Demand and sales forecasting
Demand forecasting at the SKU, store or region level, accounting for seasonality, promotions, holidays and price changes, with hierarchical reconciliation so totals add up across levels.
Machine Learning & Data
Predictive analytics services that see what's next.
Our predictive analytics services turn the history sitting in your warehouse and ERP into forward-looking estimates your planners can act on. We build forecasts and risk scores with honest uncertainty ranges and deliver them where decisions are actually made.
- Forecasts with confidence ranges
- Built on your history
- Delivered into BI tools
01 Overview
Better decisions from the data you already collect
Dashboards tell you what happened last month. Planning, purchasing and staffing all depend on what will happen next month, and too often that number comes from a spreadsheet extrapolation and a gut feeling. Predictive modeling replaces the guesswork with estimates grounded in your own patterns: seasonality, promotions, pricing changes, weather or macro signals where they genuinely help. Just as important, a good forecast says how sure it is, so teams can plan buffers instead of treating one number as gospel.
We deliver predictive analytics services with the end user in mind. A demand forecasting model is only useful if the supply planner trusts it and can see it inside the tool they already open each morning. So we pair statistical and machine learning methods with backtesting against your past periods, plain-language explanations of key drivers, and delivery into Power BI, Tableau, Looker or your ERP. Models are retrained on a schedule so accuracy keeps pace as your business changes.
02 Capabilities
Predictive Analytics capabilities.
End-to-end Predictive Analytics: strategy, engineering and the production operations that keep it working.
Time series modeling
Time series forecasting using Prophet, ARIMA-family models, gradient boosting and deep learning, selected through rigorous backtesting rather than assumptions about which method should win.
Customer behavior prediction
Customer lifetime value prediction, purchase propensity and churn likelihood models that help marketing and success teams decide where attention and budget will pay back most.
Risk scoring
Risk scoring models for credit, payment default, claims or supplier failure, built with interpretable features so reviewers can understand and challenge every score.
Predictive maintenance
Predictive maintenance analytics that estimate failure likelihood or remaining useful life from sensor, maintenance log and usage data, helping teams schedule repairs before breakdowns.
Scenario and what-if analysis
Interactive scenario tools that show how forecasts change under different prices, budgets or supply constraints, supporting planning conversations with numbers rather than opinions.
03 How we deliver
From first call to production.
Every Predictive Analytics engagement follows the same five stages, so you see value early and risk stays low.
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01
Discover Typically 1–2 weeks
AI readiness assessment, data audit and use-case discovery. We rank opportunities by business value and feasibility and agree on measurable success criteria.
Output: Roadmap · data audit · success metrics
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02
Prototype Typically 2–4 weeks
A working proof of concept on your real data, scored against an evaluation set — so you see accuracy, latency and cost before committing to a full build.
Output: PoC · evaluation report · build estimate
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03
Build Sprint-based
Production engineering: data pipelines, model training or fine-tuning, RAG retrieval, agent tooling, APIs, guardrails and the user interface your team will actually use.
Output: Production code · APIs · guardrails
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04
Deploy Cloud, on-prem or edge
Secure deployment on AWS, Azure, Google Cloud, your private infrastructure or edge devices, with CI/CD, LLMOps and MLOps pipelines from the first release.
Output: CI/CD · infrastructure as code · rollback
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05
Operate Ongoing
Monitoring, drift detection, retraining and cost optimization keep models accurate and affordable as your data, users and the AI landscape change.
Output: Monitoring · retraining · cost reports
04 Use cases
Where it pays off.
Practical, high-value applications we design and build for clients across industries.
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UC-01
Inventory and replenishment planning
Forecast demand per product and location to set reorder points and safety stock, reducing both stockouts on fast movers and cash tied up in slow inventory.
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UC-02
Workforce and capacity scheduling
Predict call volumes, deliveries, patient arrivals or store traffic by hour, so managers can staff shifts to match expected demand rather than last year's roster.
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UC-03
Equipment failure prevention
Combine sensor readings and maintenance history to flag assets trending toward failure, letting maintenance teams plan interventions during scheduled downtime.
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UC-04
Revenue and cash flow outlook
Project revenue, renewals and receivables with uncertainty bands, giving finance teams a rolling outlook that updates automatically as new bookings and payments arrive.
05 Technology
The stack we use.
Proven, well-supported tools chosen per project for accuracy, cost and long-term maintainability.
Forecasting and modeling
- Prophet
- statsmodels
- Darts
- XGBoost
- LightGBM
Data platforms
- Snowflake
- Google BigQuery
- Databricks
- dbt
Delivery
- Power BI
- Tableau
- Looker
- Streamlit
- Apache Airflow
06 Industries
Built for your sector.
We deliver Predictive Analytics for regulated and fast-moving industries alike.
What is the difference between predictive analytics and machine learning?
Predictive analytics is the business goal: estimating future outcomes to guide decisions. Machine learning is one family of techniques for getting there, alongside classical statistics and time series methods. Our predictive analytics services use whichever approach backtests best on your data, and the focus stays on delivering forecasts people use, not on the algorithm itself.
How much historical data is needed for demand forecasting?
Ideally enough to capture your seasonal cycles at least a couple of times, so weekly or annual patterns can be learned. Newer products with little history can borrow patterns from similar items or categories. We review your data early and explain which forecasts are well supported and which will carry wider uncertainty.
How do you measure forecast accuracy?
We backtest by simulating past forecasts: training on data up to a point, predicting the following period, and comparing against what actually happened, repeated across many windows. We report error metrics that match your decision, such as weighted error by revenue, and always compare against your current method so the improvement is clear and honest.
Can predictive models include external data like weather or holidays?
Yes. Holidays, local events, weather, pricing, marketing calendars and economic indicators can all be added as features. We test each one to confirm it actually improves backtested accuracy before keeping it, since extra signals add maintenance and data costs. Only inputs that earn their place stay in the production model.
Where will our team see the predictions?
Wherever decisions happen. We commonly write predictions back to your data warehouse and surface them in Power BI, Tableau or Looker, push them into ERP or planning systems, or expose them through an API. For exploratory planning, we can also build a lightweight internal app for scenario analysis.