No vendor vault. A data layer you own.
There are two ways to unify fragmented data: copy everything into a vendor’s platform, or build the structure inside your own environment. Meridian chose the second — deliberately.
Centralized vendor platforms
Legacy analytics platforms extract, transform and load sensitive data into a central proprietary database — often in the vendor’s cloud. The result: heavy bandwidth costs, data that is stale since the last sync, and your domain knowledge captured in a closed format. Every month makes leaving more expensive — that is not a side effect, it is the business model.
Meridian data layer
Meridian places its own databases between your source systems and the operational picture — inside your environment, under your control. Connectors read from the sources; the data layer structures what they deliver into one ontology and keeps its history. Your source systems remain the system of record and are never written to.
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Operational applications
The map, the control room, drone operations and the field app render one shared picture. Analysts, dispatchers and officers act on the same truth.
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Meridian’s data layer
Meridian’s own databases, inside your environment: one ontology of people, vehicles, sites and events — their links, their provenance and their history.
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Sovereign sources
Databases, sensors and registries stay where they are — on-premise, air-gapped or in the government cloud. Connectors only read; operational networks (OT) only through your existing DMZ or data diode.
Two architectures, side by side.
Location of data
Copied into the vendor’s platform, often in its cloud.
Sources stay put; the data layer runs in your own environment.
Freshness
Stale since the last batch sync.
Updated continuously by connectors — streamed where the source allows it.
Ownership of the model
Captured in a closed format.
An open, documented ontology under your control.
Integration
Proprietary connectors, licensed per source.
Open connectors and APIs; local AI through the open Model Context Protocol (MCP).
AI inference
Via cloud APIs outside your perimeter.
Language, speech and video models configured inside your environment.
Exit scenario
A multi-year migration project.
Your sources never moved; the data layer exports in open formats.