Observe
Read the work in context, not in isolation.
Combine issue detail with roadmap themes, customer signals, code ownership, and known risk policies before anyone starts building.
Control tower for product development
Assess the work. Route the change. Learn from the result.
The control tower for shipping the right product work with AI.
DeliveryTower turns backlog items into delivery-ready work packets. It reads customer and strategy context, checks verification strength and change risk, routes each item into the right execution lane, and gets sharper from delivery history.
Routing lanes
4
AI-first, hybrid, discovery, and human-led delivery.
Learned signals
12+
History from PRs, review rounds, regressions, and follow-on work.
Core promise
Less guesswork
More disciplined execution before the coding agent starts.
Tower board
Delivery readiness in motion
Live model
Strategy fitVerificationBlast radius
Similarity index
84%
Historical matches found across adjacent changes and review threads.
Verification strength
Strong
Existing tests, clear acceptance criteria, and visible rollback path.
AUTH-184
Enterprise SSO provisioning
Needs discovery
Security-sensitive area. Verification gaps still open.
WEB-216
Pricing page experiment
AI with guardrails
Clear scope, shallow blast radius, manual review required.
API-092
Audit export retention
Human review first
Cross-cutting data policy and compliance implications.
APP-331
Workspace invite reminders
AI ready
High historical similarity and strong acceptance criteria.
Flight log signals
PR review churnLow
Follow-on workModerate
Regression riskWatched
Inputs watched
Issues, PRs, incidents, customer demand
Primary output
A lane recommendation with reasons
Secondary output
An AI-ready brief that respects context
Operating model
DeliveryTower is not another issue board. It is a decision surface for product work.
The system exists to answer what should happen next: whether a change is aligned, understood, verifiable, safe for AI, and likely to stay safe after it ships.
Route
Send each change into the right execution lane.
DeliveryTower recommends AI-first, hybrid, discovery, or human-led execution based on clarity, verification strength, and blast radius.
Learn
Turn delivery history into judgment.
Review friction, PR cycle time, regressions, and follow-on work become signals that sharpen future recommendations.
Signals watched
The tower learns from the work, not just the prompt.
DeliveryTower becomes credible when it can point to evidence: similar changes, review churn, regressions, test strength, and the cost of hidden work after merge.
Issue clarity and acceptance criteria
Roadmap themes and customer demand
Historical PR friction and review rounds
Files touched, cycle time, and rollback risk
Verification paths, test gaps, and observability
Follow-on work across docs, support, and rollout
Step 01
Assess readiness
Generate a structured brief, missing questions, and confidence-backed signals for every new item entering the backlog.
Step 02
Score the real constraints
Measure strategic fit, customer value, verification strength, change risk, and likely hidden work instead of relying on a single opaque score.
Step 03
Clear the right lane
Create AI-ready work packets for bounded changes, force discovery for ambiguous work, and escalate risky items to human review by design.
Step 04
Learn after merge
Track what really happened in PRs and releases so the system gets stricter, faster, and more useful over time.
Product frame
Built for teams that want AI leverage without giving up product judgment.
DeliveryTower sits above the coding agent. It helps teams decide what AI should build, what humans should shape first, and what the organization should learn from each delivery cycle.
Why it matters
Because abundant coding capacity does not remove the need for strategy, verification, and change discipline.
Because teams need a system that learns from what really caused churn, regressions, and surprise follow-on work.
Because better AI delivery starts with better work selection, not just faster implementation.