Machine Learning and Predictive Analytics Services

It depends on the problem more than on a fixed number. Forecasting usually benefits from at least two years of history to capture seasonality, while classification models need enough examples of the outcome you want to predict, such as churned customers or fraudulent transactions. Data relevance and quality matter as much as volume. In the discovery phase we profile your data and tell you honestly whether it supports a reliable model or what additional data would be needed.

We cannot promise a specific figure before seeing the data, and you should be cautious of anyone who does. What we commit to is measuring accuracy properly, on held-out data or through backtesting that simulates real use, and comparing it with your current method. You see the uplift over the baseline, error ranges and where the model performs weakly, so you can decide whether the improvement justifies deployment.

Costs are driven by data preparation effort, the number of models, integration requirements and whether real-time scoring is needed. A proof of value on one use case is usually a fixed-scope engagement of six to ten weeks. Production deployment with MLOps and integration is estimated separately once the proof of value confirms the approach. Ongoing costs cover cloud compute for training and scoring, and optional managed services for monitoring and retraining.

Every production model we deliver includes monitoring for input data drift, prediction distribution changes and, when ground truth arrives, actual accuracy. Alerts notify the team when metrics breach agreed thresholds. Retraining pipelines are automated and versioned, and new models are validated against the current one before promotion. Monthly or quarterly model reviews, included in our managed service, look at business impact as well as statistics.

Yes. We use interpretable models where regulation or trust requires it, and apply techniques such as SHAP values to explain more complex models. Each prediction can be accompanied by its main contributing factors, and we produce global explanations showing which features drive the model overall. This supports regulatory requirements, helps business users trust the output and often reveals data issues that improve the model further.

We design integration from the start. Batch predictions can be written to your data warehouse, CRM or ERP on a schedule, while real-time models are exposed as secure APIs with defined latency targets. We work with common platforms such as Salesforce, Dynamics 365, SAP, Snowflake and Databricks, as well as custom applications. Integration effort is identified during discovery so it is part of the plan, not a surprise.