What does it take to go from “AI-curious” to shipping AI-driven features?

It starts with your data... our Managed Data Engineering Services provide the foundation to operationalize at scale.

Where does Kubernetes fit into AI transformation?

Kubernetes provides dynamic, scalable infrastructure for training, inference, and edge serving.

What AI use cases have you delivered?

Generative AI for whistleblowing, industrial IoT with predictive AI, and healthcare analytics pipelines.

Are AI Agents just a trend, or should we build for them?

Agents represent an architectural shift; we design for autonomy, resilience, and scalability.

How can I keep AI costs under control?

Right-sized models, full-stack observability, and FinOps guardrails keep spend predictable.

How does CloudGeometry approach AI security?

Security is architectural: role-aware agents, secure MLOps, and policy-based enforcement.

Do I need to rebuild everything to make AI work?

Not necessarily. We layer AI into existing platforms using APIs, feature stores, and orchestration.

How do I know if my business is ready for AI transformation?

If you’ve modernized data, ship APIs, or invest in automation, you’re close — our roadmap shows where to start.

AI Transformation Services for Scalable Growth

AI TRANSFORMATION

The AI Advantage Won’t Wait

Your competitors aren’t pausing for pilot programs. They’re scaling AI from experimentation to execution — integrating LLMs, automating decisions, and re-architecting around data intelligence.

The gap between AI potential and production performance is widening. Bridging it demands both engineering depth and organizational readiness. That’s where CloudGeometry’s AI Transformation Services come in.

Common Challenges We Solve

Legacy data infrastructure

Stacks not built for AI/LLM workloads; poor scalability for training & inference.

Fragmented pipelines

Missing observability; brittle jobs; schema drift undermines reliability.

Runaway compute costs

Model degradation and inefficient scaling drive unpredictable spend.

Enterprise AI skills gap

Limited hands-on expertise across MLOps, evaluation, and security.

ROI uncertainty

Executives need credible, value-tracked roadmaps to invest with confidence.

Team silos

Gaps between data, development, and operations slow delivery.

Pilot-to-production stalls

Promising POCs that never operationalize at scale.

Manual workflows

Slow release cycles; limited feedback loops; hard-to-measure impact.

Vendor lock-in

Unsustainable cost structures; limited portability across clouds.

CloudGeometry AI Adoption Framework

1. AI Readiness & Education

1h assessment +
two × 2h custom workshops

Explore Key Features

The first step in AI adoption is understanding what’s possible — how today’s technologies can streamline workflows, reduce manual effort, and open new opportunities for your business.

Semi-automated 5-minute interviews capture your leadership’s current AI understanding

Custom workshops showcase business automation opportunities using the latest AI tools

Training sessions align executives and IT teams on vision and outcomes

We assess feasibility to ensure your goals are realistic and actionable

The result: You leave with clarity on AI’s potential and the confidence to move forward with a shared vision.

ai agents data solutions udemy cloudgeometry.min

Custom AI Crash Courses in Business & Tech — Trusted by 2K+ teams

Nick Chase — AI/ML Practice Director / Senior Director of Product Management

By Nick Chase

Chief AI, CloudGeometry;
Co-Chair

“Two hours with Nick made it clear — we should have started this journey long ago.”

Carolyn Maxwell

VP Products,
Enterprise FinTech Startup

“Helped us build MLOps platform to monitor models running on our IoT devices. OnTime, OnBudget, OnTarget.”

Tzahi Fridman

Director R&D, Cloud Architecture,
GE Digital

2. Collaborative Use Case Discovery

Weeks

Explore Key Features

The first step toward effective automation is taking a holistic view of your business processes — both internal and customer-facing — to uncover where AI can deliver the fastest and most sustainable impact.

Review workflows across ERP, CRM, and other core systems to surface automation opportunities

Analyze what industry leaders are automating and identify existing AI agents/technologies for reuse

Capture manual employee activities on web & desktop apps with special mapping tools like Celonis Task Mining, ABBYY Timeline and Apromore to generate structured workflows

Prioritize use cases with business and IT stakeholders based on feasibility, value, and ROI

The result: Quick automation wins and a clear level of effort to achieve full-featured automation across the organization.

