Enterprise AI ServicesOn-Premise Deployment & Custom Solutions
End-to-end AI implementation with security at the core. From infrastructure to deployment, every solution is designed for enterprise compliance.
Frequently Asked Questions
We deploy all AI infrastructure directly on your hardware or private cloud — no data ever leaves your environment. Unlike cloud AI providers, there are zero ongoing per-token API costs and no vendor lock-in. Deployments are GDPR-aligned and SOC 2-ready by design, with architecture suited for HIPAA, DORA, NIS2, EU AI Act, and internal data governance policies. You own the models, the weights, and the compute.
Deployment timelines depend on scope and integration complexity. A focused RAG chatbot or document intelligence pipeline can be production-ready in 4–6 weeks, covering infrastructure setup, model deployment, document ingestion, and initial testing. A custom AI agent system integrated with ERP, DMS, or CRM platforms typically takes 8–12 weeks, including multi-system integration, RBAC configuration, user acceptance testing, and staff handover. Full multi-agent deployments with custom model fine-tuning can extend to 14–16 weeks. We provide a detailed timeline estimate at the proposal stage after scoping your specific requirements.
Yes, we design every solution around your existing infrastructure rather than requiring you to rebuild it. We deploy on bare-metal servers, VMware clusters, private OpenStack environments, and Kubernetes clusters. We integrate with your existing identity providers (Active Directory, LDAP, SAML 2.0, OAuth) so no separate credential management is required. For enterprise system connectivity, we build bidirectional integrations with SAP, Oracle ERP, Microsoft SharePoint, Salesforce, and custom legacy systems via REST and gRPC APIs. GPU or CPU-only deployments are both supported depending on workload requirements.
Yes. We fine-tune open-source foundation models including Llama, Mistral, Qwen, Phi, and Gemma variants using LoRA and QLoRA techniques on your proprietary datasets, entirely within your infrastructure. Your training data never leaves your environment at any stage. Fine-tuning allows the model to develop domain-specific language understanding, follow your internal terminology, and perform significantly better on your specific use cases. We also set up continuous fine-tuning pipelines so the model can be updated as your data evolves, without requiring a full retraining cycle each time.