SLMs & local models
Can you use AI without your data leaving the building? Often, yes. We select, adapt and deploy small language models on your own servers or devices, and test speed and cost at your real volume.
A sample run
A small model on your own server reads each incoming request and picks the queue. When it isn’t sure, a person decides.
Request
The cold-room compressor at our Pune site has been tripping since last night.
Routed REQ-30418 to Refrigeration, Pune as high priority: equipment fault at a customer site.
Checked against
Result
Refrigeration · Pune
Logged on srv-ai-02 · no external calls
Sample data. Names and figures are illustrative, not from a client.
What it handles
Your team agrees the quality threshold, data boundary and review rules. A local model is not automatically faster, cheaper or private: we test the complete system and include updates, monitoring and maintenance in the operating plan.
Classify text, extract agreed fields or draft summaries inside infrastructure you control. We trace the whole data path, including retrieval, application logs and updates, not only the model.
A smaller model on a defined job, such as routing requests or preparing short drafts, can cost less than paying per call. We compare both at your volume before recommending either.
Suitable tasks can run on a local server or device at the site. For true offline use, the application, its data and its dependencies have to work without external services too.
How it works
A small language model (SLM) is a compact language model that needs less memory and compute than a larger one. Local describes where it runs. We choose model size and deployment together, based on the task, data constraints and available hardware.
Agree the task, representative inputs, quality threshold, expected volume and where data may travel. Review the hardware available.
Compare suitable existing models and their licences. Test prompts, retrieval or fine-tuning where needed, using data your company is allowed to use.
Measure accuracy, response time, throughput and full operating cost at expected load. Include difficult inputs, peak demand and human correction.
Integrate the local model with your application. Configure access, logs, monitoring and updates; agree rollback, recovery and ongoing ownership with IT.
Across industries
Where sensitive data, steady volume or remote sites make a local model worth testing.
Your next step
Bring a recurring AI task, sample inputs, expected volume and any hardware or data restrictions. We’ll assess whether a local model fits and what a useful trial needs to establish.