An Enterprise as a Service platform and engagement service that start with a diagnostic of your reality to adapt — not impose — an AI-assisted development framework.
Lovable, Cursor, Bolt, Base44 or Lindy have shown that any professional can generate software in hours. It's a transformation comparable to the arrival of the internet. But just as then, the value is not in the tool — it's in how it integrates into the company's processes.
Companies paying annual licenses for tools covering only 60% of their processes discover they can build exactly what they need — in weeks and at a fraction of the cost.
Without architecture, data governance or auditable processes, AI-generated applications die on first contact with the real world: security, compliance, scalability and integration.
The pattern we see repeating: brilliant teams that start with enthusiasm, quickly hit a complexity ceiling, and end up with a graveyard of pilots that never consolidated as organizational assets.
Internal AI hackathons, teams using agents and MCPs in point projects, prototypes with real impact on customers or internal efficiency. Experimentation is not the bottleneck. The challenge comes next: turning those initiatives into organizational assets — governed, maintainable, integrated with the rest of the system and transferable across teams.
This document is aimed especially at technical teams who already know the territory and are looking for how to make the leap from brilliant prototype to enterprise process.
At CLOUDFRAMEWORK we have spent more than a decade deploying enterprise platforms and, since 2023, helping teams bring generative AI into their processes. What we have learned is that every company starts from a different point: culture, technical team maturity, business model, regulation, stack and — most importantly — the way they already do things well.
That is why this document does not propose a closed methodology for you to adopt. It proposes a reference framework supported by a platform and a consulting service that always start by understanding your reality before proposing changes.
Operating principle: our experience is an asset we bring to the table; your methodology, your team and your context are the starting point. The joint work is finding which parts of that experience are transferable to your reality — and discarding what doesn't fit.
An Enterprise as a Service platform that centralizes the development cycle — from business documentation to supervised execution — with enterprise AI integrated in a governed way.
A phased engagement that starts with a diagnostic of your reality, co-builds the working framework with you, and validates the result on a real case before scaling.
The platform provides the enterprise infrastructure that allows AI to operate in a controlled way. What is built on top — your processes, your data models, your conventions — is yours and adapts to your organization.
A living repository of processes, sub-processes, requirements and verifications. It is the source of truth the AI uses to understand the business before generating anything.
CLOUD Documentum lets you structure across three areas — business, development and production — the documentary elements the organization centralizes in the platform itself: operational processes and sub-processes, functional requirements, acceptance criteria, manuals, support guides, living technical documentation… all in one system with a common model. The data is always the customer's property — neither knowledge nor information sit in individual silos or third-party tools.
This is the new language of AI on the business layer: what is structured here is what the AI can understand, reason about and operate on. What is not documented simply does not exist for the AI.
This is where every project in the organization is structured: development units, functional modules, libraries, APIs and data models — from a technical viewpoint, but without ever having to access the source code.
That abstraction is what lets strategic and planning AI drastically accelerate: it no longer needs to read entire repositories to understand what exists, what is used and how it fits together. It works on a curated and maintained representation of the system, not on thousands of files.
CLOUD Development becomes the source of truth for development — the new language of AI on the technical layer. On top of it rest planning, AI-assisted task generation, change traceability and deployment cycle integration.
Infrastructure on Google Cloud, AWS or others, modular architecture, ISO 27001 security and built-in compliance — the foundation on which this abstraction is viable at enterprise scale.
Enterprise AI layer over MCPs (Model Context Protocol). Connects any LLM — proprietary or third-party — with the information structured in Documentum and Development, and with the organization's processes. Authentication, 2FA and full traceability of every interaction, by default.
Internal team capability-building with courses, content and assessments — so adoption doesn't depend on specific individuals but stays in the organization. The model's knowledge is transferred in a structured and traceable way.
| Traditional SaaS | AI without control (Lovable, Bolt…) | CLOUD Development + CloudIA | |
|---|---|---|---|
| Customization | Limited to the provider | Total, no structure | Total, with solid architecture |
| Data control | At the provider | Scattered and ungovernable | In your infrastructure, governed |
| Security | Provider-dependent | Nonexistent | ISO 27001 integrated |
| Continuity | You depend on the provider | You depend on the person who built it | Documented and transferable knowledge |
| Adoption | You adapt to the product | Each person does their own thing | Co-built with your team |
We call it a framework, not a prescriptive methodology. Three levels that order where AI can contribute and where it needs quality information to avoid mistakes. How each level applies in your company is defined in the diagnostic — because it depends on your culture and technical maturity.
What you want to build, in functional language. Processes, sub-processes, requirements. AI helps the business team structure what they want and detect gaps in the definition — not to program.
Translation of business into development units. Stack, conventions, CI/CD and QA documented. We apply reverse engineering on what already exists — we don't ask you to start from scratch.
Tasks with timeline planning and human supervision. Developers supervise and correct what AI proposes; each correction feeds the system back and makes it more accurate.
Non-negotiable. Structured information must exist before AI acts (data models, APIs, libraries, resources). Without that substrate, AI is just a prototype generator that dies on scaling.
Negotiable. How it is documented, which tools are used, what level of automation applies to each team, which parts of the framework are adopted from day one and which later.
The service is designed to reduce entry risk. It starts with a scoped diagnostic, validates on a real case, and only scales when there is evidence the framework works in your organization.
We understand your current methodology, your stack, your technical culture and your roadmap. We identify which parts of our experience are transferable and where the fit requires specific adaptation.
2–3 weeksWe adapt the three levels to your reality. Together we define what is documented, how and where. We agree on AI usage policies, data governance and human review criteria.
3–4 weeksWe apply the framework to a real case chosen with you. Business, planning and implementation teams work together. Tangible result and gaps detected in real time.
2–4 weeksOnce validated, the framework extends to more teams and projects. We establish oversight points — also AI-powered — to sustain standards as adoption grows.
OngoingPhase 1 commitment. The diagnostic is a closed deliverable: a report with (a) a map of your current reality, (b) areas where CLOUDFRAMEWORK experience applies directly, (c) areas requiring specific adaptation, and (d) a pilot case proposal. If after the diagnostic you decide not to continue, you keep the report regardless.
The AI conversation in a mature company is not "do we use AI?". It is "what role do we give it, what do we control, what do we leave open, and how do we avoid creating silos parallel to the technical governance we already have?". These are the principles we work from — and all of them are reviewable in the diagnostic.
If each person uses their own assistant on their own, knowledge leaks and standards dilute. Enterprise AI must operate on the organization's information and with common policies.
Before accelerating code generation, you have to know what data the AI touches, where it is stored, who accesses it and how it is audited. CloudIA operates with authentication, 2FA and traceability by default.
In execution, AI generates and proposes; developers supervise, correct and approve. This loop is what turns AI into an organizational asset, not a black box.
You don't lock yourself into an LLM or a provider. The architecture lets you change model, cloud provider or stack without rewriting what's been built.
ISO 27001, GDPR, environment separation and retention policies. Enterprise AI lives in an auditable perimeter, not in a sidecar Slack.
If your team has a mature technical culture, the framework adapts to it — not the other way around. The consulting exists to find the fit, not to impose convictions.
A note on "ego" in these conversations. When we talk to mature technical teams, we start from something simple: we have been doing this for a while and have seen patterns, but your team knows your company much better than we do. The diagnostic exists precisely so our experience translates into something useful for you — and to honestly discard what does not apply.
These figures are benchmarks based on real projects with EaaS + CloudIA platform vs. equivalent traditional development. Your specific case is quantified in Phase 1.
| Horizon | What you get |
|---|---|
| Week 2–3 | Closed diagnostic, current-reality map, pilot case agreed, platform deployed and CloudIA connected. |
| Week 4–6 | Adapted framework documented. Team trained on applicable parts. First AI-generated development units. |
| Week 6–10 | Hackathon executed. Pilot case in production or pre-production. Gaps identified and fixed live. |
| 3 months | Key areas documented across the three levels. Extension to multiple projects. Positive spiral: the system improves with each iteration. |
| Long term | Independence from rigid SaaS licenses. AI as an auditable organizational capability. Continuous compliance. Living and transferable knowledge. |
Once you decide to move forward after the diagnostic, the rollout follows three clear steps — no long contracts, no multi-year lock-ins.
The platform scope (CLOUD Development + CloudIA) is closed, service terms are signed, and the base configuration is executed: tenants, environments, identity, integration with your infrastructure and security policies. The platform is operational and connected.
The CLOUDFRAMEWORK team that will accompany you is assigned (lead consultant, technical leads and AI references) and working sessions for the next three months are planned: implementation agenda, pilot milestones, dynamics with your team and review points.
At the end of that horizon an informed decision is made: expand services (more cases, more teams, ongoing support) or close the engagement because your organization already has autonomy to keep moving forward independently on the platform. Success also means you stop needing us.
Exit principle. At any of the three steps, what is built stays in your organization: documentation, processes, data models, code and knowledge. The platform is Enterprise as a Service, not a dependent service that vanishes if you leave.
A first 60–90 minute conversation with your technical team to understand your reality and propose whether — and how — a formal Phase 1 diagnostic makes sense. No commitment, no sales materials: a technical, honest and useful meeting.
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