What it is
- A memory layer that ingests post-mortems from your existing tools.
- A private archive scoped to your org, queryable by humans and AI agents.
- A neutral Switzerland between every incident tool you've used or might use next.
Incident intelligence that compounds over time
Your engineering org's production memory.
Aftermath turns the post-mortems you've already written into a queryable archive. Searchable by your team. Connectable to your AI coding agents. Without changing how you run incidents today.
Early access
Get early access
- Early access to the private beta
- Shape what we build first — we'll reach out directly
- First to connect when we open the doors
How it works
Three steps. No new incident workflow.
Aftermath does not change how your team runs incidents. It reads what you've already written and makes it useful again.
- 1
Connect your sources.
GitHub, incident.io, Rootly, and the docs tools where post-mortems already live.
- 2
Aftermath embeds your corpus.
Every post-mortem becomes a structured, searchable encore in your private Playback.
- 3
Query from anywhere.
Search Playback in the dashboard, or hook the private MCP endpoint into Claude Code and Cursor.
How Aftermath Fits Your Stack
Incident intelligence, not another response tool.
Your team keeps using PagerDuty, incident.io, Rootly, Sentry, and the rest of your response stack. Aftermath sits beside that layer, never replaces it. We ingest the post-mortems you already write, wherever they live, and turn them into memory your team and your AI agents can actually use.
What it is not
- An incident response, on-call, or alerting tool.
- A replacement for PagerDuty, incident.io, or Rootly.
- An observability platform.
Lineup
Bring your post-mortems from wherever they live.
Aftermath connects to the tools your team already uses. No migration. No rewriting. We meet you where your incident history lives today.
- Available at launch
GitHub
Markdown post-mortems committed to repos.
- Available at launch
incident.io
Post-mortem documents via the official API.
- Available at launch
Rootly
Retrospectives via the official API.
- Coming soon
Notion
Post-mortem pages in your workspace.
- Coming soon
Google Docs
Post-mortem docs in shared drives.
- Coming soon
Confluence
Post-mortem pages in your spaces.
For your AI agents
Your coding agents finally know what has broken before.
Aftermath ships a private MCP endpoint scoped to your org plus installable skills for Claude Code and Cursor. Your agents query your real incident history, not a generic web search.
Private MCP endpoint
Every Aftermath org gets a private MCP URL. Connect it in Claude Code, Cursor, or any MCP-aware client. Your agent can search, fetch, and reason over your private Playback in natural language.
Three installable skills
Pre-PR incident check
Surface related past incidents from the diff before the engineer ships.
Runbook generator
Synthesize a runbook for a service from past incidents involving it.
Similar-incident finder
Given a current alert or symptom, return the closest matches from Playback with resolution context.
What Your Team Gets
What changes for your team after adoption
Your incident workflow stays in the tools you already use. Aftermath adds the memory layer: a queryable archive of every post-mortem your team has ever written, accessible to engineers and AI agents alike.
Engineers find prior context in seconds.
Past incidents are searchable by meaning, not keywords. The right post-mortem surfaces even when it predates your current incident tool.
Leadership sees patterns across tools.
Trends, repeat offenders, and unresolved follow-ups become visible in one place, without manual reporting.
Operational memory compounds.
Every post-mortem your team writes becomes reusable context for future incidents.
AI agents stop flying blind.
Cursor and Claude Code learn from your real production history through MCP and skills, not public training data.
Who It's For
Built for engineering organizations that already run incident reviews.
If your team is already writing post-mortems somewhere, Aftermath turns that effort into compounding memory. The more incidents you've reviewed, the more value you get on day one.
Leadership
Leaders see the full history of incidents across every tool the team has used. Trends, patterns, and unresolved follow-ups become visible without manual reporting.
Engineers and SREs
Engineers query the team's full incident history from the dashboard or directly from Claude Code and Cursor. The right past post-mortem surfaces in seconds, even when it predates your current incident tool.
How it fits together
Playback is what ships first. The rest fills in around it.
Aftermath is built as a memory layer with collection and synthesis layers around it. v1 is Playback. Encore, Live Stage, Cues, Lineup-for-live-incidents, and Monitors are next.
Available now
Playback
Search past incidents by meaning. Browse a unified archive of every post-mortem your team has ever written, across every tool. Queryable by your team and by the AI agents writing your code.
Coming next
Encore
When an incident closes, Encore drafts the post-mortem from chat threads, linked PRs, and deploy timestamps. Each Encore lands in Playback automatically, so the corpus deepens with every incident.
On the roadmap
The full memory loop, in build order.
Cues
Incident intake from any alerting source. Manual or webhook-triggered. Opens a Live Stage when an incident starts.
FutureLineup
Connect the tools where your post-mortems already live: GitHub, incident.io, Rootly. Notion, Google Docs, and Confluence coming soon.
Available now (post-mortem sources)Monitors
Slackbot that captures decisions, links, and rollback confirmations during a live incident. Feeds Encore.
FutureEncore
AI-drafted post-mortems from incident data. Lands in Playback. The first expansion after Playback.
Coming next
Why Now
Three shifts make the memory layer possible now
A modern model can read an incident timeline, chat thread, deploy log, and Sentry trace, then produce a post-mortem an engineer would edit, not rewrite. That quality bar did not exist a few years ago.
AI coding agents have no production memory unless you supply it. The team that owns the canonical production memory layer becomes infrastructure for every agent that touches the codebase.
Incident tooling has fragmented. A typical mid-market team has post-mortems in PagerDuty, incident.io, Rootly, old GitHub repos, Notion, and Google Docs. No single vendor will ever ingest from competitors. That fragmentation is the opening for a Switzerland memory layer.