Browse FAQs

What if my team is already AI-savvy — can we drive the process ourselves and just use your development experts?

This is great! We love to work with AI experts! you can lead the process and rely on us for development and maintenance expertise. However, we still recommend taking our 10-minute automated assessment to ensure alignment and a shared understanding of priorities.

What criteria do you use to prioritize which AI use cases to tackle first?

We evaluate each use case based on feasibility, value, ROI potential, and speed to impact. This ensures you get early automation wins while also building momentum for long-term transformation.

How do you uncover hidden opportunities for automation in our existing systems?

We use advanced process mapping tools like Celonis Task Mining, ABBYY Timeline, and Apromore to capture manual employee activities across ERP, CRM, and desktop apps. This structured view of workflows makes it clear where AI can eliminate inefficiencies.

How do you ensure our executives and IT leaders are aligned on AI priorities?

We run targeted workshops and short training sessions that bridge the gap between strategy and technology. By capturing leadership perspectives through structured interviews and mapping them to feasible automation opportunities, everyone leaves with a shared understanding of what AI can realistically deliver.

Can you help us benchmark against industry leaders in AI adoption?

Yes. We analyze automation patterns from your industry peers and highlight proven AI agents and technologies that can be adapted for your business. This gives you confidence that your roadmap is not just theoretical but validated against real-world successes.

What’s the biggest risk of skipping the readiness and discovery stages?

Skipping these stages often leads to misaligned priorities, underutilized technology, and wasted investment. Without assessing leadership alignment, data readiness, and real workflow bottlenecks, AI adoption risks becoming fragmented experiments instead of a coordinated business transformation.

3. Data & Cloud Technology Assessment

Days

Explore Key Features

The quality of your AI will only be as high as the quality of your data. Ensuring that your data and cloud infrastructure are ready is essential for reliable, scalable, and cost-effective AI adoption.

Conduct a Cloud Well-Architected Assessment with emphasis on AI adoption readiness

Evaluate the need and feasibility of unifying data sources into data lakes built on scalable technologies

Identify gaps in data quality, integration, governance, and compliance requirements

Review opportunities for Kubernetes adoption to optimize AI performance, scalability, and costs

The result: A clear remediation roadmap that strengthens data foundations, optimizes infrastructure, and prepares your organization for enterprise-scale AI adoption.

We evolved from Cloud and Data specialists into AI leaders, maintaining Advanced Consulting Partner status with the industry leaders like AWS, Azure, Databricks, and adopting emerging AI Powered technologies like Claritype and Control Plane.

4. Smart Technology Choices

Days

Explore Key Features

We stick to your existing technology stack and extend it with well-integrated Open Source and commercial solutions — tailored to your goals and budgets, avoiding lock-in, and enabling faster GTM.

Cloud Infrastructure: AWS (EKS), Azure (AKS), or on-prem with OSS options — enhanced with our CGDevX platform or next-gen K8s hosting via ControlPlane with 50%+ cloud cost savings

Data Unification: Consolidate sources into scalable Data Lakes on AWS, Azure, Apache stack, or Databricks — accelerated by Claritype AI-powered schema tools to achieve a golden schema in days, not months

AI Agent Platform: Built on popular OSS frameworks like LangFlow, LangChain, and LangSmith — integrated into our LangBuilder platform for faster prototyping and seamless transition to production with enterprise-ready levels of security, monitoring, and quality controls

Model Selection: Choose from cloud-hosted LLMs (OpenAI, Gemini, Bedrock, Grok) or self-hosted models like Mistral and Ollama for better performance, privacy, and cost control — complemented by industry-specific AI Agents available from both open-source and commercial libraries

Production Deployment with OSS Tooling or LangGraph Platform: Enterprise-grade deployment with MLOps pipelines, quality monitoring, observability, and continuous improvements

The result: A solid AI stack that supports continuous process automation. By reviewing technology choices, we gain the insights needed to evaluate cloud, data, and AI agent platform efforts, configure an AI-powered SDLC for rapid prototyping and next-day deployment, and define your business automation roadmap milestones with confidence.

