Expert-supervised AI execution
AI accelerates every change while AI Lifecycle Engineers supervise each gate – speed without losing control.
AI Lifecycle Engineer
Supervising · gate 3
AI-MSLAI-Managed Software Lifecycle
Moveexisting production systems
to end-to-end AI-powered delivery
The quickest, seamless path from legacy dev teams and sprint cycles to next-day time-to-market – without risky, long R&D programs. Next-day changes, 30–70% lower cost of change, predictable and auditable outcomes, and no dependency on retained dev teams.
Trusted by product and enterprise teams
Your roadmap is growing faster than your delivery capacity?
Roadmaps expand. Backlogs persist. Hiring takes months, and every additional vendor adds handoffs, meetings, and management cost. The result is delayed customer commitments, rising delivery expense, and less confidence in what will ship next.
Roadmap commitments move while delivery capacity stays fixed.
Elastic, pay-per-change delivery that scales with your roadmap.
Backlog work competes with revenue and customer priorities.
Clears the backlog in parallel, so revenue work keeps moving.
Hiring and onboarding delay time to value.
No hiring cycle – starts from your existing systems on day one.
Multiple vendors create more coordination and less visibility.
One governed lifecycle – full visibility, fewer handoffs.
Leaders are expected to deliver more with tighter cost control.
30–70% lower cost of change, delivered predictably.
AI-MSL is Built to Fix That
One governed, AI-powered lifecycle for the software you already run.
Deep understanding of your existing systems.
CloudGeometry maps your codebase into a structured context layer we call AppGraph – every dependency and architectural decision – so change starts from real system knowledge, not guesswork. AppGraph stays current as your software evolves, and it’s applied directly to the systems you already run.
System
AppGraph
Every repository, branch, and commit feeds AppGraph's understanding of how the system actually works today.
Existing diagrams are parsed and reconciled against the running code, surfacing drift between intent and reality.
Service contracts and data shapes are continuously indexed so changes can be planned with full impact awareness.
Terraform, Kubernetes, and cloud configs become first-class context for every change AppGraph reasons about.
Operational runbooks and process docs are encoded as governed workflows, not buried in wikis.
Tickets, Slack threads, and undocumented decisions are captured and made queryable – institutional memory becomes durable.
AI-MSL
Governed lifecycle · CR-1042
AI executing
Lifecycle gates · experts supervise each
Requirements
Specifications
Architecture & security
Implementation · test-first
Running
Release
Queued
AI Lifecycle Engineer
Supervising Gate 4 · Implementation
Approved with note: prefer async queue.
intent → spec → code → release
Governed delivery, not just code generation
Every change moves through a structured, AI-powered lifecycle – from requirements to production and continuous evolution. AI accelerates execution while experts supervise every gate, so each change is test-driven, traceable from intent to production, and delivered as production-ready code.
Consistent quality by design.
Quality is built into the model, not bolted on. Every change follows test-first execution with enforced standards and expert review gates, giving you full traceability from requirement to code to release – predictable, auditable outcomes rather than variable, single-pass output.
5–10×
Faster time-to-market
30–70%
Lower cost of change
AI-MSL
Quality report · CR-1042
Every change, verified
Tests
128
test-first · all passing
Coverage
92%
enforced standards met
Review gates
5/5
expert-approved
Traceability
Full
intent → code → release
40%+38%+42%+
Compute cost reduction
60%+
DevOps & SRE efficiency
Expert-led. AI-executed. Fully transparent.
Learn more
Pay per change, not per headcount
No idle teams or hiring cycles – you fund outcomes, not standing capacity.
This change · 120 DC
Estimated before build · 80–200 DC
Continuous evolution – no big rewrites
Improvements ship incrementally as structured changes, so your systems stay always current.
Predictable, auditable delivery
Every change is traceable from requirement to code to release, with artifacts visible in real time.
CR-1042 · fully traced. Audit-ready.
Proven in real delivery environments.
CloudGeometry combines years of experience in cloud, platform engineering, and product development with a new AI-powered execution model – enterprise-grade architectures, deep Kubernetes and platform engineering expertise, and production systems maintained and evolved continuously.
Traceable changes and audit-ready artifacts keep security and compliance aligned throughout the lifecycle.
Talk to an expertHow it works?
01
Understand your existing system
We connect to your repositories and map dependencies, packages, and architecture into AppGraph – a structured context layer that stays a complete, current representation of the system you already run.
