Steadwing

Your autonomous on-call engineer: your Incident response on autopilot

[Steadwing @steadwing]

During an incident, engineers are manually searching logs across CloudWatch, Elasticsearch, Loki or wherever the logs live. They're looking around time ranges, crafting queries and hoping they're looking in the right place.

Steadwing connects to CloudWatch, GCP Logging, Elasticsearch, Mezmo, Scalyr and many more. When an investigation triggers, it queries all connected log sources in parallel searching for error messages, stack traces and anomalies around the incident timeframe.

The relevant log lines show up in the RCA with timestamps and context. So engineers don't search for logs.

For teams running multiple log platforms across different services: Steadwing searches all of them simultaneously.

[Steadwing @steadwing]

Steadwing doesn't replace your observability or alerting tools. It connects them altogether.

When PagerDuty fires an alert, Steadwing pulls incident timelines, escalation data and response metrics. Simultaneously, it queries Datadog for logs, APM data, and triggered monitors around the incident timeframe.

The result: your on-call engineer gets a complete picture alert context from PagerDuty correlated with logs from Datadog, code changes from GitHub, and performance data from your AWS all in one RCA.

Engineering team spent years building the observability stack. We make it work as one system during incidents.

[Steadwing @steadwing]

Most production incidents trace back to something that changed recently. A deploy. A config update. A merged PR.

Steadwing connects to your GitHub or GitLab repos and automatically correlates incidents with recent commits, deployments and releases. When something breaks, the RCA shows you which specific change caused it down to the exact commit and code diff.

No more asking "did anyone deploy something?" in Slack. No more manually scanning CI/CD pipelines. The correlation happens automatically as part of the investigation.

[Steadwing @steadwing]

Engineers work in Slack already during incidents. That's where they get the alert, they coordinate the response and share updates.

Steadwing is a perfect fit for Slack. When an alert comes in, it automatically investigates your entire stack logs, metrics, traces, code changes and delivers the root cause analysis right back into Slack.

No context switching. No opening multiple tabs. The RCA appears where the team is already working with links back to each source tool for evidence.

Engineers won't have to learn a new interface or change their workflow. They get the diagnosis where they already are.

[Abejith @Abejith]

5M tokens later Codex has a pet for @steadwing. Very impressed!

[Steadwing @steadwing]

Here's a number that doesn't show up in most engineering dashboards.

Each after-hours incident costs roughly 4–6 hours of productive engineering time the following day. Not the incident itself. The day after.

2 hours on post-mortem docs and Slack async. 1 hour in a meeting too tired to contribute. 1 hour of feature work that just doesn't happen.

For an on-call engineer handling 3 incidents a week which nearly half of SREs report that's 50+ hours of lost productivity a month.

This never appears in your MTTR dashboard. But it shows up in velocity and attrition.

The real ROI of reducing on-call burden isn't faster incidents. It's preserving the other 99% of your team's engineering time.

[Steadwing @steadwing]

The AI for incident response space went through two very different eras.

AIOps was about anomaly detection. Structured metrics. The output was a correlation score and an alert. No diagnosis. No code understanding. Remediation was mostly "roll back".

LLM-powered RCA is a completely different approach. It reads logs, metrics, traces, code diffs, configs, and deploy history. It generates a natural language root cause analysis with an evidence chain you can actually verify. It references past incidents. It suggests fixes on code level, infra and CI/CD.

The shift isn't just better models. It's a fundamentally different architecture from pattern-matching on metrics to actually understanding what your system is doing and why it broke.

That's the approach we took with Steadwing to show evidence-based RCA with fixes you can verify and immediately ship.

[Abejith @Abejith]

All set for the big day at @join_ef.
Come say hi if you drop by Demo Day today!

@khant_dev @steadwing

[Shorya Jain @real_shorya]

“On-call engineers shouldn’t exist.”
@Abejith (ex-Freshworks) said it. @khant_dev (ex-Mem0) had lived it.
Weeks later: live with real teams.
Demo: a bug a team couldn’t triangulate for years → root cause in seconds.
Most RCA tools fail.
Watch out for @steadwing at @join_ef Demo Day 👀

[Abejith @Abejith]

We’re excited to share @steadwing at Entrepreneurs First @join_ef Demo Day on April 29, 2026.
It’s a special milestone for the team, and another step toward the future we believe in.
Let’s make production software self-healing!

[Steadwing @steadwing]

30 alerts in 2 minutes. Your on-call engineer opens Slack and gets overwhelmed.

It's rarely 30 problems, usually one issue causing a chain reaction.

They end up triaging each alert & 20 minutes are gone.

We group alerts into 1 incident & give root cause for it.

[Dev Khant @khant_dev]

As engineers, we know that the hardest part of an incident isn't figuring out how to fix it. It's putting together the context from all the tools and coming up with the reason why it happened.

Those first critical minutes are spent jumping across logs, metrics, traces, and recent changes and just trying to understand what’s going on.

And that's why we are building @steadwing!

It takes info from your logs, metrics, traces and codebase, connects the dots across your whole stack, and gives you a full root cause analysis with automatable fixes on code level, around deployment, and infra.

The investigation is over by the time your on-call person opens the laptop.

[Steadwing @steadwing]

We’re now SOC 2 Type II compliant!

[Daytona @daytonaio]

Almost a month ago, we brought the inaugural
@daytonaio Compute Conference to the Chase Center, and the energy is still with us.

Today, we’re sharing the official aftermovie. 🎬A look back at the people, ideas, and momentum that made it so special.

[Abejith @Abejith]

Seattle, we are in you! 😂

@usenix @SREcon @steadwing

If you’re here for SRECon26 Americas, hit me up!

[AI+ @aiplus_hq]

// last Sunday, we assembled 2000 of the best AI founders, researchers, investors and engineers in one room

where we brought together 45 speakers and 50 of the hottest startups in AI, one day before Nvidia's GTC

🔥 here's a recap of the first ever AI+ Renaissance conference

[AI+ @aiplus_hq]

this could not happen with our partners at @smallest_AI @Tiny_Fish @hydra_db @agi_inc @composio @EragonAI @theagentic Gaia, Impossible, CloudMile, Hat-trick Capital, @EntelligenceAI

and our exhibitors @wayo_ai @SolveaCX @steadwing @PerleLabs @trybytesAI @withpaygent VensureHR, @hyperspell @scalekitinc and @palebluedot_ai

this year was amazing, and all of you made it our best conference yet

see you in 2027 🥂

[Abejith @Abejith]

We are super excited for the @Aiplus__ renaissance 2026. Drop by if you are around for the Nvidia GTC weekend!

@lynn_aisv
@0xJsum

[Dev Khant @khant_dev]

We live at @Aiplus__ Renaissance 2026!

Drop by if you are around.

[Steadwing @steadwing]

Gearing up for the @Aiplus__ Renaissance 2026 🚀

Drop by if you are around!