Browse FAQs

How do you evaluate whether our current cloud and data infrastructure is AI-ready?

We run a Cloud Well-Architected Assessment with emphasis on AI workloads. This highlights gaps in data quality, governance, compliance, and scalability so you can prioritize upgrades before rolling out enterprise-scale AI.

What is the LangBuilder platform, and how much do you charge for it?

The LangBuilder Platform is our curated, enterprise-ready integration of popular open-source projects like LangFlow, LangChain, and LangGraph. We maintain it as open-source at the core, while providing full enterprise support for security, access controls, observability, CI/CD for agent lifecycle, and governance. We don’t charge for the open-source components themselves — our fees cover support, customization, and enterprise hardening so your teams can safely deploy AI agents at scale.

How do you ensure the chosen AI stack remains sustainable and future-proof?

We embed MLOps, observability, and continuous improvement practices from day one. Combined with our open-source–first approach, this ensures your stack can evolve with new frameworks, models, and tools — without costly re-engineering.

What if our data is fragmented across multiple systems and formats?

We assess the feasibility of unifying your data into scalable data lakes or hubs, applying schema alignment and quality checks. This ensures all data sources can be consistently leveraged for AI applications.

How do you avoid vendor lock-in while making technology choices?

We extend your existing stack with interoperable open-source and commercial tools instead of locking you into proprietary platforms. This flexibility ensures cost control and future-proofing while giving you the freedom to adopt emerging technologies.

5. AI Strategy & Roadmap Design

Days

Explore Key Features

Now that the executive team is educated, use cases are prioritized, and we have a clear view of cloud, data foundations, and enabling technologies, we can confidently build the AI strategy and lay down a short- and mid-term roadmap.

Build & Test MVP Agents: Deliver quick prototypes of prioritized use cases while parallel cloud/data upgrades are in progress — proving value fast without waiting for full infrastructure overhauls

Define Governance & Risk Guardrails: Establish policies for security, compliance, responsible AI use, and human-in-the-loop oversight — ensuring confidence at the board and regulator level

Set Business KPIs & ROI Models: Tie AI initiatives directly to financial and operational outcomes (e.g., cost reduction, cycle time, CSAT, revenue impact) with clear measurement criteria

Plan Gradual Rollout & Replacement: Launch AI agents step by step to augment workflows, then progressively replace manual and legacy processes with AI-powered business processes that deliver efficiency and scale

The result: An AI strategy and phased roadmap that balances quick validation (MVP agents) with governance, ROI visibility, and a structured path to replace outdated processes — giving executives confidence that AI adoption is both achievable and value-driven.

6. AI Agents & Application Development

Weeks

Explore Key Features

Building on the high-level requirements prepared in the previous stages, we begin AI agent & application development using the CloudGeometry AI-Powered SDLC process.

Rapid Prototyping with prompt templates and tool wrappers on our LangFlow-based visual editor, enabling fast agent design and validation

Iterative Development with configurable parameters, embedded guardrails & integrated monitoring hooks to ensure safety and control

Evaluation & Tuning using evaluation datasets, offline evals for accuracy, reliability, and efficiency, plus running UAT

Pre-Production Hardening by integrating with real data sources, adding authentication and authorization, enabling observability, setting fallback paths, conducting load testing, and ensuring compliance readiness

The result: Robust AI agents & applications that evolve from prototypes into enterprise-ready solutions — safely tested in staging environments and prepared for smooth production deployment.

7. Secure Deployment & Staff Enablement

Days

Explore Key Features

Once agents & applications are hardened, we move to secure deployment in your environment and enable your teams to operate and extend them with confidence.

Infrastructure & Model Deployment: Host private LLMs (e.g., Mistral, LLaMA, Falcon) in your own cloud or on-prem; provision GPU/TPU resources with autoscaling and quantization; integrate inference servers (vLLM, TGI, Ollama, Ray Serve) and orchestrators (LangGraph) with multi-agent routing and async queues for scale

Security & Compliance: Enforce data privacy with masking and redaction, apply RBAC for execution and debugging, log all agent runs for auditability, and manage secrets with Vault/KMS. Ensure compliance with GDPR, HIPAA, SOC2, and implement PII handling pipelines

Observability & Quality Control: Implement telemetry, tracing, evaluation harnesses, and guardrails with fallback paths to ensure accuracy, reliability, and safe operations

Staff Training & Empowerment: Provide hands-on training, documentation, and playbooks so teams can confidently manage, extend, and scale AI systems within governance boundaries

The result: Secure, compliant AI agents deployed in your infrastructure — supported by enabled teams and human-in-the-loop controls to ensure responsible adoption from day one.

Browse FAQs

Is the CloudGeometry AI-Powered SDLC proprietary, and would we own the source code of our agents?

No, it’s not proprietary. The CloudGeometry AI-Powered SDLC is a process we built by combining leading open-source and commercial low-code tools for each development stage. It enables rapid prototyping in hours and enterprise-grade features in days, without needing a large dev team.

All the source code produced belongs 100% to you, with complete documentation, human-readable standards, and the ability to extend it with manual programming or automated tools.

How do you handle security and compliance in deployment?

We enforce data privacy, RBAC, audit logging, and secrets management as part of deployment. Compliance frameworks such as GDPR, HIPAA, and SOC2 are built into our process, ensuring you can meet both internal governance and regulatory requirements.

Which cloud technologies do you use for deployment — AWS, Azure, or Kubernetes?

We prefer Kubernetes as the best platform for AI agent use cases, thanks to its scalability, orchestration, and cost efficiency. We can run Kubernetes-based deployments on AWS, Azure, on-premises, DIY clusters, or through managed K8s hosting with Control Plane. This gives you the flexibility to balance performance, compliance, and cost control while maintaining portability across environments.

How do you ensure the AI agents developed are enterprise-ready?

We move from prototypes to production using a hardened pipeline that includes data integration, authentication/authorization, compliance checks, observability, load testing, and fallback mechanisms. This ensures agents are reliable, secure, and compliant before deployment.

How do you empower our staff to operate and extend AI agents after deployment?

We provide hands-on training, documentation, and playbooks for your teams. By combining staff enablement with human-in-the-loop controls, your team gains the confidence to manage, extend, and scale AI systems safely within established governance boundaries.

8. Continuous Monitoring & Improvement

Explore Key Features

Once deployed, AI systems require ongoing monitoring and iteration to stay reliable, compliant, and aligned with business needs. We embed continuous improvement practices into your AI operations.

Monitor & Observe: Track performance, latency, token usage, and reliability with enterprise observability tools

Evaluate Regularly: Run automated and human-in-the-loop evaluations for accuracy, reliability, efficiency, and compliance

Collect Feedback: Capture structured user and operator feedback to refine prompts, agents, and workflows

Optimize & Evolve: Apply updates, retrain or fine-tune models, and expand automation scope to maximize ROI over time

The result: Continuous improvements that optimize business use cases and enable the adoption of new AI solutions and optimized model versions — keeping your AI systems competitive and future-ready.

Browse FAQs

What do your Managed Services cover beyond initial AI deployment?

Our Managed Services cover the full lifecycle — cloud infrastructure, data pipelines, and AI agent operations. We provide continuous quality monitoring, compliance oversight, and performance optimization so your systems remain reliable, secure, and cost-efficient.

How do you ensure AI agents remain accurate and reliable over time?

We embed observability & evaluation frameworks into production. This includes automated testing, human-in-the-loop validation, structured feedback loops, and guardrails with fallback paths. Together, these ensure your agents adapt while staying safe, accurate, and compliant.

Why should we use Managed Services instead of handling AI operations ourselves?

Running AI in production requires expertise across cloud, data, security, and AI engineering. Our team takes care of these layers end-to-end, freeing your IT teams from daily firefighting. Instead of managing infrastructure, your staff can focus on strategy and innovation, while we ensure your AI systems deliver maximum ROI.

Do you provide ongoing development and optimization of AI agents?

Yes. Through our Managed AI Development Services, we continuously refine your AI agents with new features, improved prompts, updated integrations, and retraining as needed. This ensures your AI solutions evolve with business needs rather than becoming static.

How do your services help us keep up with fast-moving AI innovation?

We actively track new frameworks, LLM releases, and best practices. Our Managed Services integrate these advancements into your stack where beneficial, so you stay ahead of competitors without constantly re-engineering your systems.

Why CloudGeometry

Modern application modernization demands deep engineering, cloud-native architecture, and AI-first delivery — all grounded in experience.

Full-Stack Modernization Expertise — Evolved Over a Decade

10+ years transforming aging systems, startup-quality apps, and post-M&A stacks...

We’ve spent over 10 years transforming aging systems, startup-quality applications, and post-M&A systems into resilient, enterprise-grade platforms. Whether it’s scaling early-stage code or reviving critical apps abandoned by former dev teams, we modernize what matters — from UI to infrastructure and beyond.

Cloud-Native & Multi-Cloud Architecture Mastery

Design and operate platforms across AWS, Azure, and hybrid environments...

We design application platforms that run securely and reliably across AWS, Azure, and hybrid environments — with Kubernetes, containerization, and zero vendor lock-in baked in.

AI-Driven Acceleration with an AI-Powered SDLC

Automation from code transcription to testing, CI/CD, and delivery analytics...

Our AI-powered SDLC transforms how modernization gets done — automating code transcription, test coverage, CI/CD, rollout orchestration, and delivery analytics.

Proven Partner Ecosystem & Open Source Foundation

CNCF and Linux Foundation AI & Data participation, plus top hyperscaler partners...

As members of the CNCF and Linux Foundation AI & Data committee, we stay on the cutting edge of open-source innovation — while partnering with top hyperscalers, tool vendors, and AI ecosystems.

Trusted by Platform-Powered Companies

Sinclair, Symphony, TetraScience, GeminiHealth and more rely on CloudGeometry...

Companies like Sinclair, Symphony, TetraScience, and GH rely on CloudGeometry not just to modernize their internal stacks — but to deliver scalable, AI-ready application platforms for their customers.

Easier to Achieve — with CloudGeometry

#AITransformation#EnterpriseAI#AIReadiness#CloudNativeAI#GenerativeAI#ConversationalBI#FinOps

Let’s talk about what’s next for your data, your AI, and your customers.

Schedule a Strategy Call Request a Proposal

The AI Advantage Won’t Wait

Common Challenges We Solve

Legacy data infrastructure

Fragmented pipelines

Runaway compute costs

Enterprise AI skills gap

ROI uncertainty

Team silos

Pilot-to-production stalls

Manual workflows

Vendor lock-in

CloudGeometry AI Adoption Framework

1. AI Readiness & Education

Semi-automated 5-minute interviews capture your leadership’s current AI understanding

Custom workshops showcase business automation opportunities using the latest AI tools

Training sessions align executives and IT teams on vision and outcomes

We assess feasibility to ensure your goals are realistic and actionable

2. Collaborative Use Case Discovery

Review workflows across ERP, CRM, and other core systems to surface automation opportunities

Analyze what industry leaders are automating and identify existing AI agents/technologies for reuse

Capture manual employee activities on web & desktop apps with special mapping tools like Celonis Task Mining, ABBYY Timeline and Apromore to generate structured workflows

Prioritize use cases with business and IT stakeholders based on feasibility, value, and ROI

3. Data & Cloud Technology Assessment

Conduct a Cloud Well-Architected Assessment with emphasis on AI adoption readiness

