Demand Forecasting for Shopify
A Shopify-tuned deployment of the canonical Demand Forecasting & Inventory Engine — wired to Shopify's Orders API, Inventory API, and per-channel data, integrated with the operator's replenishment workflow.
- Engagement
- 6–12 week deployment · self-serve to custom packaging
- Built for
- DTC operators · Shopify-Plus brands · Multi-store DTC holdcos
Shopify operators stockout on the hits and over-order on the slow movers — because the inventory app shows historical sales, not next-quarter demand. The forecasting infrastructure that would solve this is well-understood but rarely deployed at the operator level.
What this is
The canonical Demand Forecasting & Inventory Engine, tuned for Shopify-native integration. See the canonical product page in the Operations Algorithms Suite for the modeling backbone, the signal-set rationale, and the technical detail. The Shopify-tuned tuning consists of:
- Data integration directly with Shopify Orders API, Inventory API, and Sales Channels metadata.
- Per-channel demand attribution from Shopify's channel breakdown (online store, POS, social channels, marketplaces).
- Integration with Shopify Flow for the replenishment-workflow downstream side.
- Theme-app-extension surface for the operator-facing layer where appropriate.
The packaging tiers handle the buyer range — self-serve SaaS for mom-and-pop, custom deployment for established brands, multi-tenant for portfolio operators.
What you get
- The forecasting model deployed against your Shopify data.
- Shopify-Inventory-API-wired replenishment recommendations.
- Per-channel attribution where the store operates across channels.
- Operator UI consistent with Shopify Admin conventions.
- Quarterly model refresh.
Engagement is shape, not list.
Length and price are functions of the data and the destination. The shape below is the typical engagement.
- Length
- 6–12 week deployment · self-serve to custom packaging
- Lead
- Bogdan
- Cadence
- Async, weekly
- Bar
- Production
Scoped during the discovery call against the actual data and the operation it integrates with.
Principal engineer. Architecture and most code ships through one keyboard.
Written updates between, calls when the decision needs the room.
Async correctness, capacity under burst, observability at every boundary.
Products this composes with.
Same suite, or vertical-specialized versions in another.
- Also in · Operations Algorithms Suite
Demand Forecasting & Inventory Engine
A forecasting layer at SKU grain — combines time-series baselines with external signal (macro indicators, social-and-search signal, weather, consumer-behavior priors) — feeds inventory and replenishment decisions with documented uncertainty.
- Same suite · Shopify Ops Intelligence Suite
Dynamic Pricing for Shopify
A Shopify-tuned deployment of the canonical Dynamic Pricing & Promotion Engine — wired to Shopify Discounts, Shopify Functions for custom pricing logic, and the storefront experimentation infrastructure.
- Same suite · Shopify Ops Intelligence Suite
Shopify Operations Workflow Automation
An integrated operations-workflow layer — Shopify Flow for the standard triggers, custom orchestration where Flow's limits run out, and the cross-workflow coordination that the point-app stack misses.
What buyers ask about this one.
Is this just the OAS Demand Forecasting Engine relabeled for Shopify?
It's the same modeling backbone. What's different is the data integration (Shopify Orders / Inventory / Sales-Channels APIs as canonical sources, channel-attribution from Shopify metafields, theme-app-extension signal for the storefront-aware piece) and the deployment shape (wired to Shopify's Inventory operations rather than a generic ERP).
What packaging tier fits our store?
Self-serve SaaS for mom-and-pop and single-founder stores; custom deployment for established brands with 5–50 SKU categories; multi-tenant for brand holdcos and DTC portfolios. The model is the same — the packaging differs.
How does it handle Shopify-specific data?
Shopify metafields, draft orders, abandoned-checkout signal, Shopify Sales-Channels per-channel breakdown — all fed into the forecasting model. Where the operator uses Shopify Flow, the engine integrates with Flow triggers for replenishment workflows.
Is this different from Klaviyo Predictive forecasting?
Klaviyo's predictive layer focuses on customer-side prediction (next purchase date, predicted spending). This product focuses on inventory-side prediction (SKU-level demand). Complementary — different operating decisions.
Pricing?
Tiered per buyer profile — self-serve SaaS, custom deployment, multi-store portfolio. Discovery call covers the right tier.
If the deliverable matches the gap, the next step is one call.
We'll scope length and price against your data and the operation it integrates with. No retainer, no fishing.
Bogdan and team · async-first · OP—2026