Continuous Pattern Detection
Unusual risk, reliability, dependency, and ownership patterns surface automatically as they emerge across your engineering landscape.
Spot what matters before it becomes an expensive surprise.
Your teams are flooded with data but still miss cross-system patterns. DevGrid surfaces risk, delivery, and dependency insights from one live graph so teams act earlier, not after impact.
Unified
insight view across engineering signals
Actionable
cross-system risk signals
Current
insights from dynamic system state
Too many tools. Not enough insight.
Engineering data is spread across scanners, delivery tools, runtime systems, and ownership records. Each team sees a partial signal, and meaningful patterns get lost in dashboard noise. Without one live insight graph, issues surface late and remediation starts after impact.
Here's how DevGrid fixes that
Prioritized Recommendations
Surface recommended actions ranked by likely impact so teams focus on the highest-value remediation first.
Root-Cause Context
Every insight comes with linked service, dependency, ownership, and change context so teams understand why, not just what.
Natural Language Insight Queries
Ask plain-language questions about risk, dependencies, and delivery trends and get answers grounded in current system context.
Four roles. One insight truth.
Each role works from the same live insight graph and gets the exact context needed to make faster, safer decisions.
Technology Executive
Sees: Portfolio-level risk and delivery patterns, trend shifts, and high-impact intervention opportunities.
Decides: Where to focus leadership attention and funding for the biggest strategic outcome.
Technology Manager
Sees: Cross-team bottlenecks, recurring failure patterns, and services trending toward elevated risk.
Decides: Which teams and initiatives need intervention before issues escalate.
Risk Manager
Sees: Emerging control drift, vulnerability concentration, and services with compounding risk signals.
Decides: Where to prioritize escalation and preventive controls to reduce exposure.
Engineer
Sees: Service-level anomalies, likely root causes, and dependency impact in workflow context.
Decides: What to fix first for the greatest reliability and risk reduction impact.