Use case

Business observability for SaaS

Business observability is reading a SaaS company's tools as one record: revenue, product usage, releases, errors, support and the CRM, kept in history and set against each other. It answers the question every founder asks after an alert: what changed, who did it hit, and what does it cost? Vesqo is a business observability tool, built for founders and small teams running one or several SaaS products.

The problem

Every tool in a SaaS stack observes one thing well. Sentry sees errors, PostHog sees events, Stripe sees payments, Crisp sees conversations, GitHub sees releases. Each has its own alerts, its own dashboard and no idea the others exist. When something goes wrong, the person on call opens five tabs and rebuilds the story by hand: the release at 12:15, the errors at 12:24, the checkouts, the failed payments, the chats. It takes an hour, and it is the same hour every time.

Engineering has had observability for a decade: traces, metrics and logs read together, so a slow request can be followed through every service it touched. The business side of a SaaS has nothing like it. Revenue, usage, errors and support are read in four tools by four people, and the correlation between them lives in someone's head.

What Vesqo does

Vesqo connects to the tools you already run, read-only, every fifteen minutes, and keeps a history of each. Customers are joined across sources: a Stripe customer, a Sentry user, a PostHog group, a support contact and a CRM company with the same email domain are one account, with what they pay.

Twice an hour, and the minute a release ships, a set of rules reads the sources against each other and against the same hours the week before. When they disagree, Vesqo writes a signal: what changed, the release it dates to, the customers in it, the money, and the next step. Critical ones reach the team at once; the rest go into one email at seven.

Everything it read is one question away. Ask Vesqo answers from the same data, with names, amounts and what it looked at, and every answer is in the audit log.

Example

Questions it answers

  • What changed since yesterday, and what does each change cost?
  • Which paying customers were affected by this incident?
  • Is the drop in checkouts a release, a customer going quiet, or a quiet Sunday?
  • Which customers are at risk this month, and why?

See what it finds in a workspace with a bad afternoon in it.

The demo is Farol Jobs, a made-up job board with a bad release in it, every screen open. Ask it anything. Then connect your own.

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