Team Knowledge SharingFor the Agent Era

Share code snippets, ideas, and posts with your team seemlessly with voice. Access everything via an MCP server with your favorite coding agents.

For invited users only

Knowledge sharing in 2025 isn't just important—it's the competitive edge for AI-first teams.

You can code by talking to AI, but your team's hard-won knowledge dies in Slack threads and forgotten browser tabs. Every breakthrough stays locked in someone's head—or worse, lost in their command history.

The Discovery Problem

Your team discovers the same solutions three times. Everyone's learning, no one's sharing.

The Hoarding Reality

Your best engineers are hoarding game-changing discoveries. Not on purpose—there's just no natural way to share them.

From Voice to Accessible Knowledge

Native modalities for everyone: Voice for humans, MCP for agents

Voice-First Capture

Describe code architecture, debug sessions, or technical concepts naturally. No context switching, just speak and capture.

Intelligent Processing

AI agents extract code snippets, generate documentation, create knowledge graphs, and link related concepts automatically.

MCP Integration

Your team's knowledge becomes accessible to Claude, Cursor, and other MCP-enabled tools.

Universal AI Integration via MCP

Your team's voice-captured knowledge, instantly accessible in every AI tool you use

Real-Time Knowledge Access
// In your IDE, you type:
// How do we handle rate limiting?
// MCP instantly provides:
{
"implementation": "Redis sliding window",
"limit": "100 requests/minute",
"author": "Sarah (voice note 2 days ago)",
"code": "src/middleware/rateLimit.ts:45",
"context": "Chose over token bucket for accuracy"
}
Capture Ideas While Coding
// While debugging LLM behavior, you say:
"Log on Finchly using the context of this conversation - we should group Langfuse traces by session ID, not individual calls. It gives way better visibility into conversation flow."
// Finchly creates:
📊 LLM Observability Pattern Logged
Title: Session-Based Langfuse Tracing
Author: You + AI Agent
Tags: #langfuse #observability #llm #tracing
Pattern: One trace per session, nested spans for each LLM call...

Works Seamlessly With Your Development Environment

Claude Code
Cursor
VS Code
Windsurf
GitHub Copilot
Zed

Instant Context

AI understands your codebase patterns and decisions instantly

Zero Friction

No context switching—knowledge surfaces right where you code

Team Learning

Every debugging session becomes future team wisdom

10x Onboarding

New developers productive from day one with full context

Coming Late 2025

Ambient Team Intelligence

Capture knowledge from every interaction. No context switching required.

Multi-Voice Sessions

Team meetings → Quick Posts

1

Tool Discoveries

New libraries & frameworks that helped

2

Problem Solutions

How we fixed tricky bugs & issues

3

Prompting Strategies

Effective AI patterns & techniques

LiveKit Agents identify speakers & attribute ideas

Ambient Capture

Background knowledge extraction

Pair Programming

Capture reasoning behind implementations

Debug Sessions

Document root causes & fixes

Onboarding Sessions

New team member ramp-up captured

Privacy-first: Team controls when & where to capture

Launching Late 2025

How It Works

Transform team conversations into queryable technical documentation

1

Speak your technical insights

Explain that tricky algorithm, describe your architecture decision, or walkthrough debugging steps using natural speech.

2

AI agents extract and enhance

Specialized agents identify code snippets, technical concepts, dependencies, and create rich documentation with proper formatting.

3

Knowledge graph creation

Your insights are connected to existing team knowledge, tagged by technology stack, and organized into searchable collections.

4

Query from your IDE or AI tools

Access team knowledge directly from Claude Code, Cursor, Cline, etc. Allow your agent to get instant, contextual answers from your team's collective insights.

Built for Technical Teams

Whether you're shipping code or building community, Finchly adapts to your workflow

Share code snippets, architecture decisions, and debug sessions through voice. Your AI agents can access everything via MCP servers.

Ready to Transform How Your Team Shares Knowledge?

Join engineering teams building their voice-powered knowledge hubs

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