Cloudgeometry

• 2014: Founded as a managed engineering firm supporting cloud-era businesses with development and cloud operations.
• 2017: Expanded AWS and Azure partnerships and Kubernetes expertise.
• 2020: Expanded into unified data architectures and Spark-based solutions as a Databricks partner, including collaboration with AWS R&D.
• 2022: Focused on MLOps and AI adoption, including production data pipelines and AI model deployments.
• 2024: Launched an open-source agent platform and expanded into AI transformation and agentic systems.
• 2026: Advanced toward a fully integrated, expert-supervised AI-powered software development lifecycle.

CloudGeometry states that its vision is for software systems to become self-healing and self-improving, while combining AI execution with expert supervision to provide quality, governance, and reliability during the transition.

Recognition and expertise

CloudGeometry identifies itself as an AWS Advanced Consulting Partner and CNCF Kubernetes Certified Service Provider. Its engineering focus includes cloud, Kubernetes, data, AI, DevOps, and AI governance.

Leadership

The management team includes Michael Philip, CEO and Founder; Eduardo Dominguez, Chief Revenue Officer; Alex Ulyanov, CTO; Shantel Bince, Head of PeopleOps; Kevin Dunn, Head of Channels and Alliances; Nick Chase, Chief AI Officer; Robert Wolfe, Technical Advisor and AI Governance Lead; Antoine Dubuc, Senior AI Technical Product Manager; Pascal Sauve, VP Technology and Delivery; and Carter Holmes, Lead TPM and AI Lifecycle Manager.

AI-MSL continuously monitors software and its surrounding technology ecosystem to identify risks and required changes before they become production problems. Covered areas include:

• Vulnerability remediation and security hardening
• Compliance controls
• Dependency, framework, runtime, and API updates
• Bug remediation and technical debt reduction
• Test coverage and documentation improvements
• Browser, device, operating system, and platform compatibility

Clients prioritize changes while AI-MSL handles scope definition, implementation, testing, and validation. AI Lifecycle Engineers review the work before deployment.

New features

Product Managers and Customer Success teams can describe enhancements in plain English. AI-MSL refines the business intent, explores edge cases, creates delivery-ready requirements and specifications, implements the change, produces tests and documentation, and prepares a production-ready branch or merge request.

Application modernization

CloudGeometry modernizes existing systems without requiring a disruptive rewrite. Options include:

• AI readiness for AI-powered workflows and intelligent automation
• Cloud modernization and Kubernetes adoption
• Legacy platform renewal
• Technical debt reduction
• Security and compliance improvements
• Architecture and integration modernization

AppGraph maps architecture and dependencies to support impact analysis and modernization planning.

AI transformation

AppGraph provides a semantic model of architecture, APIs, workflows, and dependencies. AI-MSL uses this system intelligence to support agent development, impact analysis, and governed AI deployment. CloudGeometry describes its approach as including human approval gates, structured requirements, end-to-end traceability, and security by design.

Managed production operations

Managed Production Ops provides AI-powered root-cause analysis, dependency impact analysis, observability, risk detection, cloud cost optimization, performance analysis, capacity planning, and reliability recommendations. Deployments, infrastructure changes, and operational automations remain under human supervision, with final validation by CloudGeometry DevOps and SRE experts.

Technology offerings

CloudGeometry works with existing enterprise stacks, hyperscalers, open-source technologies, and open protocols. Its offerings include LangBuilder Open Source and LangSmith Enterprise support, CGDevX for Kubernetes-native application delivery, ControlPlane for multi-cloud Kubernetes operations, and data platform capabilities involving Databricks, ClickHouse, and Claritype.

An initial System Intelligence Assessment analyzes source code, documentation, architecture, infrastructure, dependencies, and engineering knowledge. It produces:

• An AppGraph and engineering knowledge base
• Quality, complexity, and maintainability evaluation
• A Continuous Software Readiness scope
• Modernization opportunities and priorities
• A baseline for estimating future changes

The monthly Continuous Software Readiness package covers an agreed scope of corrective, adaptive, and security maintenance. It includes expert AI Lifecycle Engineer supervision and a predefined allocation of DevCredits. These credits are replenished monthly, cover routine maintenance within the package, and expire at the end of each billing period.

New features and major changes

Each approved feature or major change receives an independent estimate based on implementation complexity, system impact, and scope. The estimate includes requirements refinement, impact analysis, specifications, implementation, testing, documentation, review, and production-ready delivery.

DevCredits are the standard accounting unit for software delivery. They cover AI execution, infrastructure, expert supervision, testing, documentation, and governance. Credits for approved new changes may be purchased individually or in discounted packages, are reserved for approved changes, and do not expire.

Annual development capacity commitments are listed as:

• 200 DevCredits: 5% savings
• 500 DevCredits: 10% savings
• 1,000 DevCredits: 20% savings

The site also lists a 30% discount for new MSL clients during their first month.

AI-MSL models

• AI-MSL Core: Product management and development, from product definition to production-ready code.
• Operate add-on: AI-powered production operations added to PM and development.
• Enterprise: Deeper integration with internal engineering processes and governance.

Enterprise deployments can run in a customer's own VPC or environment when configured for the customer's security and compliance requirements.

The website provides options to contact sales, schedule a live demo, start a system assessment, explore AI-MSL, and request a security package or speak with a security lead.

AI-MSL operates on customer systems with controlled and auditable access. According to the website:

• Customer code remains in customer repositories.
• Data is not used to train external models.
• Outputs are stored in the customer's environment.
• Customers retain ownership of their code, repositories, and generated assets.
• There is no lock-in to proprietary formats or hidden dependencies.

The Trust Center lists role-based access control, least-privilege practices, audit trails for AI agents, monitoring, and observability as safeguards.

Governed delivery

AI-MSL uses structured requirements, system-wide AppGraph context, automated testing and validation, quality gates, and human approval gates. AI Lifecycle Engineers supervise critical stages and intervene when quality, correctness, or confidence signals are low.

AI-MSL delivery includes traceability from intent through requirements, specifications, code, tests, documentation, deployment preparation, and production operations. Infrastructure changes and operational automations are also subject to human supervision.

Compliance and enterprise controls

CloudGeometry describes its delivery environment as SOC2-oriented and suitable for HIPAA-sensitive support. The website also states that its production operations platform runs on a SOC2-, HIPAA-, and FedRAMP-certified Kubernetes platform. Security practices are aligned with common enterprise compliance frameworks, and the company offers HIPAA-ready handling.

AI-MSL's stated security capabilities include OWASP-aligned automated controls, integrated quality and deployment gates, release validation, readiness checks, and more than 20 security controls per change.