Frequently asked questions about CAIAC
How does CAIAC work? How far can it automate marketing? How are data, AI agents, validations and integrations managed? Here are the answers to the main questions before starting.
CAIAC does not seek to let AI run your marketing freely. The platform organizes the methods, data, tools, responsibilities and levels of control needed to turn AI into an execution capability.
Understanding CAIAC
CAIAC is a digital marketing execution platform. It turns methods currently scattered across briefs, teams, agencies, tools and manual tasks into structured, reusable and governed workflows.
As needed, these workflows bring together human expertise, business data, AI models, agents, business rules and external systems to reach the expected result.
A CRM manages customer relationships, a CMS manages content, an advertising platform activates campaigns, and an AI model generates or analyzes. CAIAC operates between the business need and those capabilities.
The platform orchestrates the complete chain: objective → context → method → data → production or decision → controls → validation → activation → measurement → learning. The goal is to reduce fragmentation between these steps.
No. Models are an interchangeable capability within the system.
Workflow adds around them the company context, authorized data, marketing methods, business rules, accessible tools, quality criteria, validations, traceability and measurement of results.
AI provides part of the execution capacity. CAIAC brings the method and orchestration.
Content production is one of the capabilities, but it is only one part of the platform.
The platform also covers workflows related to marketing analysis, acquisition, personalization, CRM and lifecycle, conversion, measurement, experimentation, campaign orchestration and process governance.
The scope actually available depends on the use case selected for Early access or a Guided project.
CAIAC lets you bring in-house and scale some of the work traditionally performed by agencies, particularly work that relies on repeatable methods, extensive coordination or a high volume of execution.
This does not mean all expertise should be brought in-house: agencies and specialists continue to provide strategy, creativity, sector expertise, specialized production, advice and judgment. The difference is that data, methods, rules, workflows and learning accumulate within the company's own system.
No. Teams remain responsible for objectives, strategic choices, rules and sensitive decisions.
CAIAC changes how teams spend their time: less repetitive execution and manual coordination, more decision-making, expertise, creative work and judgment.
Capabilities and workflows
By a result to be achieved, not by a technology to be deployed: launch a product, enter a new segment, reduce the CAC, generate more pipeline, increase conversion, develop re-purchase, produce several hundred pages, improve a campaign.
CAIAC then breaks down this requirement into specialized workflows and coordinates the necessary capabilities.
The objective is precisely to avoid this complexity. The user starts from a business problem; the system determines which workflows, data, expertise and tools need to be mobilized.
The same program can thus involve several disciplines without the user having to manage each technical component separately.
Yes, it is a central dimension of architecture. A product launch may require simultaneously market research, segmentation, value proposition, planning, campaign production, landing pages, media, analytics and validation.
CAIAC orchestrates this work as a single marketing program, while retaining specialized workflows behind each stage.
This is one of the platform's structuring objectives. A method is represented in the form of sequence of steps, roles, necessary data, business rules, acceptance criteria, templates, validations, exceptions and indicators.
Know-how no longer remains only in briefs or in experts' heads: it gradually becomes reusable in execution.
The architecture is designed to represent companies and units, brands, offers, products, segments, markets, languages, users, roles and approvers.
This representation allows workflows to work with the right context depending on the brand, market or team involved. Configurable elements are defined according to the scope of deployment.
AI, oversight and governance
Yes, when their use is relevant — but without starting from the principle that each process must become "agentic".
The process is first analyzed and simplified, and tasks are then divided between humans, AI agents, automations and existing systems, depending on their nature, repeatability, risk and value.
There is no single level of autonomy. A frequent, reversible and low-risk task receives more autonomy than a decision involving the brand, a budget, a price, a customer data or a sensitive publication.
Policies, permissions, limits, evaluation criteria and climbing mechanisms are adapted to workflow. The objective is not the maximum autonomy: it is useful autonomy under control.
The system is designed on the basis that a probabilistic model can produce an error.
According to workflow, several levels of defense are organized: authorized sources, structured data, deterministic rules, acceptance criteria, automatic testing, exception detection, human validation and monitoring after activation. A potential error does not receive the same treatment according to its consequence.
Where its judgement brings sufficient value: strategy, professional expertise, brand knowledge, creative leadership, sensitive decision-making, arbitration, exception, validation.
Repetitive or deterministic checks are handled earlier in the workflow so human review does not itself become the next bottleneck.
By gradually transforming brand knowledge into a repository that can be used by workflows: value proposition, claims and evidence, terminology, prohibitions, examples, editorial rules, validated assets and acceptance criteria.
This goes beyond a prompt asking a model to respect the brand's tone of voice. Sensitive cases remain subject to human approval.
Roles, responsibilities, approvals, validation circuits and assignment rules are part of the architecture. A legal specialist, brand manager or product manager receives only those decisions that really fall within his or her expertise.
The available perimeter is configured according to the workflow set up.
On several dimensions: quality, robustness, respect for rules, safety, cost and behaviour on difficult cases.
The results are used to authorize, limit or suspend certain uses, and to reassess new versions. Autonomy must be earned through evaluation, not assumed simply because a model appears capable.
