Run capable language models inside your own infrastructure, with your data never leaving it.
THE CHALLENGE
For regulated industries, government work and anyone handling sensitive customer data, sending prompts to a third-party API is not a trade-off, it is a non-starter. The question is whether you can get useful performance from models you control, and what it costs to run them properly.
OUR APPROACH
Deploy powerful open-source language models within your private infrastructure. Full data sovereignty, air-gapped environments, and compliance-ready.
We understand your business requirements, existing systems and operational challenges.
We design an AI approach that fits your workflows, data and technology environment.
We turn the solution into a production-ready system that can be measured and improved.
WHAT'S INCLUDED
A practical engagement designed around your business requirements, technical environment and desired outcomes.
Select and benchmark models against your specific workload.
Determine infrastructure requirements for your workloads.
Deploy on-premise, in private cloud or within air-gapped environments.
Set up inference serving and scaling capabilities.
Apply fine-tuning or domain adaptation where the business case supports it.
Build retrieval capabilities over internal documents.
Implement security hardening and access controls.
Provide monitoring and a compliance evidence pack.
HOW WE WORK
A structured process keeps every engagement focused, transparent and aligned with business outcomes.
Talk to our teamAudit your data, tech stack, and AI readiness
Architect the solution with security-first principles
Build iteratively with continuous stakeholder input
Ship to production with full observability stack
Monitor, fine-tune, and scale for sustained ROI
EXPECTED OUTCOMES
We focus on outcomes that create practical value for your organization, rather than implementing technology for its own sake.
TECHNOLOGY & PLATFORMS
We choose technologies based on your requirements, infrastructure, scalability and long-term maintainability.
LLM
LLM
Open Source
Open Source
LLM
Framework
Framework
Platform
ML
Cloud
Cloud
Cloud
Infra
Infra
Vector DB
FAQ
Everything you need to know before starting an engagement with Nitiverk.
Ask our teamModel selection is based on the workload, performance requirements and infrastructure available.
Hardware requirements depend on the selected model, workload, concurrency and performance requirements.
Quality depends on the model and workload. Benchmarking against your actual use case is part of the deployment process.
The service is designed to support air-gapped environments where required.
Yes, deployment can be designed around your existing private cloud infrastructure.
Model updates can be evaluated and introduced through a controlled testing and deployment process.
EXPLORE MORE
End-to-end delivery of production-grade AI products: from architecture design and model selection through to deployment, monitoring, and iteration.
Bespoke Model Context Protocol servers that connect your AI systems to enterprise data sources, internal APIs, and business workflows at scale.
Reduce inference costs by up to 70% through intelligent prompt compression, caching strategies, context management, and model routing architectures.
Whether you are exploring a new AI opportunity or scaling an existing system, let's discuss how Nitiverk can help you move from strategy to production.