Every team builds its own AI layer
Projects choose vendors, SDKs, and models independently. Duplicated integrations and inconsistent standards soon make the architecture difficult to maintain.
SOLUTIONS / ENTERPRISE
Bring AIacross yourenterprise.
From a European platform, deploy private and open-weight models, connect corporate data, and run AI workloads with European sovereignty, privacy and control over the region.
FROM PILOT TO PRODUCTION
AI scales. So does the complexity.
Once AI expands beyond a single pilot, connecting an API is no longer enough. Models, data, security, cost, regions, and providers need to work together without slowing down the teams building with them.
Corporate data cannot move without control
Documents, contracts, and internal knowledge need traceability, isolation, and clear rules around which model can use them and where they are processed.
Cost is split across tokens, GPUs, and tools
Spend is scattered across providers and teams. Without a shared view, comparing cost, capacity, and performance across applications becomes slow and unreliable.
Changing models means rebuilding integrations
The enterprise needs to test new LLMs and specialized models without rewriting every application or depending on a single vendor's catalog.
Security and residency must be designed from the start
Once AI processes real information, regions, access controls, and protection against misuse can no longer be treated as later additions.
Operating models consumes platform teams
GPU capacity, runtimes, endpoints, routing, and availability need continuous attention. Solving them one deployment at a time delays production.
QDivZero brings models, GPU compute, deployments, data, routing, and security into one platform. Teams can move from pilot to production without adding a different tool for every model, application, or provider.
ONE AI PLATFORM FOR THE ENTERPRISE
Deploy, connect, and govern AI from one shared foundation.
01MODELS
Choose the model that fits each workload.
Start with QDivZero Public Models, explore open-weight models from Hugging Face, or deploy your own private and fine-tuned models, without limiting every application to one catalog.
Explore models02DEPLOYMENTS
Deploy models without turning GPUs into another project.
Choose an open-weight or custom model. QDivZero identifies its requirements, finds compatible compute across providers, prepares the inference environment, and delivers an endpoint ready for your application.
Explore model deployment03COST
Use the right payment model for each workload.
Combine balance and Unlimited Basic in one account. Public Models use balance by token, dedicated deployments by active compute time, and Unlimited Basic provides unlimited Qwen 3.8 27B, subject to fair use, plus 5,000 shared monthly AI Credits for GLM and DeepSeek for a fixed monthly fee without using balance.
Explore AI pricing04ROUTING
Send every request to the right model.
LLM Model Routing distributes requests across multiple models and lets you set priorities and fallbacks based on cost, latency, quality, and availability.
Discover LLM Model Routing05DATA
Connect models to your enterprise knowledge.
Build enterprise RAG with embeddings, semantic search, and retrieval to retrieve context from documents, catalogs, and knowledge bases during inference.
Explore RAG and retrieval06SECURITY & COMPLIANCE
Security and compliance for enterprise workloads.
European sovereignty and European data protection standards for applications subject to GDPR. Encryption with HTTPS/TLS, zero logs and no data sent to model creators’ APIs, in European regions and other available regions.
Explore security and complianceCENTRAL CONTROL, AUTONOMOUS TEAMS
Define the rules once. Let every team move forward.
Centralize the decisions that affect the whole organization—regions, providers, data, policies, and budgets—without turning every new use case into a request for the infrastructure team.
YOUR ENTERPRISE DEFINES
- Regions
- Providers
- Data access
- Policies
- Budgets
QDIVZERO OPERATES
- Capacity
- Runtime
- Deployment
- Routing
- Safeguards
TEAMS RECEIVE
- One shared API
- Ready endpoints
- Approved models
- Stable access
- Less operational work
ENTERPRISE AI USE CASES
One platform. Many production AI use cases.
Build everything from internal assistants to customer-facing applications on shared AI infrastructure with specialized models, enterprise data, and an OpenAI-compatible API.
Turn internal knowledge into useful answers
Give every team fast access to policies, processes, and company documentation through RAG assistants that answer with verifiable sources.
Enterprise RAG · knowledge management · internal assistants
Reduce manual document work
Extract data and classify contracts, invoices, forms, PDFs, and images to speed up operations and reduce repetitive work.
Document AI · document automation · data extraction
Resolve more requests without overwhelming support
Build agents that retrieve information, use tools, and complete tasks to deliver faster, more consistent customer responses.
AI customer service · agents · automation
Improve productivity across every team
Connect copilots to the tools and data used by sales, operations, finance, and support to streamline everyday work.
Enterprise copilots · productivity · workflows
Accelerate development without multiplying licenses
Give engineering shared models for generating, reviewing, and refactoring code without a separate subscription for every tool or developer.
