Industry · Food & Beverage
Private AI for the food value chain.
From a national retail food chain to a multi-product B2B food enterprise, the operating model is the same: forecast the next four weeks, segment the customer file, price with the elasticity you actually have. We help food operators do all three on a private AI foundation.
Industry Challenges
Where the margin leak is.
- Experience-driven forecasting
Manager gut feel averages around 75% SKU-store-day accuracy. The next 25 points of lift come from a model, not a hire.
- Fuzzy customer profiles
B2B and B2C needs sit in the same data file. Without segmentation, every campaign is one-size-fits-all.
- Margin pressure at the order level
Headline margin hides the real picture. Order-level profitability is rarely visible in the ERP, let alone priced against.
- Promotions with no measurable lift
Spend goes out, response comes in modest, and the question of whether the campaign worked cannot be answered after the fact.
What We Deliver
From SKU-store-day accuracy to margin.
How it works in practice
From the buyer's data to the buyer's price.
From SKU-store-day to the weekly demand plan
We start where the value is most measurable. A 75%-accurate forecast at the SKU-store-day level becomes a 90% one when the model is fed the right calendar, weather, and loyalty signals — typically within 6-8 weeks. The buyer who used to pad orders by 10-15% can stop. The store manager who used to call in gut-feel numbers gets a daily recommendation and a confidence band. The compounding effect of a 15-point accuracy lift is not just fewer stockouts; it is a quieter supply chain, a smaller safety stock, and a more confident marketing team that can finally measure campaign lift against a real baseline.
Daily forecast with calibrated confidence intervals, delivered as a feed the procurement team already consumes. No new UI to learn.
Cohort-level elasticity, not individual price
Dynamic pricing in food CPG works only when it is designed around cohorts, not individuals. A high-frequency loyalist is price-insensitive on staples and price-sensitive on indulgent SKUs; a monthly visitor with a high basket is a different signal entirely. The segmentation model treats them differently. The pricing optimizer then acts on cohort-level elasticity, with a brand-set floor that protects the price-sensitive loyalist from being squeezed. The result is a margin engine that compounds — the same product mix, the same shelf pricing, but with promotion waste cut from 30% to closer to 8%.
Per-cohort elasticity curves validated against the last 12 months of promotion performance.
B2B food enterprises and order-level profitability
For B2B food operators, the constraint is not store traffic but order profitability. We model the cost-to-serve at the order level — factoring in route density, mix complexity, payment terms, and customer lifetime value — and we surface which orders to take, which to renegotiate, and which to walk away from. The headline margin number on the P&L stops hiding order-level reality. The same dataset then powers the customer segmentation that drives the next quarter's commercial strategy.
Order-level P&L dashboards, integrated with the existing ERP. Six-warehouse operators have shipped this in 4-6 weeks.
Selected Work
What this looks like in production.
Recent Writing
Notes from the food value chain.
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Read articleWorking on a food AI problem?
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