Local AI.
Your
environment.
We select and run an existing model on the agreed infrastructure. We test quality, response time and hardware requirements against your task.
Trial first. Then decide.
We choose the model and hardware based on the task.
Answer quality
We compare results using representative material, including more challenging cases.
Memory and performance
We check RAM/VRAM, context, execution time and the agreed workload.
Maintenance cost
We agree the hardware, administration, updates and how to connect with apps.
We work with existing models and tools, including Ollama and llama.cpp. We verify which parts of the entire workflow run locally.
How does working together look?
One process. Four clear stages.
- 1
Consultation
We learn about the task, tools and expected result.
- 2
Project
We choose the solution, data sample and acceptance criteria.
- 3
Implementation
We connect and test the agreed scope, then hand it over to your team.
- 4
Development
We assess quality, time and cost, then agree ongoing maintenance.
Questions before you start
Does a local AI model work without internet access?
The model itself can run in a local environment, but the full process may use other services. Updates, external search, tools and licences have separate requirements. We confirm offline capabilities for the specific configuration.
What hardware is needed?
It depends on the model, storage format, context length, number of users and expected response time. We test the memory and performance required in a trial. We do not recommend hardware purchases based on model size alone.
Does a local setup always keep data within the company?
It depends on the whole architecture, not just where the model runs. Integrations, logging, backups, administration and external connections all need consideration. We design and test the agreed data flow boundaries.
Is a local model better than a cloud model?
There is no single answer. We compare task quality, confidentiality, time, workload, cost and maintenance. Your own environment may offer the control you need, but it requires resources and operational responsibility.
Do you train your own large language model?
The service covers selecting, running, testing and integrating available models. It does not claim that we build our own large model from scratch. Any fine-tuning requires a separate assessment of data, cost and purpose.
Are hardware purchases and maintenance included?
We agree the scope in the proposal. We separate trial and implementation work from hardware, licences, infrastructure and ongoing administration. Recurring costs should be clear before the decision to proceed.
Let's discuss what AI can do
in your business.
Describe the task. We will choose a sensible first step.