Predictive Analytics and Forecasting

Pricing depends on data complexity, use case scope, model count, and engagement model. We offer fixed-cost projects, nearshore teams, dedicated teams, and custom quotations. Most engagements are scoped per project with transparent, upfront pricing including expected infrastructure costs.

Four primary models: Fixed Cost Projects for well-defined scope, Nearshore Teams for time-zone-aligned delivery, Dedicated Teams for long-term analytics investment, and Custom Quotations for complex, regulated, or hybrid engagements. We'll recommend the right fit during discovery.

It depends on the use case. For most business forecasting we need 12+ months of historical data, ideally with relevant external signals (promotions, seasonality, events). We start every engagement with a data audit so you know upfront whether your data is sufficient, and what to fix if it isn't.

We're algorithm-agnostic and recommend models based on use case — classical statistical models (ARIMA, Prophet), gradient boosting (XGBoost, LightGBM), deep learning (LSTM, transformers), and AutoML — chosen based on data characteristics, accuracy targets, and explainability requirements.

Yes — explainability is built into every engagement. We use SHAP, LIME, and model cards to show per-prediction reasoning, feature importance, and decision logic — making models trustworthy for executives and audit-ready for regulated industries.

Every model we ship includes drift detection, accuracy monitoring, and automated retraining triggers. We track distribution shifts, performance metrics, and data quality continuously — and retrain models before accuracy degrades meaningfully.

Yes — model audits and accuracy improvements are common engagements. We benchmark your current model, identify gaps in features, training data, or architecture, and ship a hardened version with measurable accuracy lift.

Yes. Data privacy and compliance are first-class concerns. We support private VPC deployments, encrypted data pipelines, audit logging, and compliance frameworks including HIPAA, GDPR, SOC 2, and PCI DSS — with full audit trails for regulated forecasting use cases.

Yes. You own 100% of the models, code, training pipelines, and documentation. No vendor lock-in. Your forecasting codebase lives in your GitHub organization from day one and can be operated entirely by your team after handoff.

Yes. Custom integrations are a core part of our work. We build reliable, observable connectors between your forecasting models and existing systems — CRMs, ERPs, data warehouses, BI dashboards, and internal APIs — so predictions actually drive decisions.