Understand.
Then decide.
Answers to key questions about the platform,
its principles and the points to assess for your project.
VisionWhy three layers?
Alveos combines a DAM to organise and govern content, a DAC to federate knowledge and make it usable by AI, and interfaces tailored to business needs. Each layer has a distinct responsibility. External sources can feed the DAC directly without passing through the DAM.
Established foundationWhat does the DAM cover?
The DAM is Alveos’s established foundation: centralised media and documents, metadata, search, versions, user permissions and workflows. The exact scope, formats and volumes must be validated for your project.
VisionWhat is the difference between DAM and DAC?
The DAM governs the assets it is responsible for. The DAC brings together knowledge from the DAM and other authorised sources for search, assistants and agents. It does not necessarily replace source systems.
VisionDo we need to migrate all our data?
Not in the proposed architecture: the DAC can connect to sources without requiring a general migration. Synchronisation strategy, replicated data and deletion mechanisms must be defined for each source.
Established capabilityCan I connect my own LLM or AI assistant to Alveos tools?
Yes. The Alveos DAC exposes a secure MCP (Model Context Protocol) that lets you connect your own AI assistants and models, including those from OpenAI, Claude or Mistral, through a compatible tool. You can build agents around the data and knowledge in the DAC while retaining control of access: you define who may view which information, and agents access resources according to the permissions granted to them. You retain your choice of AI and control over the data made available to it.
ApproachWhat does a tailored interface mean?
Workflows, screens and interactions are designed around real work. A document portal, Brand Center or operator workspace can use the same foundations. Tailored design does not mean dispensing with technical components or developing without maintenance.
Target use caseWhat is the benefit for documentation?
Structure a diverse collection, enrich its metadata, find documents and make their usage conditions clear. AI can help query the collection; answer quality depends on documentation quality and the permissions applied.
Target use caseWhat is the benefit for marketing?
A Brand Center brings content and brand guidelines together. A workflow can combine asset search, kit creation and content preparation. Human approval, brand guidelines and distribution rights remain essential.
Target use caseHow does industrial DAM fit in?
Industrial documentation links procedures, instructions, media and standards to a business context: site, workstation, product, version or effective date. The aim is to provide access to the right information at the point of use. The industrial context and required approvals must be scoped with the teams.
PrincipleHow does RAG work?
A search selects relevant excerpts from an authorised collection, then the model uses them to formulate an answer. This reduces unsupported answers but does not guarantee accuracy. Sources, versions and validation are essential.
Established capabilityWhat is the DAC’s secure MCP used for?
The DAC’s secure MCP (Model Context Protocol) is a connection point for compatible AI assistants and agents. It lets them use DAC data and knowledge within their granted permissions. This connection enables agents tailored to your use cases with your choice of AI. Accessible resources and authorised actions are defined for each integration.
Foundation and optionsHow can applications be connected?
Alveos’s existing website presents API and integration capabilities. The interfaces, versions, quotas, authentication methods and connectors actually available must be checked for your environment.
Access governanceHow are permissions respected?
The DAM has its own content governance rules. The DAC’s secure MCP controls resources accessible to assistants and agents: who can view what, and with which permissions. Connecting an AI therefore does not give it blanket access to all DAC data. For federated external sources, identity and permission mapping is defined and validated during integration.
Hosting & deployment freedomWhere is the data hosted?
Our SaaS infrastructure — storage, retrieval-augmented generation (RAG) and large language models (LLMs) — is hosted by OVH. You remain free to choose other hosting, including deployment on your own infrastructure. This flexibility is central to our approach: helping you retain control of your data and build a solution suited to your security, privacy and sovereignty requirements, even the strictest. Deployment arrangements are defined with you to address your organisation’s constraints.
Security & access controlWhat security safeguards do you offer?
Software security is central to our priorities. It also relies on control of infrastructure, authentication protocols and access permissions. We adapt our solutions to your security requirements and environment to give you as much control as possible over your data, authorised users and their permissions. Hosting, authentication and access management arrangements are defined with your teams in line with your organisation’s security policies.
To be assessedWhat does it cost?
No public price is set in this presentation. The budget depends on volumes, users, modules, integrations, interfaces, hosting and AI usage. Scoping must distinguish initial investment from recurring costs.
To be assessedHow long does a project take?
The schedule depends on collection quality, system access and scope. The proposed method starts with a use case and a representative collection, then validates usefulness before a phased rollout. No standard delivery time is promised.
ApproachHow do we get started?
Scope a priority use case and its users, audit a content sample and permissions, run a pilot, measure relevance and quality, then put validated uses into production.
Option to assessHow can documents and media be enriched?
OCR, transcription, image descriptions and suggested metadata can support indexing. Languages, formats, performance and costs must be tested. Automated descriptions must be checked against business vocabulary.
PrincipleDoes AI replace business approval?
No. AI suggests and assists. Teams remain responsible for published content, decisions and instructions. An industrial assistant must not improvise an instruction missing from its approved sources.
Portability & technological freedomCan we retrieve our content?
Exit arrangements must cover files, metadata, relationships and permissions, as well as export formats and procedures. They are defined contractually. Our open models are designed to preserve your freedom of choice and help your ecosystem evolve, without locking you into any technology, including our own.
VisionDo we need to replace our existing DAM?
Not necessarily. The DAC is designed to federate existing sources. Whether to retain, extend or replace the DAM depends on its openness, documentation quality and your needs.
Our client referencesWho are your clients?
For several decades, we have supported major clients across many sectors in lasting relationships, including Air France, Groupe ADP, Stellantis, Bolloré, Orano, the Centre des monuments nationaux, Carrefour, Campanile, Altice, Autosur and SFR.
How the assistant worksHow does this assistant work?
This assistant uses the website’s knowledge base to answer your questions about Alveos, its solutions and use cases. For each question, it finds relevant information and sends it with your request to an AI model hosted by OVH. The model formulates an answer from this context. The articles used are displayed with the answer so you can explore the topic further.
Option to assessWhat does sovereign AI mean?
Model hosting is only one element. The whole chain must be considered: data, indexes, backups, providers, access and contracts. Choosing OVH for inference alone is not enough to describe an entire solution as sovereign.
Option to assessHow can content be delivered to a CMS?
An integration can reference content and its metadata through an API or embed, depending on available capabilities. Remote reference versus copying must be chosen explicitly, then permissions, caching, updates and removals must be managed.
ApproachHow can value be measured?
Compare before and after: search time, relevant answer rate, use of valid versions and publishing effort. For AI, include errors, unanswered questions and costs. Objectives must be agreed using a representative collection.
The DAM is no longer the centre of the game
For years, major platforms have asked companies to centralise their content in a single tool, then organise their workflows around it. We believe this model is reaching its limits.
Alveos reverses the logic.
We do not ask you to move your content to make it intelligent. We connect what already exists: DAM, PIM, SharePoint, document repositories, business tools and historical content. Then we build a knowledge layer above it, capable of understanding, contextualising, connecting and exposing that information to people, applications and AI agents.
We also reject the idea that a platform should impose its cloud, interface or AI model. With Alveos, you choose where your data lives, which infrastructure hosts it, which LLM uses it and how your users access it. AI is not confined to the DAM: your content becomes securely accessible across your ecosystem.
Our ambition is therefore not to build a smarter DAM.
It is to remove the DAM as a constraint.
Storage becomes infrastructure.
Knowledge becomes the real asset.
And interfaces simply become the best ways to access that knowledge.
Alveos transforms a collection of files into open, sovereign and actionable enterprise intelligence.