Structured and unstructured, same pipeline
MySQL, PostgreSQL, MongoDB, CSV, Excel, PDF, Tally, all extracted into a common data lake rather than living in separate one-off integrations.
Platform Data Foundation
Connecting a source is the easy part. What happens next is the point.
Databases, spreadsheets, PDFs, WhatsApp exports, invoice piles. Every enterprise has a version of this list, and every vendor claims to connect to it. The question that actually matters is what the data becomes once it lands: AI-ready data resolved to one identity, or the same mess relocated.
Sources
Data lake S3
ETL
Warehouse discovery-ready
Overview
Treating ingestion as the finish line is how companies end up with a beautifully connected warehouse that still can't answer a cross-system question. Ingestion here is deliberately positioned as step one of a chain, not a deliverable in itself. What it hands off to determines whether the rest of the platform can do anything with it.
Incremental sync
Resyncs pull only what changed, not a full re-extraction every time a source updates. A warehouse with a thousand new orders overnight doesn't mean reprocessing everything that came before them.
Automatic schema mapping
A MySQL integer and a MongoDB string describing the same concept get standardised into comparable types, so downstream joins work on meaning, not on matching data types by hand.
Warehouse, not just a landing zone
Everything lands in a governed PostgreSQL warehouse structured for querying and relationship discovery, not a raw file dump waiting for someone to make it useful later.
None of this is the destination. What lands in the warehouse becomes the input to Relationship Discovery, and the schema standardisation that happens here is precisely what makes cross-database relationship detection possible in the first place. Skip this step, or do it badly, and every claim on that page falls apart.
Insights
Use cases & field notes from the data trenches.
Customer playbooks, technical deep-dives and field notes from deployments into messy enterprise estates.
Technical
JOIN dms_secondary ON erp.sku_code
→ 94% match · conf 0.94
→ 94% match · conf 0.94
Contextual data in personal care manufacturing & distribution: the context between systems is the missing asset.
Every personal care manufacturer generates data at every stage of its value chain, and cannot join it. How automated relationship discovery turns five to eight disconnected systems into cross-source answers in minutes, not days.
Read the white paper Use Case
ERP Secondary Sales GST
One view across distributors, billing, and compliance: how pharma distribution stops flying blind.
Secondary sales in spreadsheets. Distributor stock self-declared. Billing in the ERP. Compliance records as PDFs. How Qwry.AI connects them into one governed view, and what becomes possible when it does.
Read the use caseConnectors
Reads from the systems you already have.
Databases, warehouses, files and the apps you run: connect what you already have, don't migrate. Every source lands in one governed graph, with 200+ on the roadmap.
Don't see your connector? Tell us your stack. We'll scope it.
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