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.
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.
↓ drop your own product photos into any card below
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.
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.
↓ drop a screenshot of each module into its card
Drag-and-drop 24 node types onto a canvas. Branching, business rules, version history, and a run page that animates rows streaming through each node in real time.
Typed, immutably-versioned data contracts. Workflows pin a frozen snapshot, so a schema edit can never silently change a production pipeline’s behaviour.
AI vision classifies each page — spec sheet, product grid, pricing matrix — and routes it to a specialised extractor, then maps embedded photos to the right products.
Campaign-ready imagery without a photo studio: 10+ one-click operations, style references, batch generation across thousands of SKUs, and product video.
One queue for all human work. Field-level accept/edit/reject, keyboard Focus Mode, and QR-code mobile photo walks that unblock enrichment in real time.
The single source of truth. Multilingual records, 50-version rollback per product, three write modes, and AI-assisted duplicate merge.
Every AI call traced — model, prompt, tokens, latency, cost — in an in-app dashboard. Fail-open by design: tracing can never break production.
A database per tenant, 32 granular RBAC permissions, bring-your-own AI keys, and a resilient job engine that retries only the bad rows — never the 10,000 good ones.
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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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.
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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Bring a messy supplier file to the demo. We'll run it through the pipeline live and hand you the enriched, validated output to keep.