02
Formalize and validate requirements
Requirements are formalized and validated before build, and specifications are generated and reviewed systematically – so work starts from a clear, agreed target state.
03
Define the change in natural language
Submit and refine requirements through a workspace – a new feature, a refactor, a modernization, or an operations improvement – in plain language.
04
Review and approve the plan
You see artifacts, decisions, and progress in real time, and can expand, adjust, and validate before giving the go-ahead. Expert gates ensure architecture, security, and quality.
05
AI executes; experts supervise every gate
Development follows test-driven, structured execution with quality gates. AI accelerates execution while experts supervise each step, keeping quality consistent and rework low.
06
Ship and continuously evolve
Changes are delivered as production-ready branches, fully traceable from intent to production. From there, improvements continue incrementally – no big rewrites.
Faster roadmaps. Lower delivery overhead. More predictability.
Increase software-delivery capacity without adding another hiring cycle, vendor layer, or management structure.
HIPAA-Regulated Medical Imaging
Nanox
NASDAQ: NNOX · 5-year engagement
From two-to-four-week sprint cycles down to two-to-three-day delivery, on a HIPAA-compliant AWS production system. Engineering team scaled from twelve to three on the same workload; audit passed without findings post-transition.
10×
Feature cadence
3
Team, same workload
0
Audit findings
The system stopped decaying between releases – every fix leaves it cleaner than we found it.
Advertising Technology
Digital Remedy
Concurrent multi-product engagement
AI-MSL ran end-to-end across three interconnected advertising-technology codebases simultaneously, with cross-product dependencies surfaced during evaluation rather than discovered in production. Sprint-level tasks delivered 5× faster, at roughly 10% of the cost of an in-house team using AI tools.
3
Products, one pipeline
5×
Sprint-level tasks
~10%
Of in-house cost
Three codebases, one governed pipeline – maintenance stopped competing with our roadmap.
Frequently asked questions
What is AI-MSL?
AI-MSL (AI-Managed Software Lifecycle) is CloudGeometry's end-to-end, AI-powered execution model for software – from requirements to production and continuous evolution – applied directly to your existing systems. AI accelerates execution while experts supervise every gate, so changes are delivered faster, at lower cost, with predictable and auditable outcomes.
Who is it for?
Product and enterprise teams that need to maintain, modernize, and extend existing production systems – without standing up large retained development teams or committing to risky, long R&D programs.
How is this different from hiring a dev team or agency?
You pay per change, not per headcount. There's no idle capacity and no hiring cycle: system knowledge lives in a structured context layer, and work is executed as governed, traceable changes with expert oversight.
Do you rewrite our software?
No. Modernization and improvements happen incrementally, delivered as structured changes. That reduces the risk and cost of big-bang rewrites while keeping your systems always current.
How do you ensure quality and security?
Every change follows test-first, structured execution with enforced standards and expert review gates. Changes are traceable from requirement to code to release, producing audit-ready artifacts and continuous compliance alignment.
What can AI-MSL be used for?
New feature development, corrective and adaptive maintenance, AI-powered production operations, application modernization, cloud-native and Kubernetes adoption, managed data engineering, M&A tech assessment, and security & compliance readiness.
How fast is it, and how much does it cost?
Many changes reach production in hours or days instead of weeks – typically 5–10× faster time-to-market – with 30–70% lower total cost of change. Pricing follows a pay-per-change model that aligns spend with outcomes.
How do we work with CloudGeometry day to day?
You interact through a workspace to submit and refine requirements. Each change moves through clear lifecycle stages, and you see artifacts, decisions, and progress in real time. Requirements can be expanded, adjusted, and validated continuously.
Does it work with our existing codebase?
Yes. AI-MSL is designed for existing production systems. We map your codebase into a structured context layer first, so changes align with your current architecture, patterns, and standards.
How do we get started?
Start with a system assessment: we map your existing system, identify high-impact changes, and estimate cost and timeline. From there, changes flow through the AI-powered lifecycle. Estimate your cost or talk to an expert to begin.
Talk to an expert
See where AI-MSL could improve your delivery economics.
Bring a roadmap, backlog, vendor, cost, or delivery-capacity challenge. In a 30-minute assessment, we'll identify the main constraint, evaluate fit, and recommend a practical first use case.
Move more roadmap and backlog forward Less coordination across teams and vendors Clear visibility into progress and ownership Predictable spend-to-delivered-work You keep control of priorities and final approval
Guided by CloudGeometry's AI Lifecycle experts.