Keller AI — Archangel
AI has accelerated implementation. The hard part now is turning an ambiguous initiative into decisions, architecture, and work that engineering can execute with the right review behind it.
The upstream bottleneck
Regulated teams can generate code and artifacts quickly — but deciding what to build, showing why it aligns, and carrying coordinated judgment into the build still arrives through fragmented review. The constraint sits before code: requirements, architecture, standards, and approvals.
Governed workflow
Bring business intent and the review bottleneck into one working surface.
Connect repository, documentation, and cloud context, then use AI-driven stakeholder interviews to surface gaps.
Connect observations, constraints, options, rationale, and downstream architecture.
Route judgment to the right reviewer and keep authority explicit.
Produce linked compliance evidence, architecture, delivery planning, and sprint-ready work.
Decision trace
Every architectural choice is captured as a structured, navigable record — observations, constraints, options, rationale — connected to its sources and downstream artifacts. The reasoning can be audited, replayed, and defended.
Reviewable decision
Inside Archangel
Implementation control
Drift detection monitors the build against approved architecture, flags deviations before shipment, and pinpoints the breached standard or decision — so what ships is what was approved.
Runs where you run
Archangel runs in the customer VPC or infrastructure. Bring your own keys and connect approved models through a customer-controlled endpoint.
Customer environment
Archangel in your VPC or infrastructureLive demo
Bring an initiative and a review bottleneck to a live demo — and watch Archangel build the governed path around it: decisions, architecture, evidence, and sprint-ready work.
