How Malveon works
Malveon connects to the tools your team already uses via OAuth, normalizes signals from all of them into one schema, and surfaces what matters through three focused layers. Nothing to migrate. Setup takes about 15 minutes.
Connect in 15 minutes
Malveon connects to your existing stack via OAuth. No agents to install, no data to export, no workflow changes for your team. Each integration takes under 3 minutes to authorize.
Your team keeps using Slack, GitHub, Jira, Linear, and Datadog exactly as before. Malveon reads from them, it does not replace any of them.
Currently in private beta. Integrations available: Slack, GitHub, Jira, Linear, Datadog, Notion.
One schema for everything
Every tool speaks a different format. A GitHub PR event, a Jira status update, a Slack thread, and a Datadog alert all contain related information, but nothing connects them by default.
Malveon's normalization engine converts incoming webhooks from all connected tools into a unified schema in under 300ms. A PR gets linked to the Jira ticket that spawned it, the Slack thread where the decision was made, and the deploy that shipped it.
This is what makes incident triage fast. When something breaks, Malveon already knows which PR, which deploy, which decision, and which team member, because it was watching the whole chain.
Three layers. Three problems solved.
Once signals are normalized, they surface through three focused layers. Each layer solves one problem your team faces every day.
Malve
Decisions made in Slack threads disappear within 48 hours. Malve captures them, links them to the relevant code or task, and makes them searchable. Six months from now, you can find why that architectural choice was made and who made it.
Who uses it: Engineering managers and team leads. Daily usage: checking decisions before starting new work, reviewing context during incident triage.
Replaces or augments: Slack search, Notion decision logs, Confluence pages nobody reads.
Malvedeck
Jira says 80% complete. Three blockers are sitting unresolved. Two engineers are context-switching across four projects. Your sprint will not close on time, but you will not know that until Friday.
Malvedeck pulls real status from blockers, PR review queues, CI failures, and team capacity. The health score reflects what is actually happening, not what someone last updated three days ago.
Who uses it: Engineering managers and heads of engineering. Daily usage: morning standup prep, weekly sprint health check, capacity planning.
Replaces or augments: Manual Jira status updates, weekly status reports, spreadsheet tracking.
Malviont: Fridy VS Code Extension
Most context switching happens because engineers have to leave their editor to check Jira, Slack, or Linear. Fridy brings that context directly into VS Code: relevant tasks, deploy safety warnings, and CI status alongside your code.
When an incident happens, Malviont surfaces the relevant PR, the deploy that triggered it, and the Slack thread with context, automatically, in under 10 minutes.
Who uses it: Individual engineers and leads. Daily usage: pre-deploy checks, incident triage, seeing task context without switching tabs.
Replaces or augments: Manual GitHub diffs before deploys, 45-minute incident scrambles, tab-switching to Linear or Jira.
Common questions
Ready to see it?
We are onboarding a small first cohort of engineering teams. $99/month flat, no per-seat pricing, no credit card for the waitlist.