Machine learning development services for forecasting, risk scoring, recommendations, and document processing. We build the data pipelines, train and deploy custom models, and monitor them after launch, so predictions feed real decisions instead of sitting in a notebook.
Technologies we use
A model that scores well in a notebook is not the same as a model that works every day on live data. Most machine learning projects stall in the gap between the two: the data pipeline breaks, nobody owns the model, or the predictions never reach the people who make decisions.
Our machine learning development starts from the business decision the model should improve, such as how much stock to order or which customers to call first. We build the data pipeline, the model, and the integration together, then keep measuring accuracy after launch.
We also tell you when machine learning is not the answer yet. Sometimes clean data, a good dashboard, or a few simple rules get you most of the value at a fraction of the cost.
Predict sales by product, store, or region for the coming weeks, so you order the right stock and plan staff ahead of demand instead of reacting to it.
Data it needs: Sales history, prices and promotions, holidays, and seasonality.
Score every customer by how likely they are to leave, and why, so your team can reach the right accounts before they cancel.
Data it needs: Usage or purchase history, support tickets, and billing data.
Flag risky transactions, claims, or loan applications for review, while normal ones go through without delay.
Data it needs: Past transactions or applications with known fraud or default outcomes.
Suggest products, content, or next best actions based on what similar customers did, on your website, app, or emails.
Data it needs: Product catalog, views, clicks, and purchase history.
Read invoices, forms, IDs, and contracts, extract the fields you need, and send them to your system, with unclear cases routed to a person.
Data it needs: Sample documents and the list of fields you want extracted.
Detect defects, count items, or check images against rules, for quality control, inspections, and content moderation.
Data it needs: Labeled images or video of good and bad examples.
Reliable ETL and feature pipelines, so models are trained and served on the same consistent, well-governed data.
We compare every model against a simple baseline and your current method, and judge it on business metrics, not just accuracy scores.
Text classification, entity extraction, and recommendation engines tailored to your product's data.
Versioned models, deployment pipelines on cloud and DevOps infrastructure, drift detection, and retraining workflows that keep models accurate over time.
Every project moves through the same clear steps, so you always know what is happening next.
We define the business metric the model needs to move, not just a technical target.
Data quality, availability, and labeling needs are checked before any modeling begins.
Iterative experiments with clear evaluation criteria and baseline comparisons.
Models are wrapped in APIs or batch jobs and connected to your existing systems.
Ongoing tracking of model performance, with retraining as your data shifts.
Start small and only go further when the results justify it. Eligible projects start with a free 15-day MVP.
We review your data and the decision you want to improve, and tell you whether machine learning is worth it, with the reasons.
A first model trained on your real data and compared with your current method, so you see the accuracy before investing more.
Data pipelines, the model, an API or scheduled job, and the integration into your CRM, ERP, or web application.
Monitoring, retraining, and improvements for models we built, or for models your team already runs.
Predictions and recommendations grounded in your actual business metrics.
Pipelines built for monitoring and retraining, not one-off experiments.
A pragmatic path from prototype to production, not endless research cycles.
Common questions about our AI & Machine Learning services, cost, and how projects start.
It depends mostly on how ready your data is. A project with clean historical data and a clear target costs far less than one where data must be collected or labeled first. We start with a data readiness check and give a clear scope and estimate after it. Eligible projects start with a free 15-day MVP.
It depends on the problem. A forecasting model needs enough history to see seasonal patterns, and a classifier needs enough examples of each outcome. The data readiness check tells you whether you have enough. If you don't, we say so, and suggest what to start collecting.
The data check and a first baseline model come first, so you see early whether the approach works. The model moves to production once it clearly beats that baseline on your business metric. The full timeline is part of the estimate.
Machine learning predicts or classifies from your data, for example forecasting demand or scoring risk. Generative AI writes text and answers questions from documents. Many projects use both, and we also offer generative AI development services.
Yes. We connect to where your data already lives, such as PostgreSQL, MySQL, BigQuery, Snowflake, or exports from your tools, and build the pipelines around it instead of asking you to move everything.
Yes. Models are served through an API or run as scheduled jobs, and their results flow into your CRM (for example Salesforce), ERP, dashboards, or app, where your team already works.
We monitor predictions for drift, set up alerts, and build retraining pipelines, so the model is updated as your data changes instead of quietly getting worse.
You do. The trained model, training code, data pipelines, and documentation are handed over to you, so your team or another vendor can run and improve them.
Yes. We review the data, features, and evaluation, then improve accuracy, move the model to production, or add the monitoring and retraining it is missing.
We sign an NDA on request, work inside your cloud account where possible, and use only the data the model needs. Personal data can be masked or removed before training.
Generative AI development: custom AI chatbots, RAG knowledge assistants, AI agents, semantic search, and LLM-powered workflows built for production.
Learn moreSecure, scalable APIs and backend systems, cloud infrastructure on AWS, and CI/CD pipelines that make every release routine.
Learn moreCustom web application development using Next.js, React, TypeScript, and modern backend technologies for scalable digital products.
Learn moreShare a few details about what you're building and we'll reply within one business day. Eligible projects start with a free 15-day MVP.
