Executive sponsor
Who owns the business outcome and can remove blockers?
AI consulting for enterprises
Enterprise AI transformation tied to working software.
Ferre Torres B.V. helps enterprise teams select high-value AI workflows, validate them through MVPs or PoCs, and scale useful systems into governed internal platforms, RAG layers, agentic workflows, and AI-first software.
Enterprise fit
Large companies need a route from AI ambition to governed implementation.
- Select the first workflow based on data availability, user value, risk, and executive visibility.
- Prototype with real constraints instead of disconnected innovation-lab demos.
- Design architecture for permissions, monitoring, evaluation, deployment, and long-term ownership.
- Scale the validated workflow into reusable infrastructure or internal AI-native software.
Workflow priority
Which workflow has enough value to justify a serious MVP?
Data readiness
Which systems, documents, permissions, and teams must be connected?
Production path
What must be true for the MVP to become infrastructure?
Enterprise engagement models
Start where the company has enough clarity to move.
Larger companies do not need to buy a vague AI transformation program before seeing value. The engagement can start at the level that matches the current maturity of the workflow, data access, and internal ownership.
Opportunity assessment
Rank AI workflows by value, feasibility, data readiness, risk, and sponsor strength.
Assessment pageIntegration roadmap
Define the sequence for RAG, agents, dashboards, Company Brain architecture, and governance.
Roadmap pageMVP or PoC build
Ship one working workflow with real users, realistic constraints, and clear scale criteria.
MVP and PoC pageProduction implementation
Harden the validated system with permissions, monitoring, evaluations, ownership, and rollout support.
Architecture pageProcurement and leadership fit
What to define before the first enterprise AI build.
- Business owner, technical owner, target users, and the operating metric that proves value.
- Data sources, permissions, hosting constraints, security requirements, and GDPR posture.
- Evaluation criteria for accuracy, usefulness, adoption, workflow speed, and failure handling.
- Scale decision: what has to be true before the MVP becomes production infrastructure.
Enterprise AI questions
Questions enterprises ask before choosing an AI consulting route.
How can an enterprise start an AI consulting engagement?
A practical enterprise engagement can start with an AI opportunity assessment, an integration roadmap, an MVP or PoC build, or a technical architecture review depending on how clear the workflow and constraints are.
Does enterprise AI consulting require a long transformation program?
No. The preferred path is often to select one high-value workflow, validate it through a narrow MVP or proof of concept, then scale only the components that prove useful.
Who should be involved from the enterprise side?
The engagement should include a business sponsor, a technical owner, users from the workflow, and finance, compliance, security, data, or operations stakeholders needed for production decisions.
What should procurement or leadership evaluate first?
Evaluate the business outcome, data access, security and hosting constraints, expected users, success metrics, ownership model, and whether the first MVP can become reusable infrastructure.
Next step
Start with one enterprise workflow leadership already cares about.
Define the owner, user group, data sources, risk profile, and decision metric.