Assessment & Design
We map workflows, data sources and compliance needs. Identify the best AI approach for your business processes.
Custom local LLM packages (vector DB + RAG + agents + connectors & MCPs) installed on-premise or on enterprise NVIDIA hardware. Keep sensitive data inside your network while boosting productivity across teams.
The business case for secure, local AI deployment
Your documents, customer records and IP never leave your network. Full data sovereignty and compliance.
Avoid per-call API bills and keep variable costs low as usage grows across your organization.
Lower latency for internal workflows and full control of uptime and compliance requirements.
SMEs and mid-market companies need secure, governable deployments to convert pilots into real business value.
No vendor lock-in. Choose any open-source model, customize freely, and control updates on your schedule without external dependencies.
Meet industry-specific requirements (GDPR, HIPAA, NIS2) with data residency controls and full audit trails built-in.
Complete on-premise AI platform with all components
Choice & customization of open-source LLM matched to your use case. We pick the best model for accuracy and efficiency.
Private, up-to-date knowledge access with retrieval layer for your documents and data sources.
Connectors to CRM, ERP, file shares, ticketing systems. Handles complex workflows and tool invocations.
Install on commodity servers or NVIDIA DGX-class appliances with full networking and access controls.
Optional targeted fine-tuning or prompt-engineering where needed — only if it's the best path for your use case.
Documentation, admin UI and comprehensive training for your staff to manage and operate the system.
Our three-step process to deployment
We map workflows, data sources and compliance needs. Identify the best AI approach for your business processes.
We assemble model + vector DB + RAG + connectors, run a pilot with real company data and validate performance.
On-prem installation with access controls, monitoring, backup systems, and optional managed updates.
Components: Open LLM runtime (containerized), Vector DB for embeddings and retrieval, RAG orchestration layer for knowledge injection, Agent/chain manager for workflows, Connectors and MCPs to internal systems with access controls, Monitoring and audit trails.
Data never leaves your network unless you choose otherwise. Air-gapped deployment options available for maximum security.
Audit logs, role-based access control, and optional hardware encryption (HSM/TME) for sensitive operations.
Support for GDPR-friendly data handling and residency requirements with full audit trails.
Enterprise auth integration (LDAP/AD SSO) and comprehensive monitoring dashboards.
Tailorable deployment options for every business size
Model selection + one connector + pilot dataset. Deliverable: working pilot in your environment with performance metrics.
Full vector DB, RAG, 3 connectors, admin UI, SLA and 6-month support. Complete production-ready deployment.
Validated DGX deployment, HSM, advanced agents, on-site install and 12-month managed service with dedicated support.
Assessment: 1–2 weeks
Pilot build: 3–6 weeks
Production roll-out: 2–6 weeks after pilot approval
On-premise AI refers to deploying language models and AI systems directly in your own infrastructure or data centers, rather than relying on external cloud providers. This gives you full control over your data, ensures it never leaves your network, enables customization for your specific workflows, and provides predictable costs as usage scales. Cloud APIs work well for low-volume needs, but on-premise becomes cost-effective and essential for organizations with sensitive data, compliance requirements, or high-volume internal use.
Often not. Retrieval-augmented generation (RAG) combined with proper prompt engineering typically delivers excellent accuracy without expensive re-training. We use your own documents and data as context to make the model give relevant, domain-specific answers. Fine-tuning is only recommended when the use case truly requires it and provides clear ROI—for example, specialized terminology or very niche tasks.
For low-volume projects, cloud APIs are fast to start. For sustained internal usage—especially across multiple teams—on-premise significantly reduces variable spend and improves cost predictability. Initial hardware investment is offset by lower per-query costs over time. We help you analyze the total cost of ownership specific to your expected usage patterns and compliance needs.
We work with the full ecosystem of open-source LLMs: Llama 2/3, Mistral, Nous Hermes, and others. We evaluate and recommend the best model for your specific accuracy, latency, and resource requirements. Smaller models (7B–13B parameters) work well for most business tasks on commodity hardware, while larger models deliver higher quality on very complex reasoning tasks but require more computational resources.
Assessment and requirements gathering: 1–2 weeks. Building and piloting the system with your data: 3–6 weeks. Production roll-out and staff training: 2–6 weeks after pilot approval. The timeline depends on the complexity of your workflows, data sources, and required integrations. We provide milestone visibility and can accelerate high-priority implementations.
All data stays in your network unless you explicitly choose otherwise. Air-gapped (offline) options are available. We implement role-based access control, comprehensive audit logs, and optional hardware encryption. We support GDPR, HIPAA, NIS2, and other regulatory requirements with data residency controls built in. Your data never touches our infrastructure.
Yes. We build connectors to your existing tools—Salesforce, Microsoft Dynamics, SAP, file shares, ticketing systems, and custom databases. The AI system can read from these sources, process information, and take actions autonomously or with human approval, seamlessly fitting into your existing workflow.
We recommend commodity servers (CPU-based) for most tasks, or NVIDIA DGX/GPU systems for very large models or high-volume inference. We provide hardware sizing recommendations based on your model choice and expected load. We can also work with your IT team to validate architecture before purchase, ensuring you invest in the right infrastructure.
Book a free 30-minute technical assessment with our AI specialists.
or email info@shambix.com