Tera Bullion builds ML demand forecasting for CPG brands: models trained on your actual sales history, seasonality, and market signals — not industry averages — targeting 90%+ forecast accuracy across every channel you sell through. The result is fewer stockouts in your best weeks, less capital buried in dead inventory, and purchasing decisions made on numbers instead of nerve.
Demand planning at most consumer brands is a spreadsheet with last year's numbers, a gut adjustment, and a prayer. The cost shows up on both ends: you're stocked out during your best week — velocity you paid marketing dollars to create, handed to a competitor — or you're sitting on dead inventory, capital buried in a warehouse while invoices come due.
The cruel part is that both failures come from the same root: purchasing decisions made months ahead on information that's mostly vibes. Meanwhile the actual answer sits unread in your own history — every season, promotion, and channel swing you've ever had, recorded in the POS exports and marketplace reports nobody has unified.
Padding the buy ("order 20% extra to be safe") doesn't eliminate risk — it converts stockout risk into dead-stock risk at a markup. Safety stock across every SKU is the most expensive insurance policy a brand can carry.
The built-in forecast in your inventory tool is a moving average wearing a dashboard. It extrapolates the recent past, so it's precisely wrong at the moments that matter — promotions, seasonal ramps, and channel shifts — the events that make or break a quarter.
Hiring a demand planner without fixing the data means paying a professional to reconcile five spreadsheets before they can begin. The judgment is valuable; the archaeology isn't. Give a planner a unified warehouse and a trained model and their value multiplies.
The common failure: forecasting is treated as an opinion problem when it's a data problem. Your channels already know what demand looks like — they've just never been in one place, learning-ready.
Before: purchasing pulls last year's spreadsheet → adjusts on instinct → over-buys the slow SKUs and under-buys the fast ones → the best week of the year ends in a stockout → the warehouse absorbs the misses.
After: the model reads every channel's history and calendar → purchasing gets a per-SKU demand picture with confidence ranges → orders track reality → your best weeks stay in stock and your capital stops sleeping in the warehouse.
The measured targets: 90%+ forecast accuracy, SKU-level visibility across every channel — and for one client, the visibility layer alone paid for the engagement in its first month.
If you're multi-channel with real history and your quarters swing on inventory calls, this is where ML pays hardest in CPG — because every point of forecast accuracy is margin recovered from both ends at once.
Your sales history across channels — commerce platform exports, marketplace reports, distributor EDI, retail POS — plus calendar context like promotions and seasonality. If it exports, we can warehouse it; unifying those sources is the first phase of the build and pays for itself in visibility alone.
Because the models train on your actual sales history and seasonality, not category averages. Our forecasting builds target 90%+ accuracy, and the number is measured honestly — forecast versus actuals, per SKU, visible on a dashboard you can check any week. Accuracy also compounds: every season of data makes the model sharper.
That's the design case. Channels behave differently — DTC responds to your marketing, retail to placement and promotion, marketplaces to their own weather. The system forecasts per channel and rolls up to a single demand picture, so purchasing sees one number built from real channel behavior.
Built-in forecasting is usually a moving average with a settings page — it extrapolates last quarter. A trained model learns your seasonality, promotional lift, and channel dynamics, and it's evaluated against your actuals rather than trusted on faith. The difference shows up in your best weeks, which are exactly the ones averages miss.
The honest answer: forecasting needs sales history to learn from, so very young brands usually start with the channel-visibility layer — one dashboard replacing five logins — and add models as data accumulates. One client's SKU-level visibility paid for the engagement in its first month, before forecasting even entered the picture.
Completely. Warehouse, dashboards, and models are built in your accounts, documented, and handed off — no black boxes, no lock-in, no hostage data. If we part ways, everything keeps running and it's all yours.
Scoped to your channel count and SKU complexity, which is why we start with a free build plan. Tell us how you forecast today and where it hurts — we'll map the build and the number it has to beat.
Tell us how this works in your operation today. We'll send back a build plan — no pitch deck, no fluff, just engineering.
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