For retail data and AI teams
Your retail AI isn't blocked on data access. It's blocked on data you can trust.
FYND takes the feeds you already have (POS, ERP, e-commerce, WMS, supplier files) and turns them into one clean record per SKU, store and day. Every field carries a confidence score and its source, and anomalies are held before an agent acts on them. It runs in your Databricks, Snowflake or Fabric. Keep your ERP.
ART=4521NR DESC="MONT DMN4521 NERO"
QTY_VEND_28GG=0 PREZZO=119,00ART=4521NR VEL_MEDIA_GG=0,4COD=4521/NR NEG=MI012
GIACENZA=14 UM=PZsku=DMN-4521-BLK price=129.00 EUREAN=8001234567890 SHIP_QTY=2 UOM=CTGTIN=08001234567890 PACK=6GET /v1/sku/8001234567890/store/MI-012/timeline 200 OK, 6 feeds merged { "gtin": "8001234567890", "store": "MI-012", "on_hand": 14, "uom": "EA", "sold_28d": 0, "peer_velocity_day": 0.4, "price_eur": { "store": 119.00, "online": 129.00 }, "_fynd": { "confidence": 0.62, "lineage": "erp/giacenze/row-4812", "anomalies": ["phantom_inventory", "price_mismatch"], "action": "replenishment_hold, store_ops_queue" } }
Illustrative record, synthetic SKU. The same frame arrives under four codes, two prices and two units of measure.
One SKU, six systems, one answer.
An optical frame sold in a Milan store and online, replenished from a central warehouse. Six systems describe it six ways.
What arrives
Local codes, packs in one system and pieces in another, a store price that disagrees with the web price.
What FYND binds
One GTIN, normalized units, one category, a confidence score and the exact source row. A low score goes to a reviewer, not to production.
What your agent gets
One call returns the full SKU-store history. The same layer lands as warehouse tables for forecasting, pricing and BI.
Not another ERP or BI tool. The layer that makes your systems agree.
Your integrations and dashboards stay as they are. FYND sits between the systems that hold the data and the agents and models that act on it.
Your systems
POS, ERP, e-commerce, WMS, supplier EDI, loyalty
FYND
Runs in your Databricks, Snowflake or Fabric. Standardizes, binds, scores and flags.
API and tables
SKU × store × day, with confidence and lineage
Your product
Replenishment, pricing and assortment agents, BI
When your pricing agent drops a price, you can show why.
This is the part that matters once AI starts acting on stock and prices, not just reporting on them.
Confidence on every field
A score on each mapping, not a global accuracy claim. You set the threshold for what reaches an agent.
Lineage on every value
Every number points back to the file, table and row it came from, so any decision can be audited in minutes.
Holds before action
Phantom stock, cross-channel price mismatches, unit errors and duplicate SKUs are flagged before an agent reorders or reprices.
The questions you'd actually ask
Is this a six-figure integration project?
No. The sample run on your exports is free. If you go further, discovery is a fixed €7,500 over three weeks, and after that we're paid from a declining share of the margin FYND helps recover over three years (50%, 25%, 10%). No procurement gauntlet to find out whether it fits.
Does it replace our ERP, POS or BI?
No. It sits behind them. They hold and move your data; FYND makes it consistent enough for an agent to act on. Teams keep every system and integration they already have.
How fast do we see something real?
The sample run on your exports comes first. A deployment timeline depends on your feeds, and we won't quote one before we've seen them.
Does our data leave our environment?
In deployment, no. FYND runs inside your Databricks, Snowflake or Fabric account, and you remain the data controller under GDPR. For the sample run, exports can be pseudonymized before they reach us, and we sign a data processing agreement first.
What do you need from us?
Access to the exports, one person who knows where they come from, and 30 minutes to walk through what we found.
What we've shipped
Luxury group, 75 brands
A churn model that flags at-risk customers 90 days out and now catches about 80% of those who were about to leave.
Konvergence
A strategy engagement turned into an AI adoption roadmap in three weeks, with five concrete deliverables.
Runs where your data lives
Deploys into Databricks, Snowflake or Microsoft Fabric. Your data stays in your cloud.
Paid on results
After a fixed-price discovery, our fees come from the margin we recover. No results, no fee.
Stop reconciling SKUs. Start shipping agents.
Send one week of POS, stock and price exports. We'll send back a mapped, flagged sample of what FYND finds in them.