Our positioning
AI made coding faster — but teams still lose nearly a full workday per engineer to coordination: planning handoffs, status chasing, risk fire drills, and retros that arrive too late. SignalsAI uses AI to automate planning, execution, and the feedback loop so coordination shrinks while shipping accelerates.
Same work. Three eras of how long delivery actually takes — when coordination runs on humans vs. when agents run the loop.
Shorter bar = less coordination time · same scope shipped faster
AI made coding faster. Delivery is still the bottleneck.
SignalsAI is the multiplier on delivery — turning AI speed into shipped work, not coordination debt.
Built for humans and agents across planning, execution, and delivery — from systems of record to agentic orchestration.
Plugs into fragmented systems where work already happens — no rip-and-replace.
Real-time visibility across engineering execution.
Where competitors stop at dashboards, SignalsAI builds connective memory.
Teams, repos, tickets, meetings, and AI usage linked into one graph — so every agent and workflow decision has full delivery context.
Delivery context graph — one connected memory across Jira, GitHub, Slack, and AI activity so agents act with full situational awareness.
Becomes the orchestration layer — agents detect risk, coordinate, and follow through.
From systems → to visibility → to context → to agentic intelligence
SignalsAI runs the entire delivery loop — planning, execution, feedback — so your team never has to push the process forward.
Planning
Capacity planning
Load-balanced sprints from real velocity and availability.
Intelligent task routing
Right engineer, right skill — dependencies included.
Sprint commitment guardrails
Overcommit and blocker risks flagged before kickoff.
Execution
Runs the sprint
Progress tracked automatically from real activity.
Catches risks early
Blocks and deadline conflicts surface days early.
Auto-resolves & escalates
Right person, full context — no status pings.
Feedback Loop
Reports write themselves
Summaries from committed vs. delivered data.
PR-level AI attribution
AI %, human edits, cycle time — per pull request.
Patterns feed next sprint
Insights land before the retro meeting starts.
Every capability maps to planning, execution, or the feedback loop — the same story, end to end.
Sprint load balanced against availability, WIP, and historical throughput — not gut feel.
Tasks assigned by skill, ownership, and dependency graph across teams and repos.
Overcommit, hidden dependencies, and blocker chains flagged before kickoff.
Jira, Linear, and Notion work unified — no duplicate tickets or stale scope.
Intelligent routing
AI usage correlated to cycle time & quality
2.4d
Avg cycle (high AI)
11%
Change fail rate
0.9d
Avg cycle (low AI)
| PR | AI% | Human edit | Lines | Cycle | Fail% | Signal |
|---|---|---|---|---|---|---|
PR-1842OAuth token refresh middleware | 91% | 12% | 1240 | 4.2d | 18% | Large AI PR |
PR-1839Rate limiting for public API | 44% | 68% | 186 | 1.1d | 4% | |
PR-1831Session expiry interceptor | 72% | 41% | 412 | 2.8d | 11% | High AI, rising fail rate |
PR-1827PKCE flow edge case fix | 22% | 94% | 64 | 0.9d | 2% |
Insight: PRs with 800+ lines and 85%+ AI correlate to 2.1× longer review and 3× higher fail rate this sprint.
Every pull request: AI %, human edits on AI code, lines changed — not sprint-level guesses.
See how AI-heavy PRs map to lead time, change fail rate, and review lag in real time.
Flags outsized AI-generated changes that spike review time or defect risk for the team.
Deadline conflicts, blocked chains, and velocity drops caught days before review.
Measures what the sprint promised against what shipped — every sprint, no manual rollup.
Retros drafted from delivery data before anyone asks — patterns ready for the meeting.
Connect AI adoption to throughput, quality, and team health — not vanity percentages.
Insights from this sprint inform capacity and routing in the next — the loop closes.
Committed vs delivered
8 tasks committed · 8 / 8 shipped ✓
Large AI PRs slowed Auth team
PR-1842 · +2.1d avg review · feeds Sprint 25 capacity
Human-heavy PRs shipped fastest
0.9d avg cycle · 2% fail rate
Routing rule updated for OAuth work
Pattern applied to Sprint 25 planning
Sprint retro → Auto-drafted · loop closed ✓
Software Delivery is no longer about managing cards on a board. It's about orchestrating intelligence.
8+
hours saved per engineer per week
Not from working faster — from eliminating coordination work that shouldn't require an engineer at all.
73%
of PM work that doesn't need a human
Task creation, status tracking, sprint reporting — automated from real data.
2.4d
average risk detection lead time
That's your window to fix issues before the sprint review surfaces them.
No ripping and replacing. SignalsAI connects to your existing tools in minutes.
Certified information security management system ensuring comprehensive data protection
Independently verified controls for security, availability, and confidentiality