On-premise AI deployment
We install production-grade open-source language models inside your data centre or server room: hardware sizing, inference serving, an OpenAI-compatible gateway, access control and monitoring — documented and handed over.
What the engagement includes
Sizing & procurement advice
Concurrency, latency and context-length requirements translated into a concrete GPU specification — from a single workstation to a multi-node cluster.
Model selection & quantization
Benchmarks of Llama, Mistral, Qwen and DeepSeek variants on your tasks, with GGUF, AWQ or GPTQ quantization where it preserves quality.
Inference serving
vLLM, TensorRT-LLM or Ollama configured for your throughput profile, with batching, KV-cache tuning and failover.
Gateway, SSO & access control
A self-hosted, OpenAI-compatible API layer with SAML/OIDC single sign-on, per-team keys, role-based access and full audit logs.
Metering & quota management
Token metering per user, team and application with chargeback reports, plus hard/soft quotas, budgets and rate limits so capacity stays fair and costs stay visible.
Retrieval: vector & GraphRAG
Document Q&A built on vector databases (Qdrant, Milvus, pgvector) and knowledge-graph retrieval (GraphRAG) for multi-hop questions across entities.
Applications
An internal chat workspace, document Q&A over your knowledge base, and API access for your developers.
Documentation & training
Runbooks, architecture diagrams and admin training so your team can operate the stack independently.
Frequently asked questions
How long does an on-premise deployment take?
A pilot typically runs two to four weeks. Production deployment, including gateway, access control and integration, usually lands within six to twelve weeks depending on procurement lead times.
Do we need to buy hardware before talking to you?
No. Sizing comes first. We model your workload, then recommend hardware — and pilots can run on rented GPUs or existing servers before you commit to purchases.
Which models do you deploy?
Current open-weight leaders such as Llama, Mistral, Qwen and DeepSeek families, selected and benchmarked against your actual tasks rather than generic leaderboards.
Can this replace our ChatGPT subscriptions?
For most internal drafting, summarisation, coding-assistance and document Q&A workloads, yes. We measure quality on your tasks during the pilot so the decision is evidence-based.