01
Scope
Is this an isolated workflow, a business function, or a system that touches several teams?
CComparison
Which type of partner for your AI project?
Architecture cabinet, automation agency, expert freelancer, training, SI integrator: five intervention models answer five different ambitions. This page compares them factually across dimensions, to help leadership choose - with no actor named, no value hierarchy, no disparagement.
Scope
What this page is not.
Three precisions before reading the table.
- 01Not a ranking - each model has a legitimate best use case; the point is to match the right approach to the right ambition.
- 02Not a nominal comparison - no market actor is named; the characteristics describe intervention models, not specific organizations.
- 03Not a promise - durations and profiles given are typical orders of magnitude; every engagement has its context.
Decision criteria
Before comparing providers, clarify the real ambition.
The right partner depends less on the word AI than on the level of transformation expected. Four questions help avoid choosing a model that is too tactical for a strategic stake, or too heavy for a simple need.
02
Risk
Does the topic involve sensitive data, compliance, human validation, customer exposure or leadership arbitration?
03
Durability
Should the deliverable last a few weeks, or become an operable and maintainable system component?
04
Autonomy
Does the organization want to delegate execution, build capability, or regain control of a system?
MAP C.01 - INTERVENTION MODELS
INDICATIVE READING
Architecture and governance
Execution or transfer
Point need
Durable system
01
Architecture cabinet
Frame a target, govern trade-offs and make a cross-functional AI system operable.
Governed system
02
Automation agency
Quickly automate a known process, often with a tool or workflow logic.
Known workflow
03
Expert freelancer
Solve a bounded technical problem with strong individual expertise.
Targeted problem
04
AI training
Build team capability without delivering or operating the system.
Team capability
05
SI integrator
Integrate an AI component into an existing IT stack on stabilized specifications.
IT integration
The position is indicative: the right model depends on context, data, risks, budget and internal capacity.
Table
Five intervention models, compared by dimension.
Eleven dimensions structure the reading. The first column anchors the typical ambition; the following describe how each model approaches it.
| Dimension | Architecture cabinet | Automation agency | Expert freelancer | AI training | SI integrator |
|---|---|---|---|---|---|
| Project ambition | Build a governed AI system, 12-24 month horizon. | Automate an identified process, short term. | Solve a precise, bounded technical problem. | Build internal team capability. | Integrate an AI component into the existing IT stack. |
| Type of problem addressed | Architecture, governance, scaling, structural choices. | Existing operational workflow to streamline. | Bounded task requiring deep expertise. | Insufficient team capability against AI use cases. | Defined specifications to deliver into IT. |
| Expected deliverables | Blueprint, mapping, governance principles, run. | Configured workflows, operational automations. | Code, model, prototype, targeted delivery. | Training modules, materials, optional certifications. | Integrated solution, documentation, ops handover. |
| Typical duration | 6 to 16 weeks of framing, then continuous run. | A few weeks per initiative. | A few days to a few weeks. | A few days to a few weeks depending on format. | 3 to 12 months depending on integration scope. |
| Profile of practitioners | Senior architects, advisory and arbitration posture. | Tool consultants, low-code / no-code integrators. | Specialist practitioner (data, AI, dev). | Trainers, instructional designers. | Project managers, engineers, mixed vendor / client teams. |
| Governance and accountability | Formal framework, traceability, embedded human validation. | Variable, often light; depends on the contract. | Limited to the entrusted scope. | Out of scope (training does not operate the system). | Contractual project framework, defined SLAs. |
| Moving from POC to operating system | System construction planned from framing onward. | Possible if the tool fits; architectural ceiling risk. | Difficult beyond an individual contributor scope. | Indirect - the trained team builds afterwards. | Good on fixed scope; heavy if the target evolves. |
| Handling technical and organizational debt | Anticipated by the target architecture. | Accumulating risk if tools multiply without orchestration. | Often transferred back to the client at end of mission. | Not applicable (no operational deliverable). | Depends on the quality of initial framing. |
| Required client involvement | High - workshops, decisions, ongoing validation. | Medium - initial scoping then acceptance. | Low - the freelancer delivers; follow-up is occasional. | High - the team is the one learning. | Medium to high - project committees, contractual governance. |
| Best use case | Organization wanting a coherent, governed AI system. | Well-identified process to automate quickly. | Bounded technical problem with a deadline. | Mature internal team ready to build its own use cases. | Integration into existing IT with frozen scope. |
| Limits of the model | Significant initial commitment cost; not a flash format. | Shallow architectural depth; vendor-lock risk. | No cross-cutting methodological framework; bus factor. | Does not operate or deliver a system. | Execution posture stronger than advisory posture on architecture. |
Decision
When to choose which model?
A five-line heuristic to orient the decision before any commercial conversation.
Agentic architecture cabinet
When the stake is to set a target architecture, structure governance and converge several AI initiatives into a coherent system - not to agentify an isolated workflow.
Automation agency
When you have identified a process to automate, the tool is known, and the priority is fast execution, not structural system design.
Expert freelancer
When a bounded technical problem requires deep expertise, on a clear scope, with a deadline and limited dependency on the rest of the system.
AI training
When the internal team is mature enough to build its own use cases, provided it is equipped methodologically and technically.
SI integrator / consultancy
When the need is to deliver a solution integrated into an existing IT system, on a stabilized specification, within a classical contractual project frame.
Positioning
LeadsFlowAI's place.
LeadsFlowAI is not a training provider, not an automation agency, not a delivery freelancer, not an SI integrator. It is an agentic architecture cabinet: its function is to scope, design, govern and make operable AI systems at the scale of an enterprise.
This cabinet posture is useful when the stake is no longer to test a tool, but to structure a system - and when that system must hold in front of the executive committee, the CIO, the regulator and the teams using it day to day.
For more tactical needs (automating a known workflow, training a team, delivering an IT integration), other models are better suited. This page exists precisely to make that choice serenely.
Diagnostic
Not sure which model fits your situation? Let's map it together.
A one-hour scoping conversation identifies your real ambition, the most suitable intervention format, and - if the cabinet is not the right answer - says so plainly.