Make your company think.

You also use DBRAINS by talking to it, by voice or in writing. The difference is in what sits behind it. A general assistant answers the questions you ask, inside the documents you hand it, and every conversation starts from nothing.

DBRAINS knows your company department by department, through separate Brains, each with its own permissions: the HR Brain is not exposed to Sales, and Legal does not surface inside a commercial answer. When it relates several Brains and notices something that comes from the crossover, it flags it to you even if you never asked. And when what it notices points at a process that costs more than it returns, that process gets automated inside the tools already in use.

That is the practical difference: an assistant answers, DBRAINS notices.

For the one request you have in mind, probably yes. Reading and indexing documents is a commodity now, and on that front the technology gap between solutions has closed.

The point is elsewhere: that result does not exist in your company today. Not because the technology is missing, but because nobody has the time to structure the data, define who sees what, keep the knowledge current, and turn the analysis into processes that actually run. That is the work we do, specialised on your sector.

The value is not in the underlying model. It is in knowledge that becomes usable without your team having to build or maintain anything.

Because we guarantee sovereignty too, and it is not a differentiator: it is the minimum condition any serious European buyer expects. European infrastructure, data that stays in the Union, and no training of public or third-party models on your content.

The useful question comes after that: what gets built on top. That is where separate Brains with their own permissions, specialisation on your sector, and cross-department conclusions make the difference, the layer that no competitor in our analysis covers explicitly.

The first number arrives before implementation, not after. In the Proof of Value we map a real process, measure what it costs today in hours, and calculate the return of automating it. It takes a few days of work, and the output is a document with times, volumes and payback that stays yours even if you stop there.

On implementation, the manufacturing case we publish took six weeks from kickoff to a working system, in production from the second month.

Cross-department conclusions come later, once several Brains are live and have built up enough history. That part matures over time, and we would rather say so upfront.

Less than people expect. We need:

• one person who runs the process by hand today, for a couple of mapping sessions;
• the material that already exists, however messy: catalogues, templates, past quotes, procedures;
• access to the tools in use, for the connectors.

No preliminary data project, no data warehouse, no system logs to clean up first. The process is reconstructed by interviewing the people who run it, which is the practical difference from enterprise process mining, where everything starts from structured logs an SME rarely has.

No, and not having one is not a reason to rule you out. Connectors attach to existing tools through MCP where it exists, REST APIs and webhooks where it does not. Where there is nothing to attach to, we activate a vertical app that covers the whole process.

The assessment phase includes training on two levels, one for the people who will query the Brains daily and one for the people who need to understand what is happening and decide. If there is an IT team they work with us on access and integrations, and if there isn't, we handle it.

On European infrastructure, isolated per organisation. Process data does not leave the Union and is never used to train public or third-party models.

Every operation lands in a readable log, actions that affect the outside world go through human confirmation, and deletion and portability stay under your control. GDPR and the AI Act are part of the architecture, not a compliance layer bolted on at the end.

We do not cite certifications we have not obtained yet. When we have them, you will find them written here.

Permissions are defined per Brain, not document by document: each domain has its own rules on who can query it, who can feed it, and who does not see it at all.

When several Brains are related, the conclusion stays inside the boundary of whoever is asking. If a profile has no access to the HR Brain, no answer hands them its contents indirectly. Every query is logged and cites the sources behind the answer, so a conclusion can always be verified by tracing it back to the documents it came from.

It is the right risk to raise, and the architecture is built to avoid it. You can use frontier models, which is the default, open source models hosted on European infrastructure, or a dedicated model trained on your own historical data, which stays yours.

Knowledge does not live inside the model. It lives in the Brains, with their relationships and their permissions. Swapping engines means changing the part that processes, not rebuilding the knowledge itself. That is why the choice stays reversible as the model market moves.

With four indicators agreed before we start, so the review at the end is a comparison rather than an impression:

• time to the first measurable result;
• hours returned on the chosen process, against the baseline from the mapping;
• real adoption, meaning how much of the team actually queries its Brain;
• the number and quality of cross-department conclusions actually used to decide.

The last one is the hardest to measure and the most important, because it is the only one that says whether the platform has moved from a search tool to a decision tool.

That is exactly why we do not ask for a migration. Connectors bring knowledge into the CRM, ERP, mail and drive already in use, and what happens there flows back into the Brains without manual work. For most people the change is that the tools they always used start answering better.

Where a new environment genuinely helps, such as preparing a quote or screening CVs, we activate a vertical app for that process, used by the people who run it rather than by the whole company.

Adoption is one of the four indicators we measure, precisely because it is where projects stall most often.

Three items: a platform subscription, AI usage in credits, and professional services for assessment, implementation and evolution. The subscription depends on scope, meaning how many Brains are live and how many people use them, and credits follow actual usage, so a department that queries rarely costs little.

We do not publish a price list, because the number would be fictional: sizing comes out of the Proof of Value, which also produces the return estimate on the chosen process. In the manufacturing case we publish, Smart Quoting, estimated payback is about five months: a number for that scope rather than a transferable promise.