Local LLM deployment
Hardware sizing, model selection, quantization, and serving with tools such as Ollama and vLLM. Assess quality and throughput on your own workloads.
EXPERTISE 03 / Private LLMs & knowledge systems
Your intelligence.
Your rules.
Bring useful language models closer to your data. We design and deploy private AI systems on infrastructure you control, from internal knowledge assistants to locally hosted coding models.
Talk about your projectCan we use powerful AI without sending our data away?
Yes, suitable language models and retrieval systems can run within your infrastructure. The design must also account for access controls, model licensing, logs, connectors, backups, updates, and external network dependencies.
WHAT WE CAN BUILD TOGETHER
Enterprise knowledge & RAG
Connect approved documents to retrieval with source citations, permissions, ingestion pipelines, and a plan for keeping knowledge current.
Fine-tuning & adaptation
Prepare datasets and evaluate LoRA or QLoRA adaptation when a repeatable task or specialized tone benefits from it.
Private coding environments
Configure local coding models, agent tooling, repository context, and evaluation around your engineering environment.
THIS MIGHT BE YOUR NEXT STEP IF…
Sound familiar?
- Your documents and code need to stay inside a defined boundary.
- You want more control over inference costs and model operations.
- You need domain knowledge that a general-purpose assistant does not have.
A FEW GOOD QUESTIONS
Let’s make
it clearer.
Is a local model always the best choice?
No. We compare quality, concurrency, hardware costs, support, and data requirements. Local, cloud, or hybrid deployment should follow the actual constraints.
Will an internal assistant respect document permissions?
That needs to be designed explicitly. Retrieval and source access should enforce the requesting user’s permissions, with tests for cross-user data exposure.