Turn your operations into an AI-native workflow

Legacy databases, custom APIs, unstructured PDFs and real-time streams are reached where they already sit. BLOB storage, cloud sources and enterprise systems come together in one place instead of being copied into yet another store.

Access is secured from the first connection, so unifying the estate does not mean loosening control over any part of it.

Data teams build, deploy and orchestrate multi-service AI applications through automated workflows rather than bespoke integration code.

Customisable governance rules, reusable actions and prompt-driven logic automate CI/CD and streamline DataOps across the whole estate.

Pipelines, compliant datasets and ready-to-use LLM workflows are published once and made available across every tenant, with role-based access applied at the point of use.

Build once, publish securely everywhere — consumers get the product, not a copy of the source.

Teams chat directly with enterprise data: real-time answers, semantic search and visualisation built in, without a ticket to a data team.

Agentic data workers run on custom triggers and schedules, keeping analytical-ready data flowing to the people and systems that depend on it.

Every run is observed: which matches held, which prompts drifted, where a reviewer overrode a decision and what they changed it to.

A correction becomes training signal rather than a ticket in someone's queue, so the same failure does not have to be caught twice by a human.

Findings feed back into the rules, the pipelines and the model routing, so accuracy rises and cost falls without a re-implementation project.

The loop closes and starts again at Connect — a new source joins the same cycle rather than starting a new one.