Tommbo is a production-grade observability platform for AI coding agents.
It automatically exports, ingests, and analyzes conversations from Claude.ai, Claude Desktop in Cowork mode, and Claude Code.
The platform gives engineering leaders one dashboard to understand how AI agents are being used across a team, replacing scattered local session files that cannot be queried centrally.

Engineering teams adopting Claude Desktop, Cowork, and Claude Code had no centralized way to understand what their AI agents were doing.
There was no shared run history, no cost visibility, no way to judge whether an agent’s output was correct, and no clear method for connecting AI usage to engineering output or return on investment.
Spark Eighteen built Tommbo as an auditable observability and efficiency platform that turns raw Claude Desktop, Cowork, and Claude Code sessions into a single source of truth engineering leaders can act on.

Tommbo needed to centralize scattered agent sessions, evaluate the quality of AI-generated outputs, and give engineering leaders defensible insight into cost, productivity, and ROI.
Spark Eighteen built Tommbo end to end.
A FastAPI and PostgreSQL backend ingests conversations through a Desktop Watcher that reads audit.jsonl directly without a browser dependency, along with a standalone synchronization agent and binary.
An LLM-as-judge quality framework scores every conversation for correctness, conciseness, and sentiment.
A React and Recharts dashboard includes a dedicated Team Efficiency tab covering ROI, cost per feature, and pre- and post-adoption productivity comparisons.
An MCP server exposes the complete API as tools, allowing Claude Desktop and Claude Code to query and manage agents directly within conversations.

Tommbo provides one unified dashboard that replaces scattered local exports.
An invite system and macOS .pkg installer with LaunchAgent provide zero-touch onboarding, allowing new teammates to begin syncing automatically.
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