AI & Automation
Forecasting & Analytics
Turn your history into foresight — AI-supported forecasts for demand, sales, cash and inventory, plus analytics that surface the patterns behind them, built from your own data.
Forecasting & Analytics: the overview
Most planning still runs on intuition and a spreadsheet extrapolating last year. That misses the patterns hidden in your data — seasonality, trends, the signals that precede a change — and leads to stockouts, overstock, cash surprises and missed targets. AI forecasting turns your own history into projections you can plan against.
We build forecasting and analytics around the decisions you make: how much to buy, staff or produce; what sales and cash to expect; which customers or products are trending. Using your historical and current data, we build models that project the likely outcomes with ranges, test them against your actual history and a simple baseline, and deliver them in dashboards your team actually uses.
The result is better-informed decisions with less guesswork, and earlier warning when something shifts. Because the forecasts are built on your data, they reflect your business specifically, and they can be retrained as more data arrives, with accuracy tracked against actuals.
What Forecasting & Analytics includes
Demand & Sales Forecasting
Project demand and sales by product, channel and period, with confidence ranges.
Inventory & Supply Planning
Forecast stock needs to help reduce stockouts and overstock.
Cash-Flow Forecasting
Project cash position and highlight upcoming pressure points.
Trend & Driver Analytics
Surface the patterns and drivers behind the numbers, not just totals.
Dashboards & Alerts
Forecasts in business dashboards, with alerts when actuals diverge.
Data Integration
Built on your historical and current data from ERP, sales and operations systems.
Who Forecasting & Analytics is built for
Retail & distribution
Forecast demand and stock to cut both stockouts and dead inventory.
Finance & leadership
Project sales and cash to plan with fewer surprises.
Operations & manufacturing
Plan capacity, staffing and production against projected demand.
Data-rich businesses
Put the history you already hold to work in planning.
Is AI forecasting and analytics the right choice?
A good fit when
- You have a few years of reasonably clean history for what you want to forecast.
- A recurring planning decision (purchasing, staffing, production or cash) depends on the forecast.
- Someone will use the forecast and compare it with actuals.
- Your data sits in ERP, sales or operations systems that can be extracted regularly.
Consider another option when
- You need reporting on what has happened rather than projections; business dashboards come first.
- History is short, or the business changed fundamentally recently; simple rules or scenario planning may be more honest.
- Your ERP or planning tool already has forecasting that suits your volumes; try it first.
Usually in a first release
- A data assessment: history length, gaps and outliers in the series you want to forecast
- One forecast (for example demand by product family) at an agreed level and horizon
- Back-testing against your actual history, reported against a simple baseline such as last year’s figures
- Forecasts with ranges in a dashboard, with alerts when actuals diverge
Outside the first release unless agreed
- Additional forecasts, levels or horizons beyond the agreed scope
- Automatic purchasing or production decisions (forecasts inform the people who decide)
- Cleaning historical data in the source systems
- External data feeds such as weather or market prices, unless agreed
Anything outside the approved scope is reviewed and agreed before work begins. See how we work.
Data, controls, responsibilities and ownership
Data migration and integrations
- History is extracted from your ERP, sales or operations systems through their APIs, database views or scheduled exports, with refresh frequency agreed per source.
- Forecast quality depends on data quality: we report gaps, one-off events, product code changes and outliers before modelling, and agree how each is treated.
- Most forecasting models run in your own cloud account and do not need a third-party language model. Where an AI service is used, for example to explain drivers in plain language, we document which data it receives.
- Forecasts can be written back to your planning or ERP system where it accepts imports, so planners do not re-key them.
Roles, approvals and audit
- Baseline comparison
- Each model is compared with a simple baseline, so you can see whether it actually beats last year’s figures or a moving average.
- Human decisions
- Forecasts inform buyers, planners and finance; they do not place orders or change budgets on their own.
- Accuracy tracking
- Forecast-versus-actual error is tracked over time, with an alert when accuracy drifts.
- Model change control
- Retraining and model changes are versioned and re-tested on the same history before release.
What we need from your team
- Access to the historical data, and someone who knows its quirks (promotions, stockouts, code changes).
- Planners or finance users who agree the forecast level, horizon and accuracy measure.
- Feedback on forecasts versus reality during the pilot.
- Cloud and storage accounts in your organization’s name.
Ownership, support and running costs
- Project code, model configuration, analytics and dashboards transfer to you on full payment, and your data is yours throughout. Any third-party model or service used remains the provider’s, under its terms; see our IP and ownership policy.
