Every product, enriched & ready to sell.
Turn raw supplier catalogs — spreadsheets, PDFs, APIs, even photos from the warehouse floor — into complete, validated, channel-ready product data. AI agents do the heavy lifting; your team reviews only what needs human judgment.
A blurry supplier snap becomes a styled product page.
Apparel data arrives as cryptic abbreviations and inconsistent sizing. ProductBase normalises materials, colourways and size runs, writes editorial copy, and ranks the imagery — across the whole season at once.
From raw mess to channel-ready — in five moves.
The same pipeline runs whether you drop in one spreadsheet or ten thousand SKUs across PDFs and APIs. AI handles the volume; humans handle the judgment calls.
One platform, end to end.
Everything anchored to typed, versioned data contracts — so AI output is never trusted blindly, and a running workflow can never be broken by someone editing a definition.
Visual pipeline builder
Drag-and-drop 24 node types onto a canvas — then watch rows stream through each node in real time on a live run page.
Campaign-ready imagery, no photo studio
Ten-plus one-click operations, style references, and batch generation across thousands of SKUs — plus product video.
PDF catalogs, read by AI vision
AI vision classifies each page — spec sheet, product grid, pricing matrix — routes it to a specialised extractor, then maps embedded photos to the right products.
One queue for every human decision
Field-level accept, edit or reject, keyboard Focus Mode, and QR-code mobile photo walks that unblock enrichment in real time.
One catalogue. Every channel sells from it.
Enrichment produces good data. The catalogue is where that data becomes a product record you can govern — supplier feeds land as rows, every change waits for a decision, and each destination is served the version it actually accepts.
The same item, two suppliers
When two sources send the same barcode, the records are flagged and kept apart — never silently merged. A false split is visible, annoying and cheap to fix later; a false merge ships the wrong item.
Completeness you can act on
Not “37% complete” — “12 products have no German description”. Every unfilled field carries its reason: nothing stored, not translated for this channel and language, or a requirement no column can satisfy.
One truth, many contracts
A channel spec is a versioned schema with per-channel overlays, so one marketplace can tighten a field without disturbing the rest. Category crosswalks resolve your explicit override first, then the category default.
A bench of specialists you drop into any workflow.
They share one discipline: every value carries a confidence score, a source, and a reason — and anything uncertain is handed to a human instead of being guessed.
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Nothing ships unchecked.
Six independent layers stand between a raw row and a published product. AI output below confidence — or failing a quality rule — is routed to human review, never shipped silently.
Security posture in one line: isolated databases per tenant, AES-256 envelope encryption for all third-party credentials, signed-state OAuth with HMAC verification, JWT sessions with refresh rotation, and no plaintext secret ever written to a log.
Enriched data, delivered where it sells.
Pull from Shopify or any REST/GraphQL API, enrich through any workflow, and write the results straight back — with per-product error isolation so one bad row never blocks the batch.
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