RAG implementation
Document ingestion, search, ranking, citations, permissions, evaluation sets, and source-grounded answers.
RAG implementationAI implementation services for companies
Build AI systems for companies.
Ferre Torres B.V. helps companies move from AI strategy into working systems: RAG, agents, Company Brain, finance intelligence, AI-native internal tools, evaluations, integrations, and production rollout.
- RAG systems
- Agentic workflows
- AI MVP delivery
- Production rollout
Implementation fit
Best for companies looking for an AI implementation partner.
- Move beyond AI workshops by selecting one workflow that can be built, tested, and owned by the business.
- Connect documents, dashboards, tools, databases, tickets, and operating metrics into AI-ready systems.
- Build narrow MVPs or PoCs that validate usefulness, quality, adoption, risk, and production constraints.
- Harden proven systems with permissions, evaluations, monitoring, human review, audit trails, and handover.
Implementation services
Systems that turn AI ambition into operational capability.
Agentic workflows
Controlled tool use, routing, approvals, escalation, logging, and human review inside real business processes.
Agentic AI workflowsCompany Brain
A private operating layer for documents, decisions, metrics, workflows, and reusable AI context.
Company Brain systemsFinance intelligence
Dashboards and assistants for margin, cash, pricing, forecasts, anomalies, trend detection, and reporting automation.
AI finance dashboardsAI-native internal tools
Internal SaaS, workflow apps, AI PM assistants, expert-support tools, integrations, and production user interfaces.
AI software engineeringMVP and PoC builds
Thin working systems that validate business value before a larger production implementation.
AI MVP and PoCImplementation path
Start narrow, then turn proven components into company infrastructure.
The implementation path is designed to create useful software quickly without pretending every AI workflow is ready for company-wide rollout.
- Define
Confirm the workflow, owner, users, data sources, security constraints, and proof target.
- Build
Implement the first RAG, agent, dashboard, Company Brain, or AI-native workflow surface.
- Evaluate
Measure quality, adoption, latency, failure modes, source grounding, and business value.
- Harden
Add permissions, monitoring, audit trails, human review, deployment support, and handover.
Buyer questions
AI implementation should answer the concerns of leadership and technical teams.
CEO
Which workflow becomes faster, more scalable, or more differentiated if the system works?
CFO
Which reporting, finance, pricing, or decision loop has enough value to justify production investment?
CTO
How will data access, integrations, evaluations, monitoring, ownership, and deployment be handled?
Users
Does the implemented workflow improve daily work enough to create real adoption?
Implementation outputs
What should exist after the first implementation slice.
Working product surface
A usable assistant, dashboard, workflow app, RAG console, or internal tool around a real workflow.
Data and integration map
Sources, permissions, APIs, documents, metrics, limitations, and next integration steps.
Evaluation set
Business questions, expected answers, source checks, failure examples, reviewer notes, and quality thresholds.
Production decision
A clear recommendation to harden, expand, pause, or redirect the AI implementation.
AI implementation questions
Questions companies ask before moving from AI strategy to build.
What are AI implementation services?
AI implementation services turn a selected business workflow into working software by connecting data sources, designing architecture, building AI components, validating an MVP or PoC, and preparing the system for production rollout.
How is AI implementation different from AI consulting?
AI consulting defines the opportunity, scope, architecture, and business case. AI implementation builds and integrates the system: RAG, agents, dashboards, Company Brain components, internal tools, evaluations, monitoring, and deployment.
What should a company implement first?
The first implementation should be one workflow with a clear owner, accessible data, real users, security constraints, and a measurable result that can prove whether the system should scale.
Can implementation start with an MVP or PoC?
Yes. A focused MVP or PoC is usually the safest way to validate usefulness, data readiness, workflow fit, adoption, risk, and production requirements before a larger implementation.
Implementation next step
Bring the workflow that should become working AI software.
Share the business area, current workflow, tools and data sources, expected users, security constraints, and what the first implementation should prove.