CloudByte PMS VS Genchi

CloudByte PMS's answer

• Backend: Node.js + PostgreSQL + VectorDB
• Dashboard: React + TypeScript
• Deployment: Docker, AWS infrastructure managed with Terraform
• Integrations: native Claude Code hooks and the Anthropic API (BYOK) Cursor Integration

Genchi's answer:

Node.js, Express, React, PostgreSQL, Redis, the Slack API, the Model Context Protocol (MCP), and AWS.

CloudByte PMS's answer

Engineering managers and CTOs at software teams of 10–200 developers who have rolled out AI coding tools and now need to answer: who's actually using them, what are they costing, which seats are idle, and is delivery actually improving? Reviewers and platform teams also use it for prompt governance and auditing.

Genchi's answer:

Engineering leaders responsible for several projects or teams at once, such as VPs and Directors of Engineering, CTOs, and engineering managers, who need to know which projects are heading for a missed deadline before it's too late to act.

CloudByte PMS's answer

CloudByte PMS is the analytics and management layer for AI-assisted engineering teams. A lightweight sync agent captures every Claude Code, GitHub Copilot, and Cursor session — prompts, responses, tokens, commits, and machine health — and surfaces it in one org-scoped dashboard with RBAC.

What no generic engineering-analytics tool gives you:

• Session-level telemetry — not just that AI tools are used, but how, by whom, and at what cost
• Per-developer adoption and cost per session, per project, per PR
• Ghost-seat detection — find paid seats nobody is using
• Prompt governance and a shared skills library across the team

Genchi's answer:

Genchi's project delivery predictions come from the people doing the work, not from a status report or ticket data. Each team member answers one question in an anonymous, one-click Slack vote: how confident are you the team will hit its goal by the deadline? Tracked over time, those votes show engineering leaders which projects are heading for a missed deadline, and which teams to leave alone.

CloudByte PMS's answer

Platforms like Jellyfish measure engineering output broadly; native Copilot analytics only cover Copilot. CloudByte PMS is purpose-built for AI coding telemetry across tools — Claude Code, GitHub Copilot, and Cursor in one dashboard.

• Commit-level AI attribution — AI-assisted vs. not, for the same developer
• DORA-metric correlation with AI-assisted commit ratios
• Ghost-seat detection and cost tracking across all four Anthropic token types
• BYOK Anthropic keys, self-hosted, and air-gapped deployment for stricter environments
• Free for teams up to 5 developers

Genchi's answer:

Standup bots collect what people did. Engineering analytics tools forecast from tickets and commits. Neither asks the people doing the work whether they believe the deadline will be met, which is the earliest signal you can get. Genchi captures it in about two seconds per person, with no status report, status meeting or chasing. Teams of up to 10 are free.

CloudByte PMS's answer

CloudByte PMS started when Pranav and Brijesh saw the same pattern across every engineering team adopting AI coding tools: shadow AI everywhere, costs nobody could track, and usage data trapped in silos — one tool per vendor dashboard, no single picture of what developers were actually doing.

They believed this problem deserved more than a point solution. Their vision: a common platform for AI engineering — telemetry, security, and governance in one place — where every team, whatever tools they use, can see adoption, control costs, and set guardrails without slowing developers down.

That vision became CloudByte PMS: a lightweight sync agent capturing every Claude Code, GitHub Copilot, and Cursor session, prompt, and commit into one org-scoped dashboard — built to make AI-assisted engineering visible, accountable, and safe for everyone.

Genchi's answer:

I spent years at Atlassian, a company that genuinely values transparency, and I still watched deadlines slip that the team had seen coming weeks earlier. The information existed. The channel for surfacing it didn't.

Conversations with engineering leaders at dozens of other companies told me this wasn't an Atlassian problem, it was an everyone problem. So I built the channel.