AI OCR for gas station operations
- 20K+
- transactions processed in a single day
Demand Forecasting Software | ML Models
Demand Forecasting Software
RaftLabs builds custom demand forecasting models trained on your historical order, sales, and supply data. Time-series ML models that account for seasonality, promotions, and external signals, integrated directly into your inventory, procurement, or planning systems so forecasts translate into actions rather than spreadsheet exports.
We start with a data audit: we assess your historical data quality, coverage, and granularity before committing to a model approach. If your data supports a reliable forecast, we tell you what accuracy is achievable. If it does not, we tell you that too rather than delivering a model that looks good in demo and fails in production.
Time-series ML models trained on your actual order and sales history
Seasonality, promotional uplift, and external signal integration built in
Forecast delivery direct to your ERP, inventory, or planning system
Forecast accuracy monitoring and automated retraining as patterns shift
Recent outcomes
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6× deeper insights
Text-based interviews converted to automated phone calls
AI Automation · Ops
20k+ txns day one
Manual invoice OCR across 40+ gas stations
Loyalty · Retail
1,062 users in 4 weeks
SuperValu & Centra loyalty platform with receipt validation
SaaS · Logistics
2,000+ shipments yr 1
Multi-carrier shipping hub for Indonesian eCommerce
4.9
on ClutchThe problem
Sound familiar?
Your buyers are using gut feel and spreadsheet averages to set purchase orders because you have no reliable demand signal?
You have a forecasting tool but it does not account for your promotions or seasonality so your team ignores it?
Short answer
RaftLabs builds custom demand forecasting models using time-series machine learning trained on your historical order and sales data. We integrate forecast outputs into ERP and inventory systems and include accuracy monitoring and automated retraining so the model stays reliable as demand patterns shift. A single-category model with ERP integration typically runs $20,000 to $60,000.
Key takeaways
- RaftLabs builds custom demand forecasting models trained on historical order and sales data using time-series machine learning.
- Models account for seasonality, promotional uplift, and external signals such as weather, events, and commodity indices.
- Forecast outputs are integrated directly into ERP, inventory, and procurement systems so predictions translate into purchase orders.
- Automated accuracy monitoring tracks MAPE, WMAPE, and Bias per SKU and category with automated retraining as demand patterns shift.
- A single-category model with ERP integration typically costs $20,000 to $60,000; multi-category systems with full ERP integration range from $60,000 to $150,000.
- Every engagement starts with a data audit to assess historical data quality before committing to a model approach.
Trusted by
Demand forecasting sounds straightforward until you try to build it: you have years of order data, but promotions inflated some months, supply constraints suppressed others, and a one-off contract skewed a quarter that looks anomalous but was not. A naive model trained on that history will produce forecasts that are systematically wrong in ways that are hard to diagnose. Experienced inventory planners know this, which is why they override model outputs with judgement, and why forecasting tools often end up unused.
RaftLabs builds demand forecasting models that account for the structure of your actual business: promotional uplift, seasonality, trend, and the external signals that move your demand independently of what you control. We start by understanding your data, your planning cycle, and what decisions the forecast needs to support, and we define the accuracy benchmark up front so you know what you are getting before you commit to building.
According to McKinsey, organisations implementing AI-driven demand forecasting can reduce forecast errors by 30 to 50 percent, with downstream effects including 20 to 50 percent inventory reductions and up to 65 percent fewer lost sales from stockouts. The gains do not come from the model alone. They come from integrating forecast outputs directly into procurement and replenishment workflows so the signal actually changes purchasing decisions.
Capabilities
What we build
- 01Time-series demand forecasting models
ML models trained on your historical order and sales data, with time-series methods matched to your data rather than one algorithm for every problem: strong seasonality, feature-rich tabular forecasting, non-linear patterns, and ensembles where they beat any single model. Accuracy is evaluated on held-out data using MAPE, WMAPE, and Bias, the metrics your planning team actually uses.
- Built with
- Prophet · LightGBM · LSTM
- 02SKU-level and aggregate forecasting
Forecast models at the granularity your planning process requires: SKU, product family, category, or location, built as a coherent hierarchy so SKU forecasts sum exactly to category totals and your replenishment and financial plans agree without manual fixes. Sparse long-tail SKUs with intermittent demand get intermittent-demand methods instead of classical models that break down, and we document the lowest granularity your data reliably supports before we build.
