I've been talking to a lot of early-stage SaaS teams about how they use AI internally (not the "we're AI-native" marketing version, but the real day-to-day).
What I keep hearing is:
Half the team is on ChatGPT, the other half uses Claude, nobody shares context
Prompts that work well live in someone's Notion doc (maybe)
There's no visibility into what the team is actually using AI for
Onboarding a new hire means "here's a $20/seat subscription, good luck"
We're building something in this space and I'm genuinely curious whether this matches what others are seeing, or if some teams have actually figured it out.
I built an MCP server that connects coding agents (Claude Code, Cursor, OpenCode, Codex) to a collaborative workspace where your team and other AI models can review what the agent is planning.
The problem: When Claude Code creates an implementation plan, it lives in your terminal session. Nobody else sees it until it becomes a PR. If you want GPT to check the architecture or a teammate to flag issues, you're copy-pasting between windows.
This MCP server fixes that. When your agent creates a plan, it gets shared as a collaborative thread in CoChat. Engineers comment on it, other AI models review it, and you pull all the feedback back into your agent's context with one command. Decisions can be saved as project memories that persist across sessions and are searchable by anyone.
What it does: Plans: Auto-shared as collaborative threads. Pull feedback back into your terminal. Cross-model review: Have GPT review your Claude plan, or vice versa. Project memories: Semantic memory that persists across sessions, models, and people. Ask: Query your project's knowledge base from the terminal. Auto-scoping: Detects your project from git remote. No config needed. Setup is one command per agent. Auto-share behavior is configurable (off/plan/all).
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