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Core AI · Module 05 / Foresight

Forecast the
next quarter before it happens.

Forecasting, recommendations and anomaly detection engineered as a refined analytical surface — one runtime for demand, revenue, risk and operations.

Explore the moduleSee it in action
3.2%
MAPE · 90d demand
92%
prediction coverage
3×
faster decisions
clickripple · forecast · demand · sku_top-200
LIVEus-east
Forecast · 90 daysconfidence 92%
$8.42M
projected demand · next quarter
+12.4%
vs prior quarter
historical forecast 90% band anomaly
Recommendations
  • Increase safety stock · line-14
    +18%
  • Redirect promo · SKU-8821
    hold
  • Alert · anomaly on 07-12
    review
Feature importance
  • Seasonality82
  • Promo lift61
  • Weather34
  • Competitor px21
HomeCore AIPredictive AI & Machine Learning
What it does

The future doesn't need a dashboard. It needs a decision.

cr-forecast-2 turns transactional history, external signals and expert judgement into calibrated predictions with confidence bands, feature attribution and recommended actions — served wherever your team already works.

01
Calibrated forecasts
Confidence bands, MAPE and coverage measured on live production data, not vendor benchmarks.
02
Actionable recommendations
Every prediction carries a suggested action, expected impact and a rollback plan.
03
Anomaly awareness
Signals that fall outside the confidence band raise a typed event before humans notice.
How it works

One continuous flow from signal to outcome.

Every stage is instrumented, observable and safe to swap. No timelines. No boxes to connect by hand.

  1. Stage 1
    Ingest
    history + signals
    Transactions, calendars, weather, macro and internal expert priors are fused into one feature store.
  2. Stage 2
    Learn
    cr-forecast-2
    Hierarchical models produce forecasts at SKU, segment and portfolio level in one pass.
  3. Stage 3
    Explain
    attribution
    Feature importance is computed per prediction — no black-box outputs into production.
  4. Stage 4
    Recommend
    policy layer
    Predictions are turned into typed recommendations with expected value and guardrails.
  5. Stage 5
    Act
    systems of record
    Approved actions write back to ERP, planning and marketing surfaces with signed evidence.
Forecast studio

Every scenario,
quantified.

forecast · scenario planner
LIVEus-east
model · cr-forecast-2 · v14
Segments
  • EMEA · Retail12.4%
  • NA · Wholesale8.1%
  • APAC · DTC-2.4%
  • LATAM · Retail6.8%
  • EU · B2B3.2%
Projected · quarter
$8.42M
Δ vs base
—
MAPE
3.2%
Coverage
94%
Lead time
90d
Anomalies · 30d
  • 07-12
    demand spike · SKU-8821
  • 07-09
    supplier lead time drift
  • 07-02
    promo lift confirmed
Enterprise applications

Where predictive ai & machine learning earns its keep.

Retail & CPG
Retail & CPG
Case · 01 / 04

Demand forecasting across SKUs and regions.

AI capability
Hierarchical forecasting · promo lift · weather.
Business result
3.2% MAPE · −41% stock-out days.
Explore this scenario
Performance

Numbers that earn deployment.

3.2%
MAPE · demand
measured on 90-day rolling window
58%
Less fraud loss
real-time anomaly scoring
3×
Faster decisions
recommendation-first UI
99.1%
Precision · anomaly
human-in-the-loop review
Compatibility

Meets your stack where it lives.

Bring your own models, cloud and data. Everything speaks the same runtime contract.

Forecasting models
cr-forecast-2TimesFMChronosProphetXGBoostLightGBM
Feature store
SnowflakeDatabricksBigQueryFeastcr-features
Signals
WeatherMacro (FRED)CalendarsGoogle TrendsInternal priors
Action surfaces
SAP IBPAnaplano9BrazeCustom OMS
Frequently asked

Straight answers.

It depends on the signal. For retail demand we consistently ship 3–5% MAPE on 90-day horizons. Every forecast reports its own coverage and confidence bands — you should never trust a number without them.
Both. cr-forecast-2 blends gradient-boosted trees, hierarchical Bayesian models and time-series foundation models — chosen per problem, not per vendor preference.
Yes. Any Snowflake, Databricks or BigQuery table can be registered as a feature source. Feast is supported natively.
Every model ships with drift detection on inputs and residuals. Retraining is automatic, but every promotion goes through a shadow window with human sign-off.
Batch and streaming inference run inside your VPC or ours. Sub-50ms scoring is available for online use cases.
Predictive AI & Machine Learning

Deploy this module in your enterprise.

Talk to an AI engineer. Ship a working proof-of-value in weeks, not quarters.

Book a working session