08/09/2026
Your company’s (inexpensive) private AI ....
I just wrapped up a few months of a personal project where I built a private, local AI platform on my own server at minimal cost. I was able to bring together data from multiple sources—unstructured, structured, and data streams—and process it using the simplified framework shown in the picture included with this post. (It has been a few months since I started on this, but the picture should still be representative.)
If you’re interested, I’d be happy to help your organization with any aspect of running your own private AI using Small Language Models. And before you get started, it’s important to make sure your data is truly ready for AI—I can help with that as well.
Key points from the project:
A small language model (SLM) running locally can deliver fast, private, and cost-effective AI assistance—especially when paired with GraphRAG. By connecting structured data, such as databases, inventories, and organizational charts, with unstructured content like manuals, policies, reports, emails, and technical documentation, GraphRAG gives the model richer context and reveals relationships across the organization. This enables more relevant, accurate, and grounded answers tailored to the firm’s operations and knowledge.
Because a local SLM can run on devices without an internet connection, sensitive organizational and private data can remain within the company’s environment. Employees can use it to troubleshoot issues, find procedures, summarize internal knowledge, and solve everyday problems without sending confidential information to external services. It also reduces dependence on costly cloud models and avoids spending large amounts on API tokens, making practical, organization-specific AI accessible at a very minimal cost.
(Do not forget to inspect reasoning pattern available only when SLM are used - see one of the pictures.)