- Built with
- MinT reconciliation · Croston's method
- 03Seasonality and promotional uplift modelling
Explicit seasonality decomposition using Fourier terms, calendar features, and holiday indicators, built from your actual trading history rather than generic retail curves. Promotional uplift models learn from your past promotions, so discount depth, promotion type, channel, and duration all inform the prediction, and planned promotions are injected as future model inputs so next month's promotion reads as expected uplift, not a demand surprise.
- 04External signal integration (weather, events)
Integration of external demand signals that move your sales independently of what you control, the signals a model trained on internal data alone will miss. Every candidate signal is tested against your historical demand first, and only those that improve held-out accuracy make the cut, drawing on weather, local event calendars, commodity price indices, and search trends. We report the accuracy gain each signal provides, not just that it was added.
- Built with
- Open-Meteo · NOAA · Google Trends
- 05Forecast delivery to inventory and ERP systems
Automated forecast delivery to your ERP demand planning module, inventory system, or procurement tool, so predictions become purchase orders rather than sitting in a separate analytics tool. API integration for the major ERPs, plus scheduled file delivery or a queryable database table for systems without import APIs. Planners can override forecast values in a web UI, with every override logged and fed back into retraining to surface model blind spots.
- Built with
- SAP IBP · Oracle · Dynamics 365
- 06Forecast accuracy monitoring and retraining
Automated accuracy tracking that compares forecasts against actual demand as orders arrive, catching model drift before it affects your purchasing decisions. MAPE, WMAPE, and Bias are tracked per SKU and category with control-chart alerts that fire on signal, not noise, and retraining runs weekly for fast-moving categories, monthly for stable ones, with on-trigger retrains for structural breaks. Every model is versioned, so rollback is a one-step operation.
- Built with
- MLflow
Your forecast is only as good as the model behind it.
Tell us about your current planning process, your data history, and the decisions the forecast needs to support. We will assess whether a custom model is worth building and what accuracy you can realistically expect.
Related predictive analytics services
Predictive Analytics, overview of our full predictive analytics practice
Churn Prediction, customer churn risk models integrated with your CRM
Fraud Detection, real-time and batch fraud scoring for transactions and claims
Predictive Maintenance, equipment failure prediction from sensor data
What clients say
What our clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
Charles E.
USAEntrepreneur at Aggie Technologies
All of the sprints were completed on schedule and on budget. We highly recommend RaftLabs!
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Read moreFrequently asked questions
As a starting point, two years of transactional history gives a time-series model enough data to identify annual seasonality patterns reliably. One year can be sufficient if your business does not have strong seasonal variation. Less than 12 months of data makes it very difficult to separate genuine demand patterns from noise, and any model built on that data will be unreliable for planning purposes. If your historical data is limited, we can supplement it with external signals, market indices, weather data, economic indicators, to fill gaps, but we will tell you clearly what accuracy range is achievable before you commit to building.
New products present a genuine cold-start problem: without historical demand, a time-series model has nothing to learn from. We address this through a combination of approaches depending on the product category. For products similar to existing SKUs, we use attribute-based similarity to transfer demand patterns from comparable historical products. For genuinely novel products, we model the launch curve using data from comparable historical launches in your catalogue or industry benchmarks. We are transparent about the higher uncertainty on new product forecasts and build that uncertainty into the confidence intervals the model reports.
The integration approach depends on your ERP's API capabilities. For systems like SAP, Oracle, and Microsoft Dynamics, we use standard API endpoints to write forecast data into the demand planning module on a configurable schedule, daily, weekly, or triggered by model update. For systems with limited API access, we write forecast output to a format your planning team already imports, typically CSV or Excel in the structure your system expects. We build and test the integration as part of the engagement, not as a separate phase, so forecast delivery is working in your real system before handover.
A single-category demand forecasting model, one product line or business unit, with seasonal and promotional adjustment, delivered to your planning tool, typically runs $20,000 to $60,000. A multi-category system with SKU-level granularity, external signal integration, and full ERP integration ranges from $60,000 to $150,000. We scope the engagement by reviewing your data, defining the forecast granularity, and agreeing the accuracy benchmark before providing a fixed-cost quote.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope Demand Forecasting Software in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.