Private AI and cloud. Under your control.
On-premise and private LLM packages (vector DB + RAG), plus AWS cloud architecture and CI/CD when your product needs solid infrastructure. Keep sensitive data inside your network while shipping reliable systems.
Why Cloud and Private AI?
The business case for secure private AI and reliable cloud
Protect Sensitive Data
Your documents, customer records and IP never leave your network. Full data sovereignty and compliance.
Predictable Costs
Avoid per-call API bills and keep variable costs low as usage grows across your organization.
Faster, Reliable Performance
Lower latency for internal workflows and full control of uptime and compliance requirements.
Practical ROI
SMEs and mid-market companies need secure, governable deployments to convert pilots into real business value.
Complete Control
No vendor lock-in. Choose any open-source model, customize freely, and control updates on your schedule without external dependencies.
Regulatory Compliance
Meet industry-specific requirements (GDPR, HIPAA, NIS2) with data residency controls and full audit trails built-in.
What We Deliver
Private AI platform components plus cloud and DevOps when you need both
Custom LLM Selection
Choice & customization of open-source LLM matched to your use case. We pick the best model for accuracy and efficiency.
Vector Database + RAG
Private, up-to-date knowledge access with retrieval layer for your documents and data sources.
System Connectors
Connectors to CRM, ERP, file shares, and ticketing so private AI can read and act inside your existing tools.
Secure Deployment
Install on commodity servers or NVIDIA DGX-class appliances with full networking and access controls.
Fine-Tuning and Optimization
Optional targeted fine-tuning or prompt engineering where needed, only if it is the best path for your use case.
Cloud and DevOps
AWS architecture, CI/CD, containers, and cost control when private AI sits next to production apps that must stay up.
Complete Handover
Documentation, admin UI and comprehensive training for your staff to manage and operate the system.
How It Works
Our three-step process to deployment
Assessment & Design
We map workflows, data sources and compliance needs. Identify the best AI approach for your business processes.
Build & Pilot
We assemble model + vector DB + RAG + connectors, run a pilot with real company data and validate performance.
Deploy & Operate
On-prem installation with access controls, monitoring, backup systems, and optional managed updates.
Technical Architecture
Private AI: open LLM runtime (containerized), Vector DB for embeddings and retrieval, RAG orchestration, connectors to internal systems with access controls, monitoring and audit trails. Cloud: AWS architecture, CI/CD, containers, infrastructure-as-code, and cost control. For agent pipelines and custom MCP tooling, see AI Agents and Automation.
Security & Compliance
Network Isolation
Data never leaves your network unless you choose otherwise. Air-gapped deployment options available for maximum security.
Access Control & Encryption
Audit logs, role-based access control, and optional hardware encryption (HSM/TME) for sensitive operations.
Regulatory Compliance
Support for GDPR-friendly data handling and residency requirements with full audit trails.
Enterprise Integration
Enterprise auth integration (LDAP/AD SSO) and comprehensive monitoring dashboards.
Typical Timeline & Next Steps
- Assessment1-2 weeks
- Pilot build3-6 weeks
- Production roll-out2-6 weeks after pilot approval
Sample Packages
Tailorable deployment options for private AI and cloud workloads
Starter (Small Pilot)
Model selection + one connector + pilot dataset. Deliverable: working pilot in your environment with performance metrics.
Discuss this packageBusiness (Production)
Full vector DB, RAG, 3 connectors, admin UI, SLA and 6-month support. Complete production-ready deployment.
Discuss this packageEnterprise (DGX/Airgapped)
Validated DGX deployment, HSM, hardened connectors, on-site install and 12-month managed service with dedicated support.
Discuss this packageFrequently Asked Questions
What is on-premise AI and how does it differ from cloud APIs?
On-premise AI runs models in your infrastructure so data stays in your network. Cloud APIs are fine for low-volume public tasks; private AI fits sensitive or high-volume internal use.
Do you also handle AWS cloud and DevOps?
Yes. We deliver private LLM/RAG systems and the cloud architecture around them (CI/CD, cost control, reliability) when your product needs both.
Do you need to fine-tune models for our domain?
Often not. RAG plus good prompts usually wins. Fine-tuning only when ROI is clear.
What about ongoing costs vs cloud APIs?
For sustained internal usage, on-premise often improves cost predictability. We help you compare total cost for your load and compliance needs.
Can you integrate with CRM, ERP, and file systems?
Yes. Connectors with access controls so the system reads and acts inside your existing tools.
Looking for agent pipelines or MCP tooling instead?
See AI Agents and Automation (/ai-agents/) for agent pipelines and MCP servers. This page covers private AI platforms and cloud infrastructure; many projects combine both.
Do you offer RAG development services?
Yes. RAG (retrieval-augmented generation) development is the core of most private AI projects we deliver: document ingestion, a vector database, retrieval tuned to your content and access controls, so answers come from your own data.
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Ready for private AI or solid cloud?
Tell us about data sensitivity, volume, and whether you need private LLM, cloud infra, or both.
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- If we are not the right fit, we say so
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