More control over sensitive data
Define what stays in your environment, what can reach an external service and who is allowed to access each source.
Local and private AI for SMEs
Local AI infrastructure built around your business.
We do not only supply hardware. We design and deploy a complete local AI solution around your data, users, workflows and operating requirements — from the model and company knowledge to security, integrations and ongoing support.
The complete environment
Server + model + knowledge + integrations + operations.
Designed for real company tasks
Tested before the final configuration
Clear local and external data boundaries
One AI service for your team
Provide selected models to authorised people, internal applications and controlled automation through one environment.
Performance tested on real work
Size the solution using your model, context, response time and concurrent demand — not a marketing specification alone.
Practical use cases
Start with the work your team needs to improve.
A server is not the goal. The goal is a reliable capability that helps people find knowledge, process information or complete approved work.
Company knowledge and document search
Give authorised people a private way to search policies, manuals, proposals, product information and internal know-how.
Internal assistants and agents
Run assistants for research, drafting, support or approved actions without making a public AI account the centre of your operation.
Document and workflow automation
Classify, extract, summarise and route documents, forms and recurring work through controlled integrations.
A local AI API for your applications
Provide selected models to internal tools, websites or automation through an authenticated, monitored interface.
What we actually deliver
Hardware is the starting point. The deployment creates the value.
Hardware and performance class
Select a practical starting configuration based on the model, context, number of users, response time and expected workload.
Model and local runtime
Choose and test the right model and runtime instead of installing the largest option that happens to fit in memory.
Knowledge and business data
Connect approved documents, databases, product data or company knowledge with access rules appropriate to their sensitivity.
Integrations and permissions
Connect APIs and workflows while limiting every assistant or agent to the tools and actions it is allowed to use.
Security and operations
Set authentication, network access, logs, backups, updates and operational boundaries before the server becomes a dependency.
Testing and ongoing support
Measure the system on real tasks, train the team and agree how performance, models and integrations will be maintained.
From requirement to operation
First the task. Then the model. Only then the server.
This reduces the risk of buying a system that is too weak, unnecessarily expensive or poorly matched to the software the company needs.
Define the work
Clarify the data, tasks, users, integrations and information that must remain under your control.
Test model and load
Use representative data to test quality, memory, speed and concurrent demand before recommending hardware.
Build the complete environment
Prepare hardware, runtime, models, knowledge, permissions, integrations and the operating setup as one solution.
Launch and maintain
Review security and load scenarios, train users and establish updates, monitoring and support.
Technologies and platforms we work with
Independent design, not a single-vendor prescription.
Local AI deployments can use LM Studio or another suitable runtime. The choice depends on hardware, models, security, load and the way your applications need to connect. Kodo does not claim official LM Studio partnership or representation.
LM Studio
NVIDIA NIM
vLLM or Ollama
OpenAI-compatible APIs
FAQ
Questions to answer before buying hardware.
Why run AI locally instead of using a cloud service?
Local AI can make sense when you need more control over data, a stable internal environment, predictable integration or regular high usage. Not every company needs its own server. If a cloud service is the more practical option, that should be clear before buying hardware.
Can the system work without internet access?
The local model, company knowledge and internal chat can operate within your own environment. Internet access is required only for approved external APIs, remote services, model downloads or updates.
Do you work with LM Studio?
Yes. We can use LM Studio for suitable local deployments and work with other runtimes when the workload or operating requirements call for them. Kodo is not presented as an official LM Studio partner or authorised representative.
How many users can one AI server support?
The answer depends on model size, context length, response speed, concurrent requests and connected workflows. We test the intended scenario before confirming a configuration.
Why do you not publish one fixed price?
Hardware is only one part of the project. Models, storage, security, integrations, migration, testing and support change the scope materially. We prepare options and a clear budget after a focused technical and business review.
Start by defining what AI should do inside your business.
Describe the tasks, data, users and operating constraints. We will suggest what to test first and whether a local server is the right next step at all.
Discuss your AI requirements