We build small, purpose-built models for regulated work. Under 300 MB. CPU only. Same input, same answer, every time. Numbers don't lie, and our models don't stray.
Agents that write code, call tools and reason in open loops are powerful. They are also the thing your security team lies awake about. Our models do one job, and nothing else is physically in them.
They classify, extract, match and score. They cannot write code, invent a fact, call a tool, or talk their way past a guardrail. There is no prompt to inject.
Deterministic inference with pinned weights. Run it a thousand times, run it next year, run it in front of an auditor. Same answer, byte for byte.
Runs on a CPU inside your walls: on-prem, private cloud or air-gapped. No calls home, no data egress, no third-party model in the loop.
Swap the repetitive LLM calls inside your pipeline for tinyAI Labs models. The planner stays. The token bill doesn't.
Read, classify, extract and redact on CPU before anything gets embedded. Same document in, same chunks out, with lineage on every field.
Short names, one job each. Every model runs under 300 MB on a CPU and gives the same answer every time.
Classifies documents and extracts typed fