Connect your forecasting stack
Connect code, sales, catalog, inventory, prices, promotions, plans, metrics, and forecast history.
Demand forecasting
The applied science factory for improving demand forecasts
Pavo learns how your forecasting system works across sales, products, plans, and past forecasts. It builds the evaluation and runs projects to improve accuracy at each planning horizon.
Current forecast stack
models · horizons · overrides
Sales history
units · stockouts · returns
Known future inputs
price · promo · calendar
Candidate forecasts
features · models · horizons
Rolling backtest
cutoff-safe origins
Planning cycle
one buying round
Stockouts + waste fall
Increase experimentation velocity
Explore features, model classes, and loss functions in parallel. Move stronger candidates into planning tests faster.
Increase win rates
Backtest candidates across SKUs, horizons, and known future inputs. Give planners stronger evidence before each cycle.
Force multiplier for your team
Automate demand analysis, temporal backtests, and forecast pipeline preparation.
How it works
From a demand goal to a tested forecast. Connect your stack, verify what Pavo understands about your system, then start giving it projects.
Build system understanding
Pavo reconstructs how your production system works. Your team verifies the system book.
Run applied science projects with Pavo
Give Pavo an accuracy goal and planning horizon. It frames the problem, runs backtests, compares models, and opens PRs.
Capabilities
Applied science capabilities for the whole team.To improve forecasts across SKUs and horizons, without hiding the errors that cost inventory.
Applied science judgement
Picking the right problem, the right approach, the right lever
- Finds where the error actually costs you - which SKUs, which regions, which horizons, bias or variance
- Knows whether to fix the features, the model class, the hierarchy, or the loss function
- Grounds it in your own past forecast cycles and demand patterns, not just what's in the repo
Scientific rigour
Explores widely, and ships only what's proven
- Tries multiple model classes and feature sets at once instead of betting on one retrain
- You see accuracy backtested on your own history, with proper time splits, before it hits planning
- Checks new SKUs, promo weeks and long horizons separately - no gain that hides a stockout
Knowledge compounds
Every iteration makes the next one cheaper
- The work itself produces new knowledge - how demand behaves by region and season, where your data is unreliable, which horizons resist accuracy
- Written back to your system book, reviewed by your team, reused next time - it stays with you
- Your team owns the learnings, so v2 to v3 to v4 gets faster, not just further
Use-cases
Our average forecast hides SKU misses that create stockouts and excess inventory
Pavo flags stockout-censored sales, segments SKUs by demand pattern, compares point and probabilistic models on rolling time splits, then measures error and inventory risk by horizon.
Forecast biasInterval coverageStockout exposureInventory risk
Our forecast ignores planned promotions, prices, and ad spend, so peak-week inventory is wrong
Pavo separates inputs known at the forecast cutoff from later observations, supplies planned price, promotion, and ad spend, then backtests each horizon without future leakage.
Peak-week errorForecast biasScenario deltaHorizon accuracy
We set launch buys for new and long-tail SKUs with too little history to forecast alone
Pavo compares local baselines with cross-SKU models, adds product and category context, then reports new and sparse SKU results as distinct cohorts at every forecast horizon.
New-SKU errorLong-tail errorIn-stock rateExcess inventory
Security and trust
The security posture your compliance team wants
ISO 27001
Certified
SOC 2 Type II
Compliant
Encryption
Encrypted in transit and at rest, with managed production keys.
Access control
Least-privilege access, enforced with MFA and reviewed quarterly.
Data control
Tenant-segmented, with retention and customer-controlled deletion.
Monitoring and response
Continuously monitored, centrally logged, and ready to respond.
Tested and patched
Independently pen-tested, scanned, and kept current against threats.
Resilient by design
Multi-AZ with backups and a tested continuity and recovery plan.
Full reports, policies, and the complete control list are available on request through the Trust Center.
Build a forecasting system that improves, with every planning cycle.
Start with one forecast decision and one planning horizon. Pavo helps your team understand the system, evaluate better approaches, and ship the first team-approved model.