How it works

How Malveon works

Malveon connects to the tools your team already uses over 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.

The pipeline

Connect, normalize, surface

Step 1

Connect

OAuth into Slack, GitHub, Jira, Linear, Datadog, and Notion. Each integration authorizes in 2 to 3 minutes. No agents, no exports.

Step 2

Normalize

Incoming webhooks from every tool are converted into one unified schema, so a PR links to the ticket that spawned it, the decision behind it, and the deploy that shipped it.

Step 3

Surface

Normalized signals surface through the three layers: Malve for context, Malvedeck for health, Malviont for execution and safety.

Layer 01, Malve

Decisions stop disappearing in Slack

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, without archaeology across a dozen channels.

Who uses it: Engineering managers and team leads. Checking decisions before new work, reviewing context during incident triage.
Replaces or augments: Slack search, Notion decision logs, Confluence pages nobody reads.

Layer 02, Malvedeck

Project health from real signals, not status updates

Jira says 80% complete. Three blockers sit unresolved, two engineers are context-switching across four projects, and your sprint will not close on time. 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 updated three days ago.

Who uses it: Engineering managers and heads of engineering. Standup prep, weekly sprint health, capacity planning.
Replaces or augments: Manual Jira status updates, weekly status reports, spreadsheet tracking.

Layer 03, Malviont

CI/CD, incidents, and deploy safety, ranked in one view

Malviont covers CI/CD monitoring, incident correlation, on-call visibility, and deploy safety. Fridy brings that same context into VS Code: relevant tasks, deploy safety warnings, and CI status alongside your code, so engineers stop leaving the editor to check Jira, Slack, or Linear.

When an incident happens, Malviont surfaces the relevant PR, the deploy that triggered it, and the Slack thread with context, automatically, with a goal of under 10 minutes.

Who uses it: DevOps, tech leads, and individual engineers. CI/CD monitoring, on-call visibility, pre-deploy checks, incident triage, task context without switching tabs.
Replaces or augments: Manual GitHub diffs before deploys, long incident scrambles, tab-switching to Linear or Jira, checking six separate dashboards.

Connect your stack

OAuth-first, webhook-driven, nothing to migrate

Malveon connects over OAuth and listens to webhooks. It reads from your tools in real time and never writes back without permission. Your team keeps using Slack, GitHub, Jira, Linear, and Datadog exactly as before. Currently in private beta.