What is Retailgrid? Agentic AI pricing, explained

Most pricing tools give you a dashboard and leave the thinking to you. Retailgrid takes a different position: it is an agentic AI pricing platform built for mid-market retailers, where the system does the investigation, quantifies the opportunity, and hands back a priced recommendation with its reasoning attached. Built for €10M-€500M retailers, it is designed to go live in days rather than through a six-month enterprise rollout.

What "agentic" actually means here

Agentic is an overused word, so it is worth being precise. A conventional pricing analytics product surfaces a chart and waits. An agent works the way a pricing analyst works - it pulls sales, competitor, and inventory data, runs elasticity and margin analysis, and returns ranked recommendations you can approve.

You ask in plain language: which SKUs are leaking margin this week? The agent investigates, sizes the gap, and proposes the move. The difference is not the model. It is that the output is a decision, not a report you still have to interpret.

Three connected layers

Retailgrid is built as three pillars rather than a pile of features.

AI Workspace. The AI Workspace is a grid that behaves like the spreadsheet your team already uses - filter, formula, pivot - but wired to live competitor prices, sales, and stock, and stable across millions of rows. Everything else in the platform reads from and writes to that grid.

Agentic pricing. Agentic pricing is where recommendations are produced and applied against your rules. Every price move is auditable. Nothing is a black box.

Price monitoring. Price monitoring tracks competitor prices across marketplaces and direct-to-consumer sites, refreshed every four hours and mapped to your SKUs, so competitive price tracking is a column in the grid rather than a separate competitor monitoring subscription.

Rules in plain English, not SQL

Pricing rules are the part that usually turns into unmaintainable nested formulas. In Retailgrid you describe the intent - match competitor minimum on KVIs, hold a 10% margin floor, cap daily change at ±10%, round to .99 - and the platform converts it into structured rules your team can edit, reorder, or override.

Rules are version-controlled and scoped by category, product role, or SKU set. That is what separates a genuine pricing engine from a workbook only one analyst understands.

Explainability is the product

Every recommendation shows its math. Per-SKU rule attribution is one click away. You see the feasible price range, which rules applied, which were violated, and the margin impact before you commit.

The practical value shows up in the CFO conversation. Price optimization software that cannot explain itself gets overridden within a quarter; one that logs every run gets trusted and adopted.

Who it is built for

Retailgrid targets the gap between spreadsheets and enterprise suites. Teams at that scale run pricing through 20+ untracked files, screenshots from competitors, and judgement - while enterprise pricing solutions quote six figures and half a year.

Reported outcomes include roughly 3pp gross margin improvement, 5% revenue uplift on optimised SKUs, and 25% better inventory productivity, with results varying by category and circumstance. One online wine retailer cut repricing time by 90% while adding 2.3% margin; a multi-brand fashion retailer consolidated six tools into one. The case studies cover the detail.

The founding team comes from retail pricing operations rather than consulting - former category managers and pricing analysts who built each workflow as a spreadsheet first, until it broke.

Book a demo and see Retailgrid in action.