Architecture
Which systems, APIs, documents, and users must the first AI workflow connect?
AI consulting for CTOs
Ship AI systems that fit your stack and controls.
CTOs need AI architecture that survives production. Ferre Torres B.V. helps technical teams design and build RAG, agents, evaluations, permissions, observability, audit trails, and internal AI-native software.
CTO questions
What has to be true for the AI MVP to become production software?
- Map data sources, permissions, hosting constraints, latency, cost, and ownership before build-out.
- Design RAG systems with source attribution, evaluation sets, retrieval monitoring, and failure handling.
- Build agents with tool boundaries, approval gates, workflow state, and audit logs.
- Turn the first MVP into a maintainable platform component rather than a disconnected demo.
Evaluation
Which examples prove retrieval quality, answer grounding, and escalation behavior?
Controls
Where do permissions, approvals, logging, and human review need to sit?
Operations
Who owns connectors, prompts, dashboards, monitoring, and release changes after launch?
Production RAG
Retrieval systems with permissions, citations, evaluations, and monitoring.
Agentic workflows
Tool use, workflow state, approvals, exceptions, and audit trails.
Company Brain core
A reusable knowledge and orchestration layer over internal data and decisions.
Maintainable software
AI-first internal tools that engineering teams can reason about and operate.
CTO next step
Bring one workflow, current stack, and production constraints.
Use them to define the first architecture slice and decide what must be validated before scale.