We build end-to-end AI flows: pipelines that ingest data from your sources through connectors, then classify, extract and route it. Each stage resolves what it can at the lowest cost and passes only the uncertain cases on.
Real processes are full of exceptions. We map how the process actually runs, including failure modes and edge cases, before we automate it. Where real data is scarce or sensitive, we start with synthetic data, then validate against real-world data and correct for the gaps between the two.
Daily automated checks catch issues early. Each week we refine rules and models so more cases are resolved at the cheaper early stages, and the flow gets cheaper and more accurate over time.
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.
AI agents run daily checks for issues and raise tickets automatically. Every week we analyse what reached the LLM stage and turn it into better rules, so more spam is caught in the earlier, cheaper stages.
An AI flow follows a designed sequence of steps, such as ingest, classify, extract and route. An AI agent decides its own next step towards a goal. Many solutions combine both.
It works in a demo but doesn’t scale economically. Cheaper stages can handle most routine cases, so the large model only processes what genuinely needs it.
We can start with synthetic data, then measure how real-world content differs and recalibrate, as we did on Transit Ledger.
Fijomi Systems