Encore
AI-drafted post-mortem
When the Live Stage closes, Encore drafts the post-mortem from the timeline, Slack thread, linked PRs, and deploy logs Aftermath already captured. You edit and ship, not rebuild it from memory at 4am.
For engineering teams that do not have a dedicated SRE.
Run the show. Review the tape. Remember every fix.
Your team ships faster than ever, and a lot of it is AI-written. Aftermath captures every incident, drafts the post-mortem, and turns it into memory your engineers and your agents can query, so the same failure doesn't ship twice.
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
The lifecycle
One product, the whole incident lifecycle.
Aftermath covers everything from the alert that opens the incident to the post-mortem that closes it, and the memory that survives afterward. You wear the same harness through all of it.
- 1
Something breaks
Cues
Any alert opens a Cue. PagerDuty, Sentry, UptimeRobot, GitHub Actions, or a webhook. The incident exists the moment it starts.
- 2
While it's happening
Live Stage
A real-time incident page becomes the single source of truth. Services, links, timeline, Slack thread, responders. Everyone sees the same picture.
- 3
When it closes
Encore
Aftermath drafts the post-mortem from what Live Stage already captured. You edit, not rewrite.
- 4
Forever after
Playback
Every Encore lands in Playback: a queryable production memory for your team and your AI agents. The corpus deepens with every incident.
Live Stage: during the incident
The harness you wear.
When a Cue fires, Live Stage opens. It is the single source of truth for the incident: structured, real-time, and ready for the next person to jump in without a handoff doc.
Aftermath runs alongside you when you're on call and captures everything that would otherwise live in your head until Monday. It does not page anyone, and it does not take actions in production.
One incident page, not ten tabs
Services, dashboards, Slack thread, call URL, runbooks, responders, and a running timeline in one place. Stop hunting for the right link mid-incident.
Slack thread, captured
Decisions, observations, rollback confirmations, and blame land in the timeline automatically. Nothing important gets lost in scrollback.
Everyone sees the same picture
Responders, leads, and executives read the same incident page. No status update threads. No 'what's happening' questions.
Past context, surfaced live
When the incident touches a service that has broken before, Playback surfaces the prior post-mortem inline. You don't have to remember it was 14 months ago.
Encore + Playback: after the incident, forever
The memory builds itself.
When the incident closes, Encore drafts the post-mortem. It lands in Playback, a queryable production memory your team and your AI agents can use the next time something looks familiar. Every incident makes the next one faster.
Playback
Production memory
Every Encore lands in Playback alongside post-mortems you have already written elsewhere. Search by meaning, not keywords. Exposed to your AI agents over MCP, so Cursor and Claude Code know what has broken before. Audit-friendly export when someone asks for the record.
Situation Rooms
Group every incident under the event that caused them.
Some incidents do not start with a page. Black Friday, a database migration, a product launch: high-exposure windows where every minute of downtime has a measurable cost. Situation Rooms let you scope incidents to the business event that caused them, and generate a retrospective when the window closes.
Black Friday
Open a Situation Room for the window. Every incident that fires inside it gets grouped automatically. One retrospective at the end, with per-minute cost.
Major migration
Tag a database cutover or Kubernetes upgrade. Aftermath rolls up every incident the migration caused into a single review.
Product launch
Define the launch window and get a clean record of what broke, when, and how it was resolved, without manually correlating incidents days later.
Lineup: integrations
Plugs into the stack you already run.
Lineup connects Aftermath to your alerting tools at incident time and to the places your past post-mortems already live. No migration. No rewriting. We meet you where your incident workflow runs today.
- Available at launch
PagerDuty
Trigger Cues from PagerDuty alerts.
- Available at launch
Sentry
Open a Live Stage when a Sentry issue fires.
- Available at launch
Slack
Capture the incident thread into the Live Stage timeline.
- Available at launch
GitHub
Pull markdown post-mortems committed to repos.
- Available at launch
incident.io & Rootly
Backfill post-mortems from the incident tool you already use.
- Coming soon
Notion, Google Docs, Confluence
For teams whose post-mortems live in docs, not in a tool.
For your AI agents
AI agents finally know what has broken before.
Every Aftermath org gets a private MCP endpoint and installable skills for the agent harnesses your engineers already use. Agents reason over your real production memory, 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 you ship.
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.
How Aftermath fits
A harness for you, not a replacement for the SRE you don't have.
Autonomous AI SRE products investigate, fix, and verify in production. They are built for enterprise teams with the budget, security review, and risk tolerance to put an agent in the critical path of a live system. Most engineering teams can't put an agent in the critical path and don't need to.
Aftermath sits beside your incident response stack (PagerDuty, incident.io, Rootly, Sentry, your observability platform) and gives you a coordination surface during the incident, an AI-drafted post-mortem after, and a production memory that compounds with every fix. Read-only by default. No production access required to land an incident.
What it is
- A harness for you, the on-call engineer who is also the SRE.
- A coordination surface during the incident and a memory layer after.
- Neutral ground between every incident tool you have used or might use next.
What it is not
- An autonomous AI SRE that investigates and takes action in production.
- A replacement for PagerDuty, incident.io, Rootly, or your observability stack.
- An on-call rotation manager or alerting platform.
Who it's for
For engineering teams that ship faster than they staff.
If you are the SRE, the post-mortem author, and the reliability reporter, Aftermath gives you the harness you have been building piecemeal in Notion and Slack threads.
You, the on-call engineer who is also the SRE
You wear the pager, run the incident, write the post-mortem, and present the metrics. Aftermath is the harness that holds all of it together so you do not start from zero every time.
Leadership without a dedicated SRE
Series A through C. Ten to two hundred engineers. You need reliability without a five-person platform team. Aftermath gives your strongest engineers the harness a dedicated SRE org would have built for them.
Teams whose engineers work with AI agents
Your engineers ship code with Cursor and Claude Code. Those agents have no memory of what has broken in your system. Aftermath gives them one over MCP, so the next PR knows what failed last time.
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 you would edit, not rewrite. That quality bar did not exist a few years ago.
Incident tooling has fragmented. A typical engineering 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 neutral memory layer.
And the autonomous AI SRE wave is built for enterprise teams. Most engineering teams cannot put an agent in production, do not have the security review budget, and do not need replacement. They need a harness for you, the engineer they already trust.