Chatydata methodology
From data maturity to a governed AI pilot.
A practical journey, with clear steps and concrete deliverables, for companies that want to use AI agents safely and create value.
- Focus on business cases, not isolated experiments
- Context and governance before the model
- Traceability and audit from the start
- Responsible scalability, with ROI metrics
Methodology
A practical journey from intent to a working pilot.
Five clear steps, from data maturity to a governed pilot — each with concrete deliverables.
- 01
Diagnostic
We assess maturity, data sources, risks and opportunities.
- 02
Opportunity map
We prioritize use cases by impact, feasibility, risk and context quality.
- 03
Context architecture
We define how data will be organized, governed and served to agents.
- 04
Controlled pilot
We build and validate a pilot agent with metrics and traceable sources.
- 05
Scale roadmap
We plan the next steps to scale with governance, security and ROI.
Offers
Three formats to move forward at your pace.
Executive AI & Context Diagnostic
Typical duration, depending on scope: 1 to 2 weeks
Who it’s for
For companies still figuring out risks, maturity and opportunities.
Deliverables
- Maturity analysis
- Risk map
- Initial source inventory
- Prioritized opportunities
- Executive report and next steps
AI Context Sprint
Typical duration, depending on scope: 3 to 4 weeks
Who it’s for
For companies ready to prioritize use cases and design a practical architecture.
Deliverables
- Context blueprint
- Use-case matrix
- Architecture design
- Minimum governance
- Pilot plan and 30/60/90 roadmap
Assisted Pilot with Real Data
Typical duration, depending on scope: 6 to 8 weeks
Who it’s for
For companies with a clear use case, ready to validate with real data.
Deliverables
- Pilot agent in operation
- Ingestion of selected sources
- Answers with traceable sources
- Quality metrics
- User validation and scale recommendation
Start with the diagnostic
Is your company ready for AI agents?
Start with a free diagnostic and understand which data, processes and risks you need to address before scaling AI in your business.