Case Study

SAIBA Connectors: One layer for source systems and agents

SAIBA

Most AI workflows do not fail because of the model. They fail at the connections to the systems where data, review, and publishing already happen.

SAIBA Connectors was established as a reusable integration layer for both customer projects and our own products.

SAIBA connector architecture

The challenge

Each new workflow previously required new integration thinking. That created duplication, weaker QA, and more risk when multiple agents needed the same sources.

The solution

The connector layer standardizes the most important integrations, documents their maturity, and makes them available to content, CRM, brain, audit, and video workflows.

What changed

New workflows can be assembled faster because source access, publishing, and feedback loops already have a shared contract.

Technologies

Technology Usage
GitHub Issues, PRs, and release signals
Buffer Publishing queue
Runway / Figma Visual assets and design sources
MCP Agent-friendly access to tools

Services delivered by SAIBA

  • Integration architecture
  • Connector registry
  • Agent tooling
  • QA and provenance

Does your AI workflow need safe connections to existing systems?

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