Data modeling
Raw events in. Clean entities out.
Every system describes your business its own way. Synopsis organizes the data by entity the moment it lands — customers, orders, invoices — so "this customer" is one record with everything attached, not five fragments in five tools.
Organized on arrival
Modeled the moment it lands.
As events stream in, Synopsis organizes them by entity automatically — no schemas to design up front, no transformation pipelines to write. The order event, the payment, the support ticket all attach to the records they belong to.
- No pipelines. There's no dbt project, no transformation code, nothing for your team to own.
- No upfront schema. You don't design the model before you see your data — the model comes from the data.
- Entities, not tables. The warehouse reads like your business: customers, orders, invoices, tickets.
Entity resolution
"Michael" in the CRM is "Mike" in the ERP.
Systems never agree on who's who. Synopsis links the same customer across systems: confident matches link automatically, and the in-between cases — the ones that usually mean a spreadsheet and an intern — are reviewed by AI.
- Confident matches auto-link. The obvious cases resolve themselves, at the scale of your whole customer base.
- Ambiguous cases get reviewed. When the evidence is mixed, AI weighs it instead of guessing.
- One key everywhere. Once linked, every question — in chat, dashboards, workflows — sees one customer.
Never stale
The model rebuilds as your business changes.
A data model is usually a snapshot of the company that existed when the project shipped. Synopsis keeps modeling as data streams in — new fields, new products, new systems fold into the model instead of breaking it.
- New data, absorbed. A field added upstream shows up on the entity — nobody files a ticket.
- New systems, same entities. Connect another source and its records link into the customers you already have.
- Current by construction. The model isn't maintained, it's continuously rebuilt — so it can't drift out of date.
What you get
A model your team never has to maintain.
Entities on arrival
Events are organized into customers, orders, and invoices the moment they land.
One customer, one key
The same person in five systems becomes one record every question can trust.
Auto-linked matches
Confident cross-system matches resolve automatically, across your whole history.
AI on the edge cases
In-between matches get reviewed by AI instead of piling up in a cleanup backlog.
No pipeline code
No transformation project to build, and none to inherit when its author leaves.
Ready for every surface
Chat, dashboards, and workflows all query the same clean entities — so they agree.
See your five versions of one customer become one.
Thirty minutes with your data, and you'll watch records from different systems resolve into entities you can actually query. Bring your messiest overlap.