Data, integration and security
Depending on the use case, the context may come from CRM, catalogues, content, analytics, product data, transactional data, campaigns, user behaviors, brand repositories or other business data.
The principle is to use only the necessary and authorized sources for the workflow concerned.
No. CAIAC orchestrates and mobilizes existing systems; it does not rebuild the company's technological stack.
The connections cover different types of systems — analytics, advertising, CRM, messaging, CMS and DAM, commerce and PIM, data warehouse and BI, other business services. Each connection retains its own rights, accessible objects and constraints.
An integration is not simply connected and made available to the entire system. Each connection is assessed for the service involved, intended use, owner, permissions, accessible objects, required tests and operational health.
Only validated capabilities are then made available to authorized workflows.
No. According to the chosen architecture, CAIAC works with existing sources of truth and accesses only the necessary data.
The right architecture depends on volume, sensitivity, frequency of use, necessary performance and IT and security policies.
This depends on the suppliers and configurations selected.
For each use case, you need to establish which data is sent, to which service, for what purpose, under which retention rules and with which conditions for use or training. There is no single policy independent of the provider used.
Purposes, identities, consents, restrictions, data residency and retention rules are represented in the environment configuration.
The aim is to ensure that data does not become usable simply because it is technically accessible: its use must also be authorized for the relevant context.
The architecture distinguishes users, teams, roles, permissions, approvals, accounts and external connections, with a life cycle of accesses including authentication, MFA, sessions, suspension and revocation.
The level actually available depends on the scope of deployment.
The rights depend on the source assets, licenses, models used, suppliers, type of content produced and contractual terms. These elements are arranged for each use.
Workflows may also include rules on authorized sources, claims, assets and validation requirements.
Deployment, maturity and services
Not necessarily. A sufficiently limited use case can start with some well-identified sources.
On the other hand, some projects reveal more structural problems: fragmented data, lack of repositories, inadequate architecture, unconnected tools, inadequate governance, and informal processes. Pre- or parallel work on foundations may then be necessary.
The useful minimum: a clearly identified business objective or process, some examples of inputs, the expected result, key business rules, responsible persons, relevant data and systems, and indicators to measure change.
The first task is often to understand where the current bottleneck actually lies.
There is no standard time frame for all cases. Time depends on workflow, data, integrations, rules, number of teams, level of control and activation perimeter.
Early access favours a limited case. Complex projects begin with a scoping to establish a realistic scope and schedule.
This depends on the case: marketing, CRM, e-commerce, data, IT, product, brand, legal, sales, agencies or external specialists.
CAIAC's purpose is to make teams' responsibilities and handoffs more explicit within the workflow.
CAIAC proposes a layer of Expert services when the environment needs to evolve before use cases can scale: maturity assessment, roadmap, operating model and governance; architecture, data engineering, automation, MarTech and analytics; production AI, agents, RAG and context-specific data science.
The services are an accompanying layer, not a systematic prerequisite for the use of CAIAC.
Yes. The perimeter also covers acquisition, CRM lifecycle, qualification and RevOps, measurement, conversion, commerce, reputation, workflow automation, conversational experiences and agent governance.
The current Early access offering does not necessarily cover this entire scope: scoping establishes what can be activated for your use case.
Impact on business, access and engagement
Workflows are grouped into three impact families: unlock new growth opportunities (launches, new segments, coverage, production capacity), accelerate acquisition and conversion (CAC, pipeline, conversion, campaign performance) and grow customer value (repeat purchases, frequency, basket size, retention and LTV).
Specific indicators are defined as appropriate.
No. CAIAC improves the execution capacity that influences these indicators, but no level of uplift can be guaranteed independently of the offer, market, data, traffic, context and execution.
The aim is precisely to make these effects measurable, rather than assuming that automation necessarily creates value.
A baseline is defined before or at the beginning of the perimeter: time, costs, volumes, resources, errors, delays and business performance. The new workflow is then compared to this situation.
Where context permits, testing, control groups and incrementality analyses allow for a better distinction between correlation and actual contribution.
CAIAC is currently being rolled out gradually to a limited number of organizations. Early access lets you work on a real, clearly scoped use case, with objectives, scope, data, workflows, approvals and metrics defined before starting.
It also allows CAIAC to consolidate the platform with feedback from real-life situations.
No, and this distinction is important.
The platform available today brings together the capabilities that can actually be activated within a project or Early access. Target architecture is the complete system that CAIAC is gradually building: workflow catalogue, multi-practice orchestration, business configuration, integrations, agents, administration and governance.
Before each commitment, the actual available perimeter is explicitly confirmed.
When the perimeter involves several teams, multiple systems, multiple markets, specific integrations, data issues, more complex governance or a need for organizational or technical transformation.
A scoping then allows to break down the problem and define dependencies before deployment.
Each commitment is dimensioned according to the business problem, the necessary workflows, volumes, integrations, responsibilities and level of service and support.
The scope is therefore defined after a first qualification of the need.
Does your problem not match a standard feature?
That is often a good starting point. Tell us the result you want to achieve, your current process and the main constraints. We can then determine whether the best starting point is Early access, a Guided project or preliminary work on data and AI foundations.