Your engineering organization, in every AI you use
Add Code Climate to the AI you already use, and answers about your organization go from confident to correct.
Works with
and any MCP-compatible AI tool
VP → Director
Mon 9:14
Why did Checkout slow down?
Director → Manager
Mon 2:40
Is Checkout behind?
Manager → Analyst
Tue 11:02
Can you pull Checkout velocity?
Analyst → VP
Thu 4:55
Checkout sprint velocity, last 6 sprints.
Mon 9:14
Thu 4:55
4 days. 4 people. An answer to a different question.
You ask how a team is doing. Your director asks a manager, the manager asks an analyst, someone pulls a dashboard, and days later an answer comes back, shaped by everyone who touched it.
Every handoff loses a little of what you meant. By the time it reaches whoever builds the dashboard, your question has become a request for a chart, and what comes back answers something else.
Your AI assistant is brilliant. It just doesn't know your engineering organization: who's on which team, what changed and why. So it answers anyway, confidently, from half the picture. You can't tell which half, so the question goes back down the chain. Code Climate is the missing piece.
How it works
Code Climate gives your AI the context it needs, so you can ask directly and skip the game of telephone. An admin connects your engineering tools once, and each leader adds Code Climate to Claude or ChatGPT in a minute. We resolve identities and normalize your data, and we keep the context only your people have. Your answers show up where you already work: your AI assistant, Slack, your spreadsheets, your decks.
Your tools
GitHub
Bitbucket
Jira
Linear
Google Calendar
Your HR system
What your tools know
Identity resolution
Teams and reporting lines
Delivery metrics
Deploy history
Every identity resolved to one person, every person on the right team.
Code Climate
What your people know
Jul 14
Payments split into Core and Checkout
Director, Platform
Aug 4
2 Checkout engineers start 30-day rotation to product management
Manager, Checkout
Sep 30
Mobile release freeze through October
VP Eng
Added once by the people who know. Dated, tied to the right team, and kept.
Your people
Director, Platform
Manager, Checkout
VP Eng
MCP · API · Web
Where your answers show up
Slack
via MCP
# eng-leadership
Code Climate
APP
9:02 AM
What changed overnight
• Checkout review time is still climbing
• Payments rolled back two deploys
• Mobile freeze starts Sep 30
2 replies
Last reply today at 9:14 AM
Monday morning brief
Claude or ChatGPT
via MCP
Why did Checkout slow down?
Used Code Climate
Delivery data
Human context
Two Checkout engineers started a 30-day product rotation on Aug 4. Review time rose with fewer reviewers, and it eases once they're back.
An answer with the why
Your Code Climate library
via MCP
Save it to my library and share it with my directors.
Saved as "Why Checkout slowed down," with its data.
Library
Shared with directors
Saved back to Code Climate, and shared
Excel or Sheets
via MCP
B3
18.4
A
B
C
1
Team
Review time (hrs)
Deploys
2
Payments Core
9.2
42
3
Checkout
18.4
27
4
Mobile
11.0
9
5
Platform
7.6
55
MBR
Raw data
MBR numbers by team
PowerPoint or Slides
via MCP
Q3 delivery by team
Checkout recovering after the July split
A QBR slide, ready to present
Your BI
via API
Engineering delivery
Team: All
Last 90 days
Teams
12
Deploys
Up
Review
Steady
Delivery by team in your dashboards
Code Climate Analytics
via Web
Checkout review time
Jul 14 · Payments split
A deep dive into one change
Sheets, decks and briefs are made in the AI you already use, with Code Climate data. Your agent gets the same access and permissions you do, never more.
Why you need it
A free MCP server can connect your AI to GitHub in an afternoon. That gets you confident answers. Correct ones take the six layers above that connection, and we've already built them.
Layer 1 · What a free MCP server gets you
Your tools' data, connected and kept working every time a vendor changes its API.
Adds to the answerPulls Checkout's pull requests, issues and deploys.
Where free stopsThis is all a free MCP server does. Everything above it, your AI has to guess.
Layer 2
One person record across every tool.
Adds to the answerjane@corp.com, jdoe-gh, @jane and "JD" in Jira become one Jane Doe.
Without itJane counts as four people, and every per-person number is wrong.
Layer 3
From your HR system, kept current as people move.
Adds to the answerCheckout split from Payments on Jul 14.
Without itThe split is invisible, so Checkout just looks slow.
Layer 4
Code, project and deploy data tied to the same people and teams.
Adds to the answerCheckout's commits, PRs, issues and deploys land on one team and one timeline.
Without itGit says one thing, Jira says another, and your AI picks one.
Layer 5
Captured once by the people who know, tied to the right team and dates.
Adds to the answerFrom the Checkout manager: two engineers start a 30-day product rotation Aug 4. Expect review time to rise until they're back.
Without itYour AI blames the team, or the engineers still on it.
Layer 6
Scoped by team, person or management chain. SOC 2 Type II.
Adds to the answerScopes it to the teams you're allowed to see.
Without itYour AI sees too little to answer, or more than you should.
Layer 7
MCP server, API and app, behind one SSO login.
Adds to the answerDelivers it in Claude, ChatGPT, Slack or your BI, wherever you asked.
Without itThe right answer sits in a dashboard nobody opens.
Out the top
Checkout slowed after it split from Payments on Jul 14, and review time rose while two engineers were out on a 30-day product rotation. It's easing now that they're back.
Free MCP server aloneExample"Checkout's velocity is down. Consider reviewing team performance."
Claude · with Code Climate
Checkout slowed after it split from Payments on Jul 14, and review time rose while two engineers were out on a 30-day product rotation. It's easing now that they're back.
Why did Checkout slow down?
Getting started
Start with your own team, and scale to your whole organization when you're ready. Early-access leaders help shape what we build next.
1
Admin, once
Your admin connects your tools
Your engineering tools, connected for the whole organization.
2
Each leader, once
Each leader adds Code Climate to their AI
Claude, ChatGPT, GitHub Copilot, Grok Bot or any MCP-compatible tool.
3
Everyone, every day
Ask about your organization in your own words
Answers come back with the context behind them, where you already work.
Security
Code Climate inherits the permissions your teams already maintain. Connect it to Claude or ChatGPT, and the AI gets the same access you have, never more.
SOC 2 Type II
Independently audited controls for how we handle your data.
SSO and SAML
Sign in through the identity provider you already use.
Role-based access control
Admins decide what each role can see and do, like who sees individual-level data and who can add context.
Scoped to you
Your AI gets live engineering data for your part of the organization, following the permissions your teams already maintain.
Encrypted in transit and at rest
Your data is encrypted while it moves and while it's stored.
No model training
We never use your data to train AI models.
We work with your security and compliance teams to get sources like HR and calendar approved.
SSO, permissions and API access come with every plan. They're never held back for an enterprise tier.
Fifteen years of watching teams ship, reorganize and change their tools is why Code Climate knows what your data means, and why your AI's answers come back right. Our leadership came out of Pivotal, where we helped large organizations through two big shifts: agile, then cloud. At Code Climate, we're focused on the next one, helping engineering organizations become AI-native. Historically, the tooling changes much faster than the structure and operations. The gap between them is where the value leaks.
2011
Since then, thousands of engineering leaders, from startups to Fortune 500 companies, have used Code Climate to improve their organizations with data.
2018
One of the first platforms to connect people, teams and delivery data for engineering leaders.
Now
Fifteen years of it, now in Claude, ChatGPT, GitHub Copilot or any MCP-compatible AI tool.
For engineering leaders who want their AI to understand their organization. Request early access, start with your own team, and help shape what we build next.