Build the data and AI foundations you need to scale.
When the company's data, systems or processes are not yet mature enough, our teams support the transformation needed to make CAIAC and AI sustainable.
Digital transformation is rarely a purely technological problem.
Strategic
- Data strategy and ia
- Prioritisation of use cases
- Governance
- Organization
- Skills
Tactical
- Operating model
- Architecture
- Integration
- Technological choices
- Process
Operational
- Engineering
- Automation
- Data quality
- Deployment
- Monitoring
- Activation
Weakness in one of these dimensions can prevent the relevant use of technology on a scale, even when AI models themselves are efficient.
Three areas of expertise.
- 01
Organizational & Data Strategy
Define where to go and build the model to achieve it.
Digital & Data Maturity Diagnostic
Assessment of data, organizational, technological and AI maturity.
Strategic Data & AI Roadmap
Roadmap prioritized according to impact, feasibility and dependencies.
Data Strategy & Operating Model
Organization, responsibilities, ownership and operation of teams.
Data Governance, Security & AI Governance
Quality, access, security, rules of use and AI governance.
- 02
Data & Automation Ecosystem
Build the environment that allows data to flow to action.
Digital Transformation & Architecture Planning
Transformation path and target architecture plan.
Data Engineering & Architecture
Collection, processing, storage and provision of data.
Automation & MarTech Integration
Link CRM, analytics, catalogues, content, advertising and business systems to make data really usable in workflow marketing.
Analytics & Business Intelligence
Measurement, restitution and shared reading of performance.
- 03
AI & Decision Intelligence
Go from isolated AI experiments to systems that are truly usable in the organization.
AI Factory & Industrialization
Transition of prototypes to deployed, monitored and maintained uses.
Generative AI, RAG & AI Agents
Assistants, augmented research and agents backed up by the company context.
AI & Data Science Portfolio Management
Arbitrage and follow-up of a portfolio of projects according to their value.
Custom Data Science & Decision Intelligence
Forecast, segmentation, recommendation, optimization, scoring, decision aid and specific models.
From diagnosis to industrialization.
- 01DiagnosisUnderstand maturity and gaps.
- 02DesignDefine strategy, roadmap, governance and architecture.
- 03BuildBuild the necessary data, automation and AI capabilities.
- 04ScaleIndustrialize usages with CAIAC and company systems.
Services only work where your environment needs them.
Your data is scattered
→ Data Engineering & Integration
Your AI roadmap lacks priorities
→ Maturity Diagnostic & Strategic Roadmap
PoCs don't go into production
→ AI Factory & Industrialization
Marketing, Data and IT work in silos
→ Operating Model & Governance
Your need requires specific intelligence
→ Custom Data Science & Decision Intelligence
The services build the foundations. CAIAC industrializes the execution.
- 01Strategy & Organization
- 02Data & Architecture
- 03AI & Decision Intelligence
- 04CAIAC
- 05Marketing Execution at Scale
We can only intervene on the necessary foundations, then let CAIAC capitalize these capabilities in day-to-day marketing execution.
Identify the capabilities that are lacking today to scale up.
We can start with a targeted diagnosis or directly with the data, technology or AI problem that blocks your project.