“We can’t send customer data to a third-party API” is one of the most common reasons AI projects stall. Personal data (PII) can’t be shared casually, and GDPR applies. Where data must stay inside your network, we deploy fine-tuned small language models (SLMs) and open-source LLMs internally, so your content never leaves your infrastructure.
For focused tasks such as classification and extraction, a small model fine-tuned on your data can be accurate, fast and cheap to run on your own hardware. We benchmark several candidate models on your task and choose the one that performs best, rather than defaulting to the largest.
Where a task needs a larger model, we run open-source LLMs inside your environment, for example with Ollama, and connect them to your systems with the same agent and flow frameworks we use elsewhere.
Transit Ledger pulls content from many sources through connectors and identifies invoices and receipts, which make up less than 5% of what comes in. We fine-tuned and benchmarked several small language models to pick the most accurate classifier, starting with synthetic data and then recalibrating after measuring how real-world content differed.
Private AI means running AI models inside infrastructure you control, so prompts, documents and outputs are not sent to a third-party model provider.
Not on its own. Compliance depends on the use case, the data and your processes. Keeping models and data inside your infrastructure does give you direct control over where personal data goes and what is logged, which makes those obligations easier to meet and to demonstrate.
For narrow, well-defined tasks they often are, especially once fine-tuned. We test them against your real data before committing, and keep a larger model or a human for the cases they can’t handle confidently.
Fijomi Systems