complex solution with data engineering and MLOps runs 3–6 months. The first step is always a Scope & Design Document that turns your data and goal into a phased plan.

In-house gives control, but ML talent is scarce and expensive, and a single-champion team is fragile if that person moves on. A partner brings a full-lifecycle team (ML, data, and MLOps engineers) accountable for the model's production deployment and ongoing accuracy. We can also work as an extension of your team.

Yes, completely. You own the trained model, the code, the data pipeline, and all IP, with full rights transferred to you, and it runs in your own environment.