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NVIDIA Public Sector France

French Ministry of Economy and Finance

Document Processing · Fraud Detection · Security and Compliance

French public servants needed to answer citizen requests against more than 100 million documents per year while maintaining legal accuracy, security, confidentiality, and compliance, but manual retrieval and analysis of PDFs, scans, and other records was too slow and costly.

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1,000 work hours saved per month for a typical 100-user deployment
Document retrieval improved from two days to two minutes
More than 100 million documents handled annually
One week to set up DeepBrain and configure connectors

Reported impact

Business outcome

DeepBrain cut document retrieval from two days to two minutes, saved a typical 100-user deployment about 1,000 work hours per month, and helped the Ministry of Finance save €2 million when deployed at scale for 10,000 employees, while requiring only about one week to set up and connect.

  • Compliance
  • Cost Reduction
  • Productivity Improvement

Solution

ThinkDeep and NVIDIA built DeepBrain as an on-prem, multi-agent document and knowledge platform for the French administration. The system connects to many data sources, ingests millions of PDFs, scanned images, schemas, and videos, and builds domain-specific knowledge bases so public servants can cross-reference legal and operational information securely. On the infrastructure side, DeepBrain uses NVIDIA AI Enterprise with H100 and H200 GPU resources plus the NVIDIA DGX platform to keep data on customer-controlled infrastructure. NVIDIA NIM packages and serves the latest LLMs on premises, NVIDIA NeMo Retriever grounds agents in current documentation, NVIDIA NeMo Guardrails enforces safer behavior, and NVIDIA Dynamo helps optimize inference speed for advanced reasoning models. That stack allows ministries to run fraud-detection, legal-document, and cross-domain assistants that return sourced answers in minutes instead of days while preserving sovereignty over highly sensitive data.

Data flow

Government records from PDFs, scans, schemas, videos, and other internal sources flow into DeepBrain knowledge bases, NIM-served models and NeMo Retriever agents analyze and cross-reference them, Guardrails constrain the outputs, and public servants receive sourced answers or fraud-detection insights on ministry-controlled infrastructure.

Solution architecture

7 components · 5 layers
  1. Ingest
    • NVIDIA NeMo Retriever Grounds DeepBrain agents in current ministry documents and knowledge bases.
  2. Compute
    • NVIDIA DGX platform Provides the on-prem GPU infrastructure required to serve large user populations securely.
  3. Serving
    • DeepBrain Provides the multi-agent assistant layer that builds domain knowledge bases and serves ministry workflows.
    • NVIDIA NIM Packages and deploys the latest LLMs on premises for DeepBrain agents.
    • NVIDIA Dynamo Optimizes inference speed for advanced reasoning models used in the agent workflows.
  4. Orchestration
    • NVIDIA AI Enterprise Supplies the software platform for building and operating DeepBrain on customer-controlled infrastructure.
  5. Governance
    • NVIDIA NeMo Guardrails Applies safety controls to reduce biased or harmful outputs in regulated workflows.

Architecture clues

  • DeepBrain builds domain-specific knowledge bases and a graph methodology for searching across millions of files and relationships.
  • The deployment keeps data on customer infrastructure at all times to meet security and sovereignty requirements.
  • The platform was designed to satisfy confidentiality needs and compliance with the European AI Act.

Evidence from the source

Customers were able to automate the document retrieval process, improving from two days to two minutes.
The DeepBrain agent’s unique data confidentiality approach guarantees the data stays on the customer infrastructure at all times.
The French administration annually handles over 100 million documents to provide tailored responses to citizen inquiries.
The Ministry of Finance has saved €2 million by deploying DeepBrain at scale for 10,000 employees.
Together, ThinkDeep and NVIDIA worked to develop DeepBrain AI agents, capable of processing information from millions of PDF documents, scanned images, schemas, and videos.
Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. NVIDIA

    French Ministry of Economy and Finance customer story

    First-party
    https://www.nvidia.com/en-us/case-studies/thinkdeep-sovereign-ai-agents-automate-public-services/
    Source type
    Vendor-published customer story
    Published
    Jul 7, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Jul 7, 2026 · NVIDIA · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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