Overview

The integration model

Colabra has six distinct integration surfaces. They are related, but they solve different problems:

Surface	Best for	Examples
Agentic AI	Letting AI clients work directly against live Colabra context	ChatGPT, Claude, Codex, Microsoft Copilot, Gemini
Cloud storage	Pulling evidence into a project	Google Drive, Box, Dropbox, OneDrive, SharePoint, Egnyte
Transcripts	Bringing call transcripts into the evidence set	Gong, Fireflies
Notifications	Pushing project activity into team channels	Slack, Microsoft Teams
Data publishing	Sending structured project data into BI tooling	Power BI, Excel exports
Webhooks & API	Building automations or external integrations	Webhooks, REST API

The useful mental split is:

• agentic AI — MCP clients acting on live Colabra context
• evidence in — cloud storage and transcripts
• notifications out — Slack and Teams
• data out — Power BI and Excel
• app-to-app automation — webhooks and REST

That split matters because “integration” is too broad to be useful on its own. A diligence team usually wants one of three concrete outcomes:

• get source material into the project faster
• push project state into another working surface
• let an external system or AI act on live Colabra context

If you pick the wrong surface, you usually end up with the wrong operating model. For example, Slack is good for awareness but not formal follow-up. Cloud sync is good for evidence intake but not for missing-document requests. MCP is good for interactive assistants but not for unattended server automation.

Common integration jobs

Need	Start here
Sync a folder of diligence files	Cloud storage
Bring meeting or call transcripts into evidence	Transcripts
Notify the team in chat tools	Slack or Microsoft Teams
Feed project data into downstream reporting	Power BI or Excel exports
Let ChatGPT, Claude, or Codex work directly against Colabra	MCP
Trigger your own systems when Colabra changes	Webhooks
Build a normal API integration	API reference

A realistic integration stack for one deal

Real deal example: evidence in, team awareness out, AI on top

A team might sync the seller’s Drive folder into the project, route management-call transcripts from Gong, notify the internal deal channel in Slack, and connect ChatGPT or Claude over MCP for evidence-backed synthesis. Those are four different integrations, but they all feed the same project rather than creating four parallel systems.

Common setup pattern

Most integrations follow the same high-level pattern:

1. Connect the provider at workspace scope.
2. Select the project-level destination, source, or routing rule where relevant.
3. Let Colabra feed the output back into the normal evidence, task, request, or report model.

That matters because integrations are not separate products. They are ways to bring evidence in, move context out, or extend the same working system.

How to choose quickly

• Use cloud storage when the source of truth is already a folder.
• Use transcripts when spoken diligence context should become evidence.
• Use notifications when the team needs ambient updates in chat.
• Use business intelligence when data needs to leave Colabra in structured form.
• Use MCP when an interactive AI client should work inside live Colabra context.
• Use webhooks or the REST API when your own software needs to react or write back.