SCAND offers private LLM development as a dedicated service line, and private RAG is one of its components. The company builds retrieval systems that connect language models to a client's internal knowledge sources, alongside work on data readiness, model selection, fine-tuning, enterprise integration and LLMOps.
Its deployment options include on-premise data centers, private cloud, hybrid setups and dedicated GPU hosting, and the company states that air-gapped installations can run in isolated environments without internet connectivity. Typical solutions listed on its site include enterprise knowledge assistants, private enterprise search, document processing and legal and compliance assistants. SCAND lists offices in New York and Warsaw.
Key Facts
- Best for:
- Enterprises that want a private LLM and RAG stack planned and built by one software vendor
- Core services:
- Private LLM strategy, private RAG development, fine-tuning, deployment, integration, LLMOps
- RAG focus:
- Private RAG over internal knowledge sources, on-premise and air-gapped deployment options
- Location:
- New York, USA and Warsaw, Poland
- Notable strength:
- Broad coverage from use case assessment to monitoring and governance
Deviniti provides RAG architecture implementation services that cover consulting and planning, data pipeline setup, retrieval design, integration with LLM agents and monitoring after launch. The company builds RAG chatbots, custom LLM and RAG integrations and multi-index RAG applications.
Self-hosted deployment is a named part of the service: Deviniti states that it develops and deploys RAG architecture in the client's private infrastructure, in local data centers or in cloud environments such as AWS or GCP. It also runs a separate self-hosted LLM development service and supports Bielik, an open Polish language model. Its published examples come from banking, legal contract analysis and customer service. The company is headquartered in Wrocław, Poland.
Key Facts
- Best for:
- Organizations that need RAG deployed in private infrastructure, including banking and legal teams
- Core services:
- RAG architecture implementation, RAG chatbots, self-hosted LLM development, AI agents
- RAG focus:
- Self-hosted RAG in local data centers or the client's cloud, multi-index retrieval
- Location:
- Wrocław, Poland
- Notable strength:
- Self-hosted deployment offered as a standard step of its RAG process
Sphere builds private enterprise RAG systems that connect a company's documents, business systems and policies to language models. Its RAG work includes connectors and ingestion, chunking and embedding pipelines, role-based access control, audit logging, governance and observability.
According to its site, Sphere's RAG deploys in AWS, Azure or GCP, or fully on-premise, including air-gapped environments, and can run inside the client's own infrastructure when data residency or security policy requires it. The company lists support for hosted models as well as open models such as Llama and Mistral, and data sources including SharePoint, Confluence, Salesforce, SAP and SQL databases. Industries named on the site include healthcare, insurance, banking, energy and manufacturing.
Key Facts
- Best for:
- Enterprises connecting many internal systems to a governed, private RAG deployment
- Core services:
- Enterprise RAG, agentic AI, AI governance, AI strategy, product engineering
- RAG focus:
- Private RAG with access control and audit logging, on-premise and air-gapped options
- Notable strength:
- Wide range of enterprise data connectors combined with governance features
Artezio offers custom LLM development and private deployment, with enterprise knowledge base integration through RAG as part of the service. Its work covers domain-specific training and fine-tuning, inference optimization, evaluation and benchmarking, MLOps and ongoing model maintenance.
The company describes four deployment routes: on-premise on enterprise infrastructure, private cloud in AWS, Azure or GCP, hybrid, and air-gapped systems that operate fully disconnected from the internet. Its private RAG architecture uses on-premise vector databases, and the site names open models such as Llama 3, Mistral and Falcon together with serving tools including vLLM and Text Generation Inference. Artezio lists an office in New Brunswick, New Jersey.
Key Facts
- Best for:
- Organizations that want their own fine-tuned model combined with private retrieval
- Core services:
- Custom LLM development, fine-tuning, private deployment, RAG integration, MLOps
- RAG focus:
- Private RAG architecture with on-premise vector databases, air-gapped deployment option
- Location:
- New Brunswick, New Jersey, USA
- Notable strength:
- Model training and inference optimization alongside retrieval work
Aimprosoft offers custom RAG development and private LLM development as two connected services. The RAG practice covers consulting, custom RAG applications, data preparation, RAG-based chatbots and model fine-tuning, while the private LLM practice handles readiness assessment, domain-specific models, secure deployment and monitoring.
