Build enterprise AI you can trust, explain, and defend.
Open-source large language models have reached a level of capability where they can match or exceed cloud-hosted alternatives for most enterprise use cases. The difference is that with Aigentcy, you deploy them entirely within your own infrastructure — on-premise or on a private cloud — so your sensitive data never leaves your environment.
We assess your use case, select the right base model (Llama, Mistral, Phi, or others), fine-tune it on your domain-specific data, and deploy it with the retrieval-augmented generation (RAG) pipelines, APIs, and monitoring infrastructure your teams need to put it to work immediately.
- AI risk assessment & classification
- Regulatory mapping (EU AI Act, ISO 42001)
- Model audit trails & explainability
- Board-level AI reporting dashboards
- Vendor AI due diligence
- Responsible AI policy development
What Our Private AI Engagement Delivers
Aigentcy does not hand you a model and leave. We architect the full stack — from GPU infrastructure and model serving to the RAG pipelines that connect the model to your proprietary knowledge bases, and the user-facing applications your teams interact with daily. You get a production-ready AI system, not a prototype.
Domain-Specific Performance
Fine-tuned on your documents, processes, and terminology, the deployed model outperforms generic cloud alternatives on your specific tasks — with significantly lower hallucination rates.
Complete Data Sovereignty
Zero data leaves your infrastructure. The model, your documents, and every query and response remain within your environment — meeting GDPR, HIPAA, and data sovereignty obligations by design.
Lower Total Cost of Ownership
Eliminating per-token cloud API costs at enterprise query volumes delivers substantial TCO savings — typically within 12–18 months of deployment.
Frequently Asked Questions
We assess your use case and data environment, then select, fine-tune, and deploy an open-source LLM (such as Llama, Mistral, or Phi) entirely within your on-premise or private cloud infrastructure. Your data never touches a third-party server.
We work across the leading open-source model families — Llama (Meta), Mistral, Phi (Microsoft), Falcon, and others. Model selection is driven by your use case, hardware constraints, and performance benchmarks on your specific data.
Requirements depend on model size and throughput needs. We conduct an infrastructure assessment as part of the engagement scoping. We can work with on-premise GPU servers, private cloud GPU instances (AWS GovCloud, Azure Government, etc.), or air-gapped environments.
Let's Build Your AI Future Together.
