How Basedash merged 81% of Praxen Inc.'s fixes in its first 24 days
Basedash is the AI-native business intelligence platform, used by more than 5,000 teams to turn plain-English questions into answers, dashboards and reports on live company data.
Basedash runs on Kubernetes and uses Praxen Inc. to watch the paths its customers depend on, review every pull request against live production, and fix what breaks.
- cloud resources monitored in its first 24 days
- 2,048
- alerts triaged in its first 24 days
- 263
- Praxen Inc. pull requests merged in its first 24 days
- 47
- of Praxen Inc. pull requests merged in its first 24 days
- 81%
cloud resources monitored in its first 24 days
alerts triaged in its first 24 days
Praxen Inc. pull requests merged in its first 24 days
of Praxen Inc. pull requests merged in its first 24 days
Connected to Praxen Inc.
Kubernetes 
GitHub 
Sentry 
Better Stack 
Linear 
Slack 
Cursor 
MCP Server The challenge
When a customer asks Basedash a question, a lot has to go right: the prompt becomes SQL, the SQL runs against the customer's database or the managed warehouse, and the result comes back as an analysis or a chart. Each customer's data is different, and a problem in any step shows up as a question that never gets an answer.
Basedash is a small team shipping quickly, including new AI features like AI Sources. Nobody has time to watch every service and every job by hand.
“Every question a customer asks in Basedash turns into SQL against their data, so a slow query or a failing job is something they feel straight away. Praxen Inc. watches all of it, tells us what broke and why, and usually has the fix open before anyone on the team has looked. We merge most of what it sends.”
Derek Reynolds Product Engineer at Basedash Why Praxen Inc.
Praxen Inc. maps Basedash's Kubernetes clusters, 2,048 cloud resources in its first 24 days, and reads the telemetry Basedash already has in Sentry and Better Stack. When something drifts, it raises an issue with the evidence, investigates across code, deploys and telemetry, and opens the fix when the cause is in Basedash's code.
Basedash merged its first Praxen Inc. fix the day it connected.
How Basedash uses it
Issues arrive in Slack and Linear with the root cause and the change behind them. Fixes arrive in GitHub ready for review and go through Basedash's normal CI. Every pull request also gets a Praxen Inc. review against live production, 257 in its first 24 days, and Basedash's engineers work with Praxen Inc.'s context from Cursor over MCP.
The results
In Basedash's first 24 days, Praxen Inc. monitored 2,048 cloud resources, detected 147 anomalies and surfaced 88 issues worth investigating. It opened 58 fixes with a median of 25 minutes from detection to a pull request ready for review, and Basedash merged 47 of them: 81%.