Deployment that fits your environment
Review model, hardware and hosting options against your use case. Establish the system requirements before committing to a deployment.
Private & local AI deployment
Private AI deployment. On infrastructure you control.
Run AI models in an environment your business controls. We scope local, on-premises or private cloud deployments around your data requirements, workload and available infrastructure, then build the system that runs on it.
01 What we build
Your infrastructure. Your control.
For businesses that need control over where models run and how their information is handled.
Defined data boundaries
Map where information is stored, processed and sent. Agree which external services, if any, the system may use.
Access and action controls
Define who can use the system, which information they can access and which actions require approval. Include logging requirements in the project scope.
Evaluation before wider rollout
Begin with a focused use case and representative data. Review output quality, performance and operating costs before expanding the system.
Deployment configuration, agreed data connections, evaluation results and operating documentation for the scoped system.
02 What runs where
A private system, end to end.
Local inference
Open-source models served through Ollama or llama.cpp on your own hardware, using a GPU where one is available and the CPU where it is not.
Private knowledge and documents
Document extraction, search and a knowledge store run alongside the model, so your files are processed inside the deployment.
Agents propose, people decide
Agents read, extract and draft. Nothing becomes a record and nothing is sent without a person, and every step is logged.
Built without production access
Systems can be developed on dummy data with no developer access to your production environment, then handed over as Docker deployment packages.
Technologies in this work
- Ollama
- llama.cpp
- Qwen3
- LangGraph
- Python
- pgvector
- PostgreSQL
- Docling OCR
- Docker Compose
- NestJS
- React
03 Related work
Built, shipped and measured.
The requirements, technical decisions and results behind systems like this one.
04 Our approach
A clear plan. Working software. A documented handover.
01
Understand the requirements
Discuss users, business problems, existing systems and constraints. Define what a useful first release needs to accomplish.
02
Design and scope
Agree the architecture, deliverables, milestones and project price. Review deployment choices, data handling and running costs.
03
Build and evaluate
Review working software as development progresses. Test application behavior and evaluate AI outputs against agreed acceptance criteria.
04
Deploy and hand over
Launch in the agreed environment. Receive the custom source code, documentation and a walkthrough, with support responsibilities defined.
05 Good questions
Questions, answered.
Yes, where the use case and infrastructure support it. We review local, on-premises and private cloud options against your model requirements, workload and operating budget.
Local inference keeps model processing in the local environment. Connected APIs, email services and other tools may send information elsewhere. We map those connections and agree the permitted data flows before implementation.
It depends on the model size, the number of people using the system and the response speed you need. We review options against your workload before you commit, and systems can use a GPU where one is available or run on the CPU.
Hosted models are often stronger on complex reasoning. Local open-source models keep processing in your environment and make running costs more predictable. We test against your own examples before recommending a deployment.
We establish data flows, access requirements and deployment boundaries during scoping. The project can include role-based access, secrets management, action approvals and logging according to your requirements. Specific controls and responsibilities are documented in the proposal.
07 Your next chapter
What do you want to build?
Tell us about your application, the system your team needs or the AI capability you want to develop.
A free 30-minute conversation about your requirements and a practical first release.