You find out your
blast radius in the post-mortem.
A PR ships. Something breaks. Three services you didn't know depended on this one start erroring. The post-mortem question, “did anyone check the dependency graph?”, has no good answer, because there was no tool that did it automatically before the deploy.
The situation
"The PR looked fine. Two approvals. Staging passed. It shipped Thursday at noon. By 12:40, three teams were paging."
The change was 200 lines. It touched a shared auth utility that eight services called. Nobody knew, not because they were careless, but because that dependency information lives in a graph that nobody maintains, nobody queries before merging, and nobody thinks about until it's the subject of a post-mortem action item that also doesn't get done.
The real problem
Deploy risk is a function of four things: what changed, what depends on it, how healthy your error budget is, and when you're shipping. Most teams have none of these systematically available at merge time. They rely on reviewer familiarity, which is tribal knowledge that doesn't scale. The senior engineer who knows every dependency is one vacation away from being the single point of failure in your release process.
And even when teams do check (manually pulling up Datadog before a deploy, eyeing the error budget, pinging the downstream service owners), none of that is connected to a deploy gate. A high-risk PR can still ship on a depleted error budget on a Friday afternoon, because nothing blocks it.
The fix
Risk intelligence needs to be automatic, pre-merge, and connected to a deploy gate. Not a dashboard someone checks. A system that reads every PR, walks your live dependency graph, checks your error budget balance, and surfaces a single risk score with a recommendation, as a PR comment, before anyone approves. High-risk changes get a soft block or a require-SRE-sign-off. The information that used to live in one senior engineer's head becomes systematic, enforced, and auditable.
Risk model
Six factors. One score.
Titan Foresight weighs six signals against your specific service's history and dependency graph to produce a calibrated risk score.
Guardrails
Automated enforcement, not just warnings.
Configure what happens at each risk threshold. From advisory notifications to hard blocks: you define the policy, Titan Foresight enforces it.
GitHub integration
Risk score on every PR. No workflow change required.
The status quo
How teams assess deploy risk today, and why it fails.
Most teams have no systematic risk model. They rely on gut feel, reviewer experience, and "it worked in staging."
How we compare
Foresight vs. gut feel and DORA dashboards.
| Capability | DeployTitan | Manual review | DORA metrics tools |
|---|---|---|---|
| Risk score per PR (automated) | ✓ | ✗ (gut feel) | ✗ |
| Blast radius mapping | ✓ | ✗ | ✗ |
| SLO error budget awareness | ✓ | ✗ (manual check) | ~ (reporting only) |
| Automated deploy guardrails | ✓ | ✗ | ✗ |
| Analysis time per PR | < 2s | 5–15 min | N/A |
See a risk score on one of your real PRs.
Connect your GitHub repo, pick a recent PR, and we'll show you the blast radius map, error budget check, and risk score, live in 20 minutes.