- Model provider, hosting and storage accounts are set up in your organization’s name where possible, so usage, retention settings and terms sit between you and the provider.
- Running costs by category: compute for training and scheduled forecasts, data storage, dashboard hosting, any model or API usage, accuracy monitoring, and periodic retraining. Amounts depend on data volume, refresh frequency and hosting.
- Forecasts need retraining as your business changes; a maintenance and support agreement covers retraining and re-testing.
Related reading for this decision
- Business dashboards
Reliable reporting on what has happened usually comes before forecasting what will.
- Business process intelligence
For understanding why operations behave as they do, not just projecting totals.
- ERP integration services
Forecasts depend on clean, regular extraction from your ERP.
- Security and data protection
How commercial and financial data is stored, accessed and hosted.
How we deliver Forecasting & Analytics
Custom software built around the way your business works. Five steps, with a free pilot of 2 to 3 key modules before the full build.
Step 1: Understand
We learn how your business works.
Your requirements, workflow, challenges and goals, understood before anything is recommended.
Step 2: Plan
We design the right solution around your workflow.
Modules, workflows, roles, approvals, reports and integrations, agreed before development.
Step 3: Select Technology
Choose the right technical foundation.
Technology options matched to your users, security, budget and growth, not one fixed stack.
- Free pilot
Step 4: Pilot
Test our work before full project development.
Free. You choose 2 to 3 key modules and we build them first, so you can judge our work.
The full project starts only after you approve the pilot.
- Full project
Step 5: Build & Scale
From approved pilot to complete digital system.
Full development, testing, deployment, training and support, built to grow with you.
Guides for this decision
In-depth, practical reading for teams planning this kind of project.
- Guide · 9 min readBusiness Dashboards People Actually Use: KPI Definitions, Data Sources and RefreshA dashboard gets used when every tile supports a decision, every KPI has a written definition and the data refreshes as fast as the decision needs. This guide gives a KPI template, refresh trade-offs and a worked distributor example.Read the guide
- Guide · 9 min readHow to Evaluate an AI Pilot Before Rollout: Test Sets, Metrics and a Go/No-Go ScorecardAn AI pilot is ready for rollout when it meets acceptance criteria you set in advance, on a test set built from your own data, including the hard cases. This guide covers metrics by task type, human review thresholds, failure-mode testing and a go/no-go scorecard.Read the guide
Forecasting & Analytics FAQ
Accuracy depends on the quality and history of your data and how predictable the outcome is. We back-test models against your actual history to show their accuracy honestly, compare them with a simple baseline, present forecasts with ranges rather than false precision, and track accuracy against actuals after go-live.
Enough history to capture your patterns — typically a couple of years for seasonal businesses, less for stable ones. In discovery we assess your data and tell you honestly whether forecasting will be reliable or needs more history first.
Demand and sales, inventory and supply needs, cash flow, capacity and staffing, and customer or product trends — outcomes with enough historical signal in your data.
Yes. We deliver forecasts and analytics in dashboards and can feed them back into your planning and ERP systems where they accept imports, with alerts when actuals diverge from forecast.
The pilot back-tests a forecast on your own history and shows how it compares with a simple baseline, such as last year’s figures. It does not prove future accuracy: conditions change, and a model that tested well can drift. That is why forecasts carry ranges, and accuracy is tracked against actuals after go-live.
Project code, model configuration, analytics and dashboards transfer to you on full payment, and your data is yours throughout. Third-party foundation models remain the provider’s and are used under the provider’s terms, and open-source components stay under their own licences, as our IP and ownership policy sets out.
See working software before you commit
Before you commit to the full project, we build 2 to 3 of your key modules as working software, free of charge. Your team tests the pilot, and the full build starts only after you approve it.
See how the free pilot worksUnderstand
We learn your requirements and how your organisation works today.
Select pilot modules
Together we choose 2 to 3 key modules that prove the solution.
Build the working pilot
We build those modules as real, working software, free of charge.
You test it
Your team uses the pilot. The full project starts only after you approve it.
Start a conversation
Tell us how your business works.
Describe what is slowing your team down. We will help you work out what to build, and how a free pilot lets you judge our work before the full project.
Prefer WhatsApp? Start a chatWhat happens next
You send a short brief
The problem, the people involved and any target date. A senior engineer replies within 4 business hours.
We understand your workflow
A first call about how your business works today. An NDA can be signed before you share details.
You test a free pilot
You choose 2 to 3 key modules and we build them first, so you judge real software before the full project.