Platform
Alongside custom-built systems, some of the expertise we develop on projects goes into a ready-to-use product, which each client configures on their own document base.
MyGPT or a custom-built system: a first orientation
These are not equivalent alternatives: they answer different needs. This is a starting point, not a decision — that gets made in the Assessment, on the specific case.
Sovereign AI, ready to use: data and model both stay inside your perimeter
MyGPT
MyGPT makes your organization's entire document archive queryable in natural language, with every answer anchored to your real documents and the source always cited — without waiting on a custom-built project's timeline. It's Futura AI's proprietary platform: designed, developed and owned in-house, not a product resold under license from a third party. Same multi-tenant RAG architecture for every client, configured on your document base instead of rebuilt from scratch, with the same source-traceability logic we apply on custom projects.
Why an internal system, not an external chatbot
The model runs inside your perimeter
Not just the documents: inference itself runs on self-hosted open-source models, on dedicated or client infrastructure. No request ever leaves the organization's perimeter to be processed by a third-party API — that's the difference between a private AI and a sovereign one.
Company knowledge never travels elsewhere
Every question, every document consulted, every answer generated stays on infrastructure you directly see and control — not on a generalist cloud service whose logs, policies or jurisdiction you have no visibility into.
You're not buying intelligence, you're building it
Calling a generalist model's API isn't adopting AI in your organization: it's renting someone else's intelligence, with one more reseller layer between you and the model. A system configured on your documents, integrated into your processes and under your control becomes a capability of the organization — not just another response paid for by the call.
No dependency on an external model provider
A generalist AI provider can change terms of use, pricing, service availability or data policies without the organization having any say. A sovereign architecture depends on none of those decisions.
A requirement, not a preference, in regulated contexts
For public administration, banks, insurers and critical infrastructure, data and model sovereignty isn't an optional technical choice: it's often the constraint a generalist public-cloud AI cannot meet.
What it does
Automated document search
Query the organization's document archive in natural language, without needing to know in advance where to look.
Answers grounded in company data
Answers are based on the document base the client configures, not on the model's general knowledge alone.
Multi-format document ingestion
An OCR pipeline that reads native and scanned PDFs, images (TIFF, JPEG, PNG) and the main Office formats, with pre-processing for lower-quality scans before indexing.
Customization
Tailored configuration for each organization's data base, use cases and specific constraints.
Ready-made connectors to systems already in use
SharePoint, Amazon S3 buckets, Google Drive and relational databases: connects to existing document sources without duplicating archives.
Multi-tenant, data-governance-first architecture
Each client in an isolated tenant from the architecture design stage: data segregation isn’t a layer added on top of a shared base, it’s the first requirement the system was designed around.
Aligned with AI Act requirements
Risk classification, technical documentation and transparency requirements handled from the platform’s design stage onward — the same discipline we apply on custom projects, not a blanket compliance claim: it depends on how each client actually uses it.
Deployment
Private cloud
The default deployment model: dedicated cloud infrastructure, with a private company data base kept separate from other clients', and a self-hosted model — no call to an external API to generate answers.
On-premise
For organizations with data-residency constraints that rule out the cloud: both data and language model run on the client's own infrastructure.
Multi-tenant architecture with per-client data isolation: each client's data and requests are not accessible to other clients and are not used to train the base models — the same data-governance rule we apply on custom projects.
Organizations of any size that manage large volumes of documents and want a natural-language entry point, without the development time of a custom-built system.