Your SLOs, kept. Every month.
SteadyNines watches the burn rate of every error budget and acts while there is still budget left, across operations and code. An AI SRE works with your SREs. An AI engineer works with your developers. Nobody changes their tools.
Runs inside your estate, with your tools, under your governance.
One SLO over six months. Each bump smaller than the last.
Illustrative · 99 of 100 checkouts under 2s · budget 1 in 100A change goes wrong
Seen as the budget started burning. AI SRE proposed the rollback, the on-call approved from Teams. Fix drafted in the editor, shipped Thursday.
Capacity creeping up
Seen a week before it would fail. Two connections added, approved from a phone. No incident, no ticket queue.
The same pattern again
A known situation. Handled alone within the limits you set, two minutes, Friday night. Your engineer told Saturday morning.
Every company with SLOs misses them the same three ways
The SLO is defined. The signals exist. The loop that keeps the SLO is still run by people, after the fact.
Error budget violations are handled after the budget is gone
Alerts fire on breach, when customers are already affected. Then feature work stops until the budget recovers.
Shipping stops.
Operations and engineering are disconnected
Each side has its own tools and its own context. Nothing correlates what operations saw with what engineering changed.
Context is lost at every handoff.
Every fix lives in someone’s head
What fixed it last time is not written anywhere a system can use. When that engineer is away, the recurrence starts from zero.
Reliability scales with headcount.
See the burn. Act on both sides. Learn every time.
Not a better alert and not a faster investigation. A closed loop that runs all the time, on one connected context graph of your estate, with an arm in operations and an arm in code.
Burn rate, every minute, on every SLO
SteadyNines reads the SLIs you already collect and watches how fast each error budget is being spent. A deploy that went wrong, capacity creeping, a risky scheduled change: all seen while there is still budget left, days before a breach.
One connected context graph
Discovered automatically and correlated, so what operations sees and what engineering changed are one picture.
Two arms, one picture
AI SRE, with your SREs
Stops the burn now: rollback, scale, patch. Writes the incident and the problem record as it goes. Your on-call approves from Teams or a phone, with the evidence attached.
- Mitigation proposed with the exact blast radius
- Incident and problem records written automatically
- Verifies the SLI recovered before closing
AI engineer, with your developers
Removes the cause for good. The ticket arrives in the editor with the full trail from the incident, a drafted fix, and the deploy gated on the budget.
- Fix drafted where the developer already works
- Incident, problem and change linked end to end
- Deploy verified against the SLO, written back
Nobody changes their tools. Engineers approve from Teams, their phone, or their IDE. The console is where you observe, govern and tune.
Every situation becomes a known situation
A correlated learning engine stores what burned, what each side did, what failed, what worked, and how the SLO recovered. The next time the same situation starts, SteadyNines starts from that record instead of from zero. It earns more autonomy with every situation your team approves.
Each time it needs less of your team
The loop is the same seven steps. What changes is who does each one, and whether the context survives the handoff.
Today
No SteadyNines
- 1Breach alert firesbudget already gonehuman
- 2On-call investigates40 min, three toolshuman
- 3Rollbackhuman
- 4Ticket to devcontext lost herehuman
- 5Dev re-investigateshuman
- 6Fix, deployhuman
- 7Nothing written backnone
First time
With SteadyNines
- 1Burn rate risingseen before breachAI
- 2Cause correlated on the graphAI
- 3Rollback proposed, approvedone tap in TeamsAI + human
- 4Ticket with full trailAI
- 5Fix drafted in editorAI + human
- 6Deploy gated on budgetAI + human
- 7Situation recordedAI
Tenth time
Known situation
- 1Burn rate risingAI
- 2Matched to known situationAI
- 3Rollback, run and reportedwithin limits you setAI
- 4Ticket with full trailAI
- 5Fix drafted, reviewedAI + human
- 6Deploy gated on budgetAI
- 7Record updatedAI
You decide how much it may do, per situation type
No unguided AI in production. Autonomy is granted one situation type at a time, as the record earns it, and can be changed at any time from one screen.
| Mode | What happens | When it fits |
|---|---|---|
| Propose | SteadyNines assembles the evidence and the recommended action. A person executes. | New or high-impact situations. |
| Ask | Everything is prepared. A one-tap approval goes to the engineer’s phone or Teams. | Situations seen before, with a good record. |
| Run, then tell | SteadyNines acts within minutes and notifies. The full record is there the next morning. | Situations with a consistent record you have chosen to release. |
One timeline is the audit trail
Every action, by a person or by SteadyNines, sits on one timeline with its evidence. That is what your auditor sees.
Data stays inside
Connectors and the executor run inside your estate, outbound only. The control plane can be hosted or deployed into your environment.
Built for regulated industries
Role and attribute based access for humans and agents, encryption in transit and at rest, and secured human access integrated with the loop.
Everyone else watches SLOs. SteadyNines keeps them.
SLO tools, AI SRE tools and coding agents each work one disconnected piece of the loop, and all of them act after the breach.
The market today
- Measure the burn and alert
- Investigate the incident once it is paged
- Fix what a human says is broken
- Each tool works its own piece of the loop
- Starts from zero each time
SteadyNines
- Acts on burn rate, before the budget is gone
- Stops the burn and removes the cause, on one picture
- Connects what operations saw to what engineering changed
- One loop, from signal to verified deploy, written back
- Starts from the known situation, with autonomy you granted
Your tools stay. SteadyNines connects to what you already run.
Forty years building the AIOps products that major MSPs run on
Through every generation of the technology: ML-based, deep learning, GenAI, and now agentic operations. We shipped proactive problem management and secured human access into large enterprises and regulated industries.
Balavigneshwaran M
Co-founder · 17 years in AIOps
Reliability and operations products for managed service providers and large enterprises.
Muthu Kumar Duraisamy
Co-founder · 14 years in AIOps
Proactive problem management and secured operations in regulated industries.
Abhilasha Mathur
Co-founder, CTO · 10 years in AIOps
Leads engineering and the SteadyNines platform.
How SteadyNines started
We built qRaptor, an AI architect for enterprise systems. Enterprise customers using it asked us for a product that keeps their SLOs. SteadyNines is that product, built with them.
Pick one SLO you are missing. See the baseline within a day.
Connect the tools, declare the SLO, and SteadyNines shows where the loop breaks, how long each handoff takes, and which situations repeat. Agents begin in propose mode. Autonomy is yours to grant.
Talk to us