The best of econometrics, optimised by AI, enriched with real-world data.
The Problem with Modern Forecasting
Enterprise forecasting faces a fundamental tension. Traditional statistical methods—ARIMA, Prophet, regression analysis—deliver reliable, interpretable results but require significant expertise to configure. Meanwhile, AI-first solutions promise simplicity but produce expensive, unexplainable outputs that finance teams cannot trust for board presentations or regulatory compliance.
Most "AI forecasting" tools are wrappers around large language models. They query GPT for every prediction, burning through API costs while producing results that cannot be audited, reproduced, or explained. When a CFO asks "why does the model predict a 15% decline in Q2?", these tools offer no answer.
Ordeen takes a different approach.
Statistics First, AI for the Magic
Ordeen's forecasting platform is built on a core principle: verified data and statistical rigour form the foundation; AI enhances the edges.
The platform employs a four-stage analytical pipeline:
Stage 1: Data Integration Connect directly to SAP Business One (Finance, Sales, Inventory, HR, Procurement, Manufacturing modules) or upload CSV files. The system automatically detects date columns, numeric variables, and data quality issues. SAP integration pulls verified transactional data—not estimates, not projections, but actual recorded business activity.
Stage 2: Econometric Analysis Before any AI touches the data, Ordeen runs comprehensive statistical analysis:
Correlation screening identifies which variables genuinely relate to the target metric
Multicollinearity detection (VIF analysis) prevents redundant variables from distorting results
Stepwise selection and LASSO regularization determine which variables earn their place in the model
Stationarity testing (Augmented Dickey-Fuller) ensures time series assumptions hold
Model comparison evaluates Linear Regression, ARIMA, and Prophet, selecting the best fit by AIC/BIC criteria
Every variable selected comes with a p-value. Every variable rejected comes with a reason. The model equation is displayed explicitly—no black boxes.
Stage 3: Forecasting with Constraints The platform generates forecasts with proper confidence intervals. Crucially, it applies business-logic constraints: sales forecasts cannot go negative; inventory predictions respect physical limits. These aren't afterthought clamps—they're built into the model structure.
Stage 4: AI Augmentation Only after the statistical foundation is solid does AI enter. Users can request AI-generated augmented variables: lagged values, moving averages, seasonal indicators, or derived ratios. The AI suggests variables based on the existing data structure—but every suggestion is computed deterministically and fed back through the statistical pipeline for validation.
The result: AI proposes, statistics disposes. If an AI-suggested variable doesn't improve the model's explanatory power, it's rejected with the same rigour as any other candidate.
Why This Architecture Matters
Trustworthiness
Every forecast can be explained. The R² score shows how much variance the model captures. The coefficient table shows exactly how each driver affects the target. When the board asks questions, analysts have answers backed by statistical significance tests, not AI confidence scores.
Reliability
Statistical methods are deterministic. Run the same data twice, get the same forecast. No temperature parameters, no prompt sensitivity, no hallucinations. The underlying mathematics has been validated across decades of academic research and practical application.
Cost Effectiveness
The AI-first approach to forecasting burns through £500+ per month in API calls for a single client's projections. Ordeen's architecture reduces this to under £50—a 90%+ cost reduction—because AI is used sparingly and strategically, not as a substitute for proper analysis.
A typical enterprise forecast analyses 15-50 variables across 24+ months of history. Running this through GPT-4 repeatedly is expensive and unnecessary. Running it through statsmodels, Prophet, and scikit-learn is essentially free. AI adds value at the margins: suggesting which derived variables might help, detecting anomalies that merit investigation, generating natural-language summaries of results.
Enterprise Integration
Ordeen connects where enterprise data lives. SAP Business One integration pulls directly from OData APIs across six modules. The platform handles SAP's date formats, numeric string conventions, and hierarchical data structures automatically.
For organisations without SAP, CSV upload supports any tabular time series data. The system auto-detects schemas, validates data quality, and flags potential issues before analysis begins.
Once connected, forecasts refresh on schedule. No manual re-runs, no stale predictions. The statistical models update as new actuals arrive, maintaining accuracy without analyst intervention.
The Ordeen Principle
Across all Ordeen products—contract analysis, travel planning, forecasting, brand matching—the same architecture applies:
Database and templates first: Pre-computed, verified, deterministic
Statistical analysis second: Free, fast, reproducible
Verified third-party APIs third: Weather data, economic indicators, market prices
Rule-based logic fourth: Regex, business rules, constraint validation
AI last: Only for genuine intelligence tasks that cannot be solved deterministically
This isn't AI scepticism—it's AI discipline. Large language models excel at natural language understanding, creative synthesis, and pattern recognition in unstructured data. They're poor substitutes for linear algebra, time series decomposition, and hypothesis testing.
Ordeen uses each tool for what it does best. The result is forecasting that enterprises can trust, explain, and afford.
