Try It Live
Choose a metric (Sales Revenue, Labour Costs, Staffing Headcount, or your own), load the pre-filled sample data or enter your own, add any upcoming events or campaigns, and hit Generate Forecast. The dashboard renders a line chart showing historical actuals alongside a projected trend, a confidence band, an AI insight summary, and a period-by-period breakdown table.
How It Works
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Choose your metric — Sales Revenue, Labour Costs, Operating Costs, Staffing Headcount, Customer Volume, or any custom metric with a free-text label and unit.
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Enter historical data — Use the editable table pre-loaded with 12 months of sample retail data, or upload your own two-column CSV (Period, Value). Any frequency works: weekly, monthly, or quarterly.
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Add context — Specify upcoming campaigns, trade shows, product launches, or other events with an expected impact level. The AI weighs these against the statistical baseline.
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Forecast generates in two steps:
- A statistical baseline is computed server-side using linear regression on the historical data, with a 90% confidence band derived from the model's residual error.
- GPT-4o-mini then applies per-period adjustment multipliers based on your events, built-in US holiday calendar, and any seasonality visible in the historical pattern. The AI explains every adjustment.
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Dashboard renders — A static SVG line chart shows historical actuals (solid line) and the adjusted forecast (dashed line) with confidence band. Below it: an AI insight paragraph, a key-factors summary, and a scrollable period table showing baseline vs. adjusted values and the AI's reasoning for each period.
What This Demonstrates
| Capability | Description |
|---|---|
| Hybrid forecasting | Statistical baseline + AI qualitative adjustment — more reliable than pure LLM number generation |
| Structured AI output | GPT-4o-mini returns JSON with per-period multipliers, reasoning, and narrative |
| Event-aware forecasting | Named events with impact levels shift the forecast for affected periods |
| Holiday calendar | US holidays automatically factored into the AI's adjustment logic |
| Flexible data input | Manual table or CSV upload; weekly, monthly, or quarterly data |
| Static SVG dashboard | Rendered server-computed data — no charting library dependency |
| Explainability | Every forecast adjustment includes a plain-English reason |
Use Cases
- Monthly and quarterly revenue forecasting for sales leadership
- Labor cost and headcount planning for HR and finance teams
- Demand forecasting for inventory management and supply chain optimization
- Capital expenditure planning and cash flow projection for CFOs
- Customer volume projections for capacity planning and staffing
- Marketing campaign ROI forecasting and budget allocation
From Demo to Production
This demo shows how a hybrid statistical + AI model can produce an explainable, event-aware forecast from a single data series. A production deployment takes it further by connecting to live data sources, running multiple forecast models in parallel, and delivering results to the tools your team already uses.
Real-World Challenges
| Challenge | Why It's Hard | How to Solve It |
|---|---|---|
| Data quality and missing periods | Historical data often has gaps, outliers, or regime changes (COVID, M&A, product launches) that distort the baseline | Implement automated data cleaning, outlier detection, and regime-change markers that the model can account for |
| Forecast accuracy expectations | Leadership expects precise numbers; models produce probability ranges — the gap creates trust issues | Educate stakeholders on confidence intervals; track and publish accuracy metrics (MAPE, bias) over time to build credibility |
| Conflicting departmental forecasts | Sales, finance, and operations each produce their own forecast using different assumptions | Establish a single forecasting engine as the baseline; layer departmental adjustments transparently on top |
| Incorporating qualitative intelligence | Sales rep gut feel, market rumors, and competitive moves don't fit neatly into a statistical model | Provide structured input fields for qualitative events (like this demo does) and weight them against the statistical baseline |
| Model drift and retraining cadence | Forecast accuracy degrades as market conditions change; models need regular recalibration | Automate accuracy monitoring and trigger retraining when MAPE exceeds a threshold; retrain monthly at minimum |
Cost Estimates
| Line Item | Small (Single Team) | Mid-Market (Finance Dept) | Enterprise (Multi-BU) |
|---|---|---|---|
| AI API + statistical compute | $30–100/mo | $100–400/mo | $400–1,500/mo |
| Data warehouse / ERP integration | $50–200/mo | $200–800/mo | $800–3,000/mo |
| Model maintenance (labor) | $100–200/mo | $200–500/mo | $500–1,500/mo |
| Hosting & infrastructure | $0–20/mo | $20–100/mo | $100–500/mo |
| Total monthly | $100–400 | $400–1,500 | $1,500–5,500 |
ROI Definition
- Primary metric: Forecast accuracy improvement — target MAPE (Mean Absolute Percentage Error) reduction of 15–30% vs. existing process
- Secondary metrics: Time saved on forecast preparation, reduction in over/understock costs, improved cash flow predictability
- Break-even timeline: 1–3 months depending on scale and current forecast maturity
- Example: A retailer with $20M annual revenue improving forecast accuracy by 20% reduces overstock and understock costs by 3–5% of revenue = $600K–$1M/year saved vs. ~$15K/year in tool costs.
Technology Stack
- AI Model: OpenAI GPT-4o-mini
- Statistics: Linear regression with RMSE-based confidence intervals (computed server-side in TypeScript, no external library)
- Frontend: React client component — multi-step form, editable data table, CSV upload, static SVG chart
- Backend: Next.js API route (serverless)
- Chart: Hand-built SVG (historical line + forecast line + confidence band + event markers)
Want This for Your Business?
A production deployment connects directly to your source systems — POS, ERP (SAP, NetSuite), HRIS (ADP, Workday), or data warehouse — and runs forecasts on a schedule. Results are pushed to dashboards in Power BI, Looker, or Tableau, or delivered as weekly email summaries to managers. The model retrains continuously as new actuals come in. A full deployment typically takes 2–3 weeks and starts at $3,500.