FT Ferre Torres B.V.

AI solutions for companies in Europe

AI solutions for companies that need production impact.

Ferre Torres B.V. provides AI consulting and implementation for companies: private company brains, retrieval systems, agentic workflows, finance intelligence layers, and expert assistants. The previews below define where value appears, what needs to connect, and how a first MVP can scale.

  • MVP first
  • RAG and agents
  • GDPR-aware
  • Production architecture
Start an AI project discussion AI integration roadmap MVP and PoC path Choose the right solution AI implementation services

What can be built

Solution areas that turn AI consulting into working software.

These are not generic chatbot concepts. Each solution is framed as a business workflow, an implementation path, and a future demo surface.

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01 Core platform

Company Brain workbench

A private knowledge and decision layer over documents, metrics, tickets, meetings, policies, and operational history.

  • Knowledge ingestion and permission-aware retrieval
  • Decision memory for leadership and delivery teams
  • Dashboards and assistants backed by company context
First questions

Which systems hold the knowledge? Who should access it? What decision should become faster?

Company memory Private RAG Executive visibility
Explore Company Brain View guided preview
02 Finance

Finance intelligence dashboard

Cash, margin, pricing, forecast, and anomaly views with AI explanations and recommended follow-up actions.

  • Dynamic pricing and margin trend detection
  • AI explanations over finance and operating metrics
  • Less dependency on Tableau or Power BI for repeated questions
First questions

Which metrics drive decisions? Where are reports manual? What should leadership see weekly?

Dynamic prices Spot trends Board-ready views
Explore finance AI View guided preview
03 Retrieval

RAG readiness and implementation

A structured path from scattered documents to reliable retrieval, grounded answers, source attribution, and production monitoring.

  • Data source, metadata, and permissions assessment
  • Chunking, ranking, evaluation, and feedback loops
  • Architecture for regulated or sensitive company data
First questions

Which documents matter? What does a correct answer need to cite? How will retrieval quality be tested?

Source-grounded Evaluated retrieval Access control
Explore RAG implementation View guided preview
04 Operations

Agentic workflow console

Controlled agents for drafting, routing, updating, reconciling, and escalating work with human review where risk matters.

  • Tool-using agents connected to real workflows
  • Approval gates, audit traces, and exception handling
  • Reusable orchestration patterns across departments
First questions

Which actions can be drafted? Which actions need approval? What failure modes must be blocked?

Human-in-loop Tool use Operational speed
Explore agentic systems View guided preview
05 Delivery

AI PM assistant

Project context, risks, decisions, follow-ups, blockers, and delivery status across teams, tools, and meeting notes.

  • Automatic status synthesis from delivery context
  • Risk, blocker, owner, and decision tracking
  • Leadership updates without manual reporting drag
First questions

Where does project truth live? Which risks are missed? What status update should be automatic?

Status intelligence Risk tracking Faster alignment
Explore operations AI View guided preview
06 Expert work

Expert workflow replica

Software-backed expert work with human oversight, evaluation, controlled execution, and a path from bespoke service to scalable system.

  • Codify expert judgment into repeatable workflows
  • Evaluate outputs before automation is expanded
  • Turn high-value services into AI-native software
First questions

What expert task repeats? What makes an output acceptable? Where should human review stay mandatory?

Expert replicas Evaluation AI-native SaaS
Explore AI engineering

Clear next step

Start with one email. We turn it into a practical MVP path.

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  • Choose one high-leverage workflow with a clear owner, users, and success metric.
  • Build a narrow MVP or PoC using realistic constraints and synthetic or anonymized demo data when needed.
  • Evaluate answer quality, workflow speed, adoption, governance, and the cost of operating the system.
  • Scale into production only when the workflow proves useful to both business and technical teams.

High-level scoping questions

Pick the implementation type, then answer only the questions that matter.

These questions help leadership and technical teams decide whether the right first step is a demo, MVP, PoC, architecture review, or production build.

Company Brain

What knowledge should become searchable, permission-aware, and reusable across the company?

RAG implementation

Which answers must cite sources, respect access rights, and pass quality evaluation before launch?

Agentic workflow

Which repeated actions can be drafted, routed, reconciled, or escalated with human oversight?

Finance intelligence

Which reports, margin changes, pricing trends, or anomalies should be explained automatically?

AI PM assistant

Which projects lose time to manual updates, unclear decisions, missed blockers, or fragmented context?

Expert workflow replica

Which expert task is valuable, repeatable, measurable, and suitable for software-assisted execution?

Common architecture

The same foundation supports multiple AI-first systems.

Most useful company AI systems share a small set of core components. The exact stack depends on data residency, security, existing tools, budget, and how quickly the first workflow needs to reach production.

Data connectors

Documents, databases, SaaS tools, spreadsheets, tickets, CRM, ERP, meetings, and internal APIs.

Knowledge layer

Metadata, permissions, embeddings, search, retrieval evaluation, and source attribution.

Reasoning layer

Prompts, agents, workflow orchestration, guardrails, tool use, and human approval points.

Product surface

Dashboards, assistants, admin panels, audit views, reporting workflows, and API access.

Open technical architecture diagrams

AI solution questions

Questions companies usually ask before building.

Can this start as an MVP or PoC?

Yes. A focused MVP is often the best way to prove business value, understand data constraints, and decide whether a system deserves production investment.

Do public demos need client data?

No. Public demos should use synthetic or anonymized data. Client implementations should run in private environments with the right permissions, audit trails, and governance.

What makes a good first AI workflow?

A good first workflow has repeated manual effort, accessible context, clear users, measurable outcomes, and enough business value to justify scaling after validation.

How is this different from a chatbot?

The goal is not a generic chat window. The goal is AI inside the workflow: connected data, reliable retrieval, business logic, user interfaces, and controlled actions.

Next step

Use one solution preview to scope a real company workflow.

Share the workflow, users, data sources, expected usage, and target business outcome. Ferre Torres B.V. can help turn it into a practical MVP plan and production architecture.

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