Its private LLM service states that models stay within the client's infrastructure, with a choice of on-premise hosting, private cloud or hybrid architecture, and includes RAG integration so the model can retrieve information from connected knowledge bases. Beyond AI, the company provides product engineering, cloud and DevOps consulting and legacy modernization. Aimprosoft is headquartered in Nicosia, Cyprus, and lists representatives in several European countries.
Key Facts
- Best for:
- Companies that want a private LLM with retrieval built by a full-service engineering vendor
- Core services:
- Custom RAG development, private LLM development, AI integration, product engineering
- RAG focus:
- RAG integrated with privately hosted models, on-premise, private cloud or hybrid
- Location:
- Nicosia, Cyprus
- Notable strength:
- Separate, detailed service lines for custom RAG and for private LLMs
Folio3 AI develops generative AI and LLM solutions in which retrieval-augmented generation is a regular component, used to ground model answers in a client's documents, databases and knowledge bases. Its services also include LLM fine-tuning, AI assistants and agents, evaluation and red-teaming, guardrails and integration with CRM, ERP and legacy systems.
For deployment, the company lists cloud, private cloud and on-premise options with staged rollouts, and says models can be integrated directly or self-hosted, with private hosting for sensitive data. Its stack includes open models such as Llama, Mistral and DeepSeek, vector databases such as Qdrant, Weaviate and pgvector, and vLLM, Docker and Kubernetes for serving. Folio3 AI lists an office in Pleasanton, California.
Key Facts
- Best for:
- Businesses adding generative AI to existing CRM, ERP or legacy systems with private hosting
- Core services:
- Generative AI development, RAG, LLM fine-tuning, AI agents, evaluation, integration
- RAG focus:
- Retrieval over internal documents and databases, on-premise and self-hosted model options
- Location:
- Pleasanton, California, USA
- Notable strength:
- Integration of AI features into established business systems
Acosom is a Swiss consultancy that builds self-hosted LLM platforms connected to streaming data infrastructure. Its private LLM and real-time AI service includes RAG pipelines that pair a language model with a retrieval system, with no external API calls and no data leaving the client's infrastructure.
The company's background is in data engineering with Apache Kafka, Flink, Spark and ClickHouse, so its RAG work is aimed at data that changes continuously. Its service covers GPU hardware consulting, model selection and quantization, and model serving with tools such as vLLM and TensorRT-LLM, using open models and vector databases such as Qdrant, Milvus and pgvector. Acosom is based in Pfäffikon, Switzerland, and refers to regulatory alignment for the DACH region.
Key Facts
- Best for:
- Teams that need self-hosted RAG over real-time or streaming data
- Core services:
- Private LLM platforms, RAG pipelines, event-driven agentic AI, Kafka and Flink consulting
- RAG focus:
- Self-hosted RAG pipelines fed by streaming data, with on-premises processing
- Location:
- Pfäffikon, Switzerland
- Notable strength:
- Combination of stream processing expertise and GPU model serving
Tanθ Software Studio offers on-premise and private AI deployment services that place the full AI stack inside the client's data center, private cloud or air-gapped environment. Private RAG pipeline deployment is one of six listed services, next to private LLM serving, GPU infrastructure setup, on-premise fine-tuning, air-gapped deployment and private AI application development.
The company self-hosts open models such as Llama 3, Mistral, Mixtral, Phi-3, Qwen and Gemma, and names vLLM, Text Generation Inference, Ollama, Qdrant, Chroma, pgvector, Kubernetes and LangChain among its tools. Industries listed on its site include healthcare, banking, government and defense, legal services, pharmaceuticals and manufacturing. The company lists an office in Surat, India, along with other international locations.
Key Facts
- Best for:
- Organizations that need the infrastructure layer and the RAG pipeline deployed together
- Core services:
- Private LLM serving, GPU infrastructure setup, private RAG pipelines, on-premise fine-tuning
- RAG focus:
- Complete RAG pipelines deployed in private or network-isolated environments
- Location:
- Surat, India
- Notable strength:
- Service line dedicated specifically to on-premise and private AI deployment
10. Petronella Technology Group
Petronella Technology Group deploys private LLMs on infrastructure the client controls: GPU servers in the client's data center, dedicated single-tenant managed hosting, or a hybrid of the two. Its private LLM service includes RAG integration that connects the model to a document library, so answers come from the organization's own procedures and policies with source citations.
The company states that it deploys AI systems on air-gapped networks with no internet connectivity and works with open-weight models such as Llama 3, Mistral, Mixtral and Qwen 2.5. Private AI sits alongside its managed IT, cybersecurity and compliance services, and its site addresses defense contractors, healthcare organizations, financial institutions, law firms and government agencies. Its office is in Raleigh, North Carolina.
Key Facts
- Best for:
- Regulated US organizations that want private AI from a provider that also handles IT and security
- Core services:
- Private LLM deployment, RAG integration, fine-tuning, managed IT, cybersecurity
- RAG focus:
- RAG over internal documents on on-premise, dedicated or air-gapped infrastructure
- Location:
- Raleigh, North Carolina, USA
- Notable strength:
- Private AI delivered together with infrastructure and security operations
11. OneClick IT Consultancy
OneClick IT Consultancy provides LLM app development that includes RAG-powered applications connecting a model to live knowledge bases. Its other LLM services are custom model development, fine-tuning, integration through APIs and LangChain, and LLM agents and automation.
Private LLM deployment is a separate service in which the model runs entirely on the client's own infrastructure. The company lists VPC and air-gapped on-premise servers as target environments, with tools such as Ollama, vLLM, Docker, Kubernetes and NVIDIA NIM, and vector databases including Qdrant, Weaviate, Milvus and pgvector. Industries on its site include healthcare, legal, finance and insurance, ecommerce and logistics. OneClick lists offices in Ahmedabad, India and London, UK.
Key Facts
- Best for:
- Companies that want an LLM application and its private deployment from the same team
- Core services:
- LLM app development, RAG applications, fine-tuning, private LLM deployment, LLM agents
- RAG focus:
- RAG applications over live knowledge bases, deployable in a VPC or on air-gapped servers
- Location:
- Ahmedabad, India and London, UK
- Notable strength:
- Wide choice of frameworks and vector databases for RAG builds
Acquaint Softech offers LLM development services that include RAG pipelines built with chunking, embeddings, hybrid search and re-ranking over a client's documents. The same service line covers model selection, custom LLM applications, fine-tuning, agents with tool calling, evaluation and guardrails, and LLMOps.
Its private and self-hosted LLM service deploys in the client's Azure or AWS account or on the client's own servers, with self-hosted Llama listed as a private hosting option. For retrieval the company names MongoDB Atlas Vector Search, Pinecone, pgvector and Weaviate, with LangChain and LlamaIndex for orchestration and Python, Node.js and Laravel on the backend. Its head office is in Ahmedabad, India, with further offices in the USA, UK, Canada and New Zealand.
Key Facts
- Best for:
- Product teams that need RAG and a self-hosted model added to a web or mobile application
- Core services:
- LLM consulting, custom LLM apps, RAG pipelines, fine-tuning, private and self-hosted LLMs
- RAG focus:
- Hybrid search and re-ranking pipelines, deployable on the client's own servers
- Location:
- Ahmedabad, India
- Notable strength:
- Full application stack around the LLM, including evaluation and cost optimization
How to choose an on-premise RAG development company
Start by writing down what on-premise means in your case. A private cloud account with a model behind a private endpoint, GPU servers in your own data center and a network with no outside connection are three different projects with different costs. A vendor that is strong in one may have little to show in another, so ask for the deployment model you need by name and ask which parts of the pipeline they have run inside it.
Then look at the two things that decide whether the system is useful after installation: answer quality on a locally hosted model, and who operates the system once the vendor leaves. Open-weight models behave differently from large hosted ones, so retrieval quality and evaluation matter more, not less. These questions help compare vendors:
- Which components run inside our boundary: document parsing and OCR, embeddings, reranking, the vector index, the language model, logging? Does any of them call an outside service?
- Which open-weight models do you recommend for our languages and document types, and what GPU hardware do they need at our expected number of users?
- How do you measure retrieval and answer quality on a locally hosted model, and will we get a test set built from our own questions?
- How are source-system permissions carried into the index, so that a user only gets answers from documents they are allowed to open?
- How are model, index and software updates delivered into a restricted or disconnected network, and who validates them before release?
- What do our engineers receive at handover: code, infrastructure definitions, runbooks and monitoring, and what does ongoing support look like?
Conclusion
On-premise RAG is as much an infrastructure decision as an AI one. The companies in this list range from software development firms that add private deployment to a custom RAG build, to infrastructure-focused providers that install GPU servers and model serving and then connect retrieval on top. Which type fits depends on whether your harder problem is the application around the answers or the environment it has to run in.
Before choosing, agree internally on the deployment boundary, the documents in scope and how a correct answer will be judged. With those three points fixed, vendor proposals become comparable, and a short paid discovery or pilot on a sample of real documents will show more than any capability list.