Evaluate the need and feasibility of unifying data sources into data lakes built on scalable technologies

Identify gaps in data quality, integration, governance, and compliance requirements

Review opportunities for Kubernetes adoption to optimize AI performance, scalability, and costs

4. Smart Technology Choices

Cloud Infrastructure: AWS (EKS), Azure (AKS), or on-prem with OSS options — enhanced with our CGDevX platform or next-gen K8s hosting via ControlPlane with 50%+ cloud cost savings

Data Unification: Consolidate sources into scalable Data Lakes on AWS, Azure, Apache stack, or Databricks — accelerated by Claritype AI-powered schema tools to achieve a golden schema in days, not months

AI Agent Platform: Built on popular OSS frameworks like LangFlow, LangChain, and LangSmith — integrated into our LangBuilder platform for faster prototyping and seamless transition to production with enterprise-ready levels of security, monitoring, and quality controls

Model Selection: Choose from cloud-hosted LLMs (OpenAI, Gemini, Bedrock, Grok) or self-hosted models like Mistral and Ollama for better performance, privacy, and cost control — complemented by industry-specific AI Agents available from both open-source and commercial libraries

Production Deployment with OSS Tooling or LangGraph Platform: Enterprise-grade deployment with MLOps pipelines, quality monitoring, observability, and continuous improvements

5. AI Strategy & Roadmap Design

Build & Test MVP Agents: Deliver quick prototypes of prioritized use cases while parallel cloud/data upgrades are in progress — proving value fast without waiting for full infrastructure overhauls

Define Governance & Risk Guardrails: Establish policies for security, compliance, responsible AI use, and human-in-the-loop oversight — ensuring confidence at the board and regulator level

Set Business KPIs & ROI Models: Tie AI initiatives directly to financial and operational outcomes (e.g., cost reduction, cycle time, CSAT, revenue impact) with clear measurement criteria

Plan Gradual Rollout & Replacement: Launch AI agents step by step to augment workflows, then progressively replace manual and legacy processes with AI-powered business processes that deliver efficiency and scale

6. AI Agents & Application Development

Rapid Prototyping with prompt templates and tool wrappers on our LangFlow-based visual editor, enabling fast agent design and validation

Iterative Development with configurable parameters, embedded guardrails & integrated monitoring hooks to ensure safety and control

Evaluation & Tuning using evaluation datasets, offline evals for accuracy, reliability, and efficiency, plus running UAT

Pre-Production Hardening by integrating with real data sources, adding authentication and authorization, enabling observability, setting fallback paths, conducting load testing, and ensuring compliance readiness

7. Secure Deployment & Staff Enablement

Security & Compliance: Enforce data privacy with masking and redaction, apply RBAC for execution and debugging, log all agent runs for auditability, and manage secrets with Vault/KMS. Ensure compliance with GDPR, HIPAA, SOC2, and implement PII handling pipelines

Observability & Quality Control: Implement telemetry, tracing, evaluation harnesses, and guardrails with fallback paths to ensure accuracy, reliability, and safe operations

Staff Training & Empowerment: Provide hands-on training, documentation, and playbooks so teams can confidently manage, extend, and scale AI systems within governance boundaries

8. Continuous Monitoring & Improvement

Monitor & Observe: Track performance, latency, token usage, and reliability with enterprise observability tools

Evaluate Regularly: Run automated and human-in-the-loop evaluations for accuracy, reliability, efficiency, and compliance

Collect Feedback: Capture structured user and operator feedback to refine prompts, agents, and workflows

Optimize & Evolve: Apply updates, retrain or fine-tune models, and expand automation scope to maximize ROI over time

Why CloudGeometry

Full-Stack Modernization Expertise — Evolved Over a Decade

Cloud-Native & Multi-Cloud Architecture Mastery

AI-Driven Acceleration with an AI-Powered SDLC

Proven Partner Ecosystem & Open Source Foundation

Trusted by Platform-Powered Companies

Easier to Achieve — with CloudGeometry

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