From historical sales to executable forecasts, all in one platform — it auto-selects the best model, breaks down every factor’s impact, and lets AI explain the result in plain language. Forecasts can be reviewed and adjusted before release — the final call is always yours.
No one is sure which algorithm to use — one fixed model for every SKU makes forecasts hit or miss; the model gives a number but can’t say why, so the business doesn’t fully trust it; and you worry about being fooled by variables only known after the fact, or about AI scrambling the numbers.
Forecasting is no longer the manual grind of tuning a model per SKU — Planforce puts the whole chain, from ingesting history to publishing forecasts, into one engine that auto-selects models, stays explainable, and keeps a full audit trail.
Reference backtest: one enterprise’s sales forecasting — ingest historical sales → auto-benchmark 20+ models → factor attribution & predictability assessment → AI-generated narrative; overall accuracy improved from 86.4% to 92.1% versus the statistical baseline (single-customer backtest, specific window, not a general guarantee).
The platform auto-benchmarks 20+ models per sales series and picks the best by backtest, so you don’t need to know the algorithms.
On one enterprise’s backtest, overall accuracy rose from 86.4% to 92.1% and WAPE dropped from 13.6% to 7.9% versus the statistical baseline (single-customer figure, specific window, not a general guarantee); actual results depend on your data.
No. AI only explains the results in plain language; all numbers come from the models and factor contributions, AI is forced to quote them verbatim with server-side validation, and AI never touches the forecast numbers themselves.
Yes. Every forecast is decomposed into each factor’s direction and contribution, each factor is graded for predictability, and “trap factors” that cheat with future values are filtered out automatically.