AI for developers · code models · cost control
Help people find what matters sooner
Improve how customers and employees find products, content, and documents with semantic results and more relevant recommendations.
Semantic search · discovery · recommendations
Scale content creation and review
Analyze and generate text, image, video, and audio on shared infrastructure for marketing, catalogs, moderation, and operations.
Multimodal AI · content generation · moderation
Turn every conversation into an opportunity
Transcribe and analyze calls, assist agents, and automate voice responses to improve service, quality, and follow-up.
AI contact center · call analytics · voice AI
Keep sensitive data under control
Run private and fine-tuned models on dedicated compute in available European regions, with encryption in transit and control over processing location for workloads subject to GDPR.
GDPR · data residency · private AI
Choose how to get started.
THREE WAYS TO OPERATE AI
The flexibility of an internal platform, without having to build it.
QDivZero combines model choice, multi-provider compute, and an OpenAI-compatible API so teams can move faster without taking on the full operational burden.
#Frequently asked questions about enterprise AI platforms
Answers about deploying private and open-weight models, enterprise RAG, GPU infrastructure, routing, security, cost, and OpenAI compatibility.
What should an enterprise AI platform include?+
It should cover the full lifecycle: model access, GPU compute, runtimes, deployments, endpoints, routing, data connections, and security. QDivZero brings these capabilities together so multiple teams can move AI applications into production on one shared foundation.
How do you deploy open-weight Hugging Face models in production?+
Choose the model and define the region and workload requirements. QDivZero checks compatibility, finds capacity across providers, and prepares the inference environment. Your application then connects through an OpenAI-compatible endpoint.
Can QDivZero run private or fine-tuned models?+
Yes. You can deploy proprietary, private, and fine-tuned models on dedicated compute. QDivZero prepares the runtime and infrastructure required to serve them without your team manually operating GPU servers.
How do you build enterprise RAG with corporate data?+
Enterprise RAG turns documents and internal content into embeddings, indexes the information, and retrieves relevant context before generating an answer. QDivZero lets you combine embeddings, semantic search, retrieval, and LLMs in one platform.
How can an enterprise control data residency and inference regions?+
QDivZero offers European regions and other available regions. Choose where each deployment runs to match your residency requirements. The platform is developed and operated by a Spanish company under the European data protection framework. In every region, we maintain zero logs of inference content and do not send your data to model providers. Unlimited runs in Europe.
Can QDivZero be used for applications subject to GDPR?+
Yes. You can run models in European regions, control where data is processed, and keep prompt and response content outside QDivZero storage. Communications use encryption in transit, and your data is not used to train models. Final GDPR compliance also depends on how your application collects, uses, and manages personal data.
How do you protect enterprise AI applications from prompt injection and jailbreaks?+
LLM Firewall analyzes requests and responses for prompt injection, jailbreaks, sensitive data, abuse, and disallowed content, then applies the policy defined by your organization.
How can an enterprise avoid AI vendor lock-in?+
Keep an access layer independent from any single vendor. Open-weight models, multi-provider compute, and an OpenAI-compatible API let teams test alternatives or change infrastructure without rewriting every application.
How do you control AI inference cost at enterprise scale?+
Combine payment methods by workload. Public Models use balance by token, dedicated deployments by active compute time, and Unlimited Basic provides unlimited Qwen 3.8 27B, subject to fair use, plus 5,000 shared monthly AI Credits for GLM and DeepSeek for a fixed monthly fee without using balance. LLM Model Routing can also direct each task based on cost, latency, quality, or availability.
Can an enterprise use multiple LLMs in one application?+
Yes. LLM Model Routing places several models behind one shared layer and distributes requests by intent, priority, or availability. Your application keeps one access point instead of integrating every destination separately.
Is QDivZero compatible with the OpenAI API?+
Yes. Endpoints use an OpenAI-compatible format. Teams keep a familiar integration while changing the model or infrastructure that responds behind it.
What enterprise AI use cases can teams build?+
Teams can build RAG assistants, intelligent document processing, customer service agents, internal copilots, semantic search, recommendations, and multimodal or voice applications.
How can multiple teams share an AI platform without losing control?+
The enterprise defines regions, providers, data access, policies, and budgets. QDivZero operates the shared infrastructure, and each team works with approved models, ready endpoints, and a stable API.
Let's talk about your enterprise AI infrastructure.
Tell us which models, data and regions your enterprise needs. Build an AI foundation with European sovereignty, privacy and zero logs, without sending your data to model providers, including when deploying outside Europe.