Catch the risky change before it reaches production.
You own the pager, and AI agents now ship changes faster than anyone can review them. NOFire scores every change against a live model of production, maps its blast radius while it is still a pull request, and holds back what would take services down.
Mission control
Recent changes and deploys, ranked by risk. The riskiest first.
Velocity went up. Understanding didn't.
AI copilots and agents are opening more pull requests, touching more services, and shipping more changes than any review process was built to catch. The tooling that catches problems still waits for symptoms in production, long after the change that caused them shipped.
Our take: The Search for PreventionReactive by default
Alerts fire once error rate or latency climbs, which means the damage is already live, the error budget is already spent, and someone is already awake at 3am.
Blast radius is invisible
A config bump or dependency change looks harmless in a PR. What it actually puts at risk downstream stays hidden until it ships, and more of those PRs are now written by agents that never saw the last outage.
The same failures recur
Past incidents live in postmortems no one reads at deploy time, so the same class of change breaks production again and again, faster than the team can learn from it.
The moment it ships, the risk is flagged.
NOFire scores the change against your live production graph and surfaces the only live path to a business-critical service, while the change is still reviewable.
Ranked against the graph
Every change and deploy is scored the moment it lands and ordered riskiest-first in Mission Control.
Blast radius, not a guess
The graph already knows which services sit downstream, which carry live traffic, and which are business-critical.
The only path to checkout
Here the changed paymentgateway is the single live route to a business-critical checkout, so the finding is held for review.
Specialists explain why, with citations.
Instead of a black-box score, a standing council of specialist reasoners argues the change and shows its work: the checks it could not rule out, each backed by a citation.
#2417 · paymentgateway: cut checkout timeout 30s → 5s
NOFire tries to rule the risk out. These are the checks it could not clear.
Reaches a business-critical service checkoutservice sits directly downstream of this change.
On the live traffic path Real customer requests flow through this path right now.
No fallback paymentgateway is the only route to checkout.
Not happened before No similar incident has hit this service.
It tries to rule the risk out
Every finding starts as refutation. NOFire tries to prove the change is safe and surfaces only the checks it cannot clear, each an explicit verdict rather than a number.
paymentgateway: cut checkout timeout 30s → 5s
The new 5s timeout is not covered by the compatibility suite. Older clients on the checkout path still expect the 30s window.
A shorter timeout changes retry behavior under load. Live traffic to checkoutservice could see failed charges during spikes.
A council, not a score
Instead of one opaque number, a panel of specialist reasoners runs what-if scenarios against your live traffic and past incidents, and each raises concerns it can cite.
paymentgateway: cut checkout timeout 30s → 5s
Escalated to commerce-platform
Flagged at deploy, verified within minutes, with a revert of #2417 already prepared for approval.
Escalate with a revert ready
The finding routes to the owning team with a revert already prepared for approval.
Reactive tools wait for the outage. NOFire catches the change before it lands.
- Alerts fire only once error rate or latency climbs
- The damage is already live and the error budget is spent
- Blast radius is discovered during the incident, not before
- Every change is scored while it is still a pull request
- Blast radius to business-critical services is mapped before deploy
- The risky change is held back before it reaches production
Every finding is scored against the live model of your services, dependencies, ownership, and change history. No graph, no prevention.
Ahead of the incident, not chasing it.
Ahead of the incident
Risk is surfaced before a change lands, so problems get caught before they page anyone or burn the error budget.
Blast radius before you ship
See the downstream impact of a change while it is still reviewable, not after production degrades.
Keeps up with agent velocity
Every change gets the same check automatically, whether a human or an AI agent opened the PR, at the pace they now ship.
Risky changes held back
The change that would have taken services down is flagged or gated before it ever reaches production.
Fewer repeat outages
Each past failure informs every future deploy, so the same class of incident stops recurring.
Evidence you can verify
Every flag is backed by production data you can interrogate, not a black-box risk score.
AI writes the code. NOFire keeps it running.
A 30-minute call with a founder. We map your stack to the Context & Control Model, live.