🚀 Launched PulseBoard — AI-powered incident investigation for B2B SaaS teams🚀 Launched PulseBoard — AI-powered incident investigation for
Hey everyone 👋
I'm Haseeb, a full-stack developer and indie hacker from India.
I recently launched PulseBoard — a monitoring platform that doesn't just tell you when your website or API goes down, but helps investigate why it happened.
Most uptime tools answer:
❌ "Your service is down."
PulseBoard is focused on answering:
✅ "What caused this incident?"
✅ "What changed before it happened?"
✅ "What evidence points to the root cause?"
It uses monitoring data, incident history, and AI-powered analysis to help teams understand production issues faster and generate postmortem insights.
We are currently validating it with real production monitoring and improving Vigil AI based on real-world incidents.
Would love feedback from B2B SaaS founders and builders:
👉 https://pulseboard.haseeb.work
What tools are you currently using for uptime monitoring and incident debugging?
Congrats on the launch, Haseeb!
The focus on root cause and "what changed" instead of just firing off a raw 500 error alert is a genuine pain point for smaller dev teams. At 2 AM, the "your site is down" notification isn't the hard part—it's the 20-minute hunt through logs and recent commits to figure out why.
Right now, a lot of builders lean on UptimeRobot or Better Stack for uptime pings, combined with Sentry for stack traces and Axiom/Datadog for log searching. The biggest friction in that stack is having to manually correlate the time of the ping failure with Sentry errors and recent Vercel/GitHub deployments.
A few quick feedback thoughts and questions on PulseBoard:
Context Sources: How does Vigil AI aggregate the "what changed before it happened" context? Are you hooking into GitHub deployment webhooks/commits, or analyzing response headers and error payloads from the endpoints?
Noise vs. Signal: AI postmortems are awesome, but guard hard against hallucinated root causes when log data is sparse. Giving users raw confidence scores or direct links to the underlying log evidence will build a lot of trust early on.
Wishing you great traction with PulseBoard! Will definitely check out the site.
Thanks a lot for the detailed feedback! This is exactly the problem we are trying to solve — the alert itself is usually the easy part, but understanding what actually caused the incident and connecting the timeline is where teams lose the most time.
Currently Vigil AI analyzes multiple context sources, including monitoring signals, incident history, GitHub changes, deployment context, and AWS infrastructure signals, to narrow down possible causes.
You also pointed out something we care about a lot: avoiding AI hallucinations. We have built guardrails around the investigation flow so Vigil focuses on evidence-backed explanations rather than making assumptions when the available data is limited.
Our thinking is simple: the more you connect, the less Vigil has to guess. More context from your systems gives Vigil more evidence to correlate and helps make investigations more reliable.
The confidence score and showing supporting evidence are great suggestions. We are moving toward making investigations more transparent — showing why Vigil reached a conclusion and which signals contributed to it.
Really appreciate the thoughtful feedback! Would love to hear your thoughts after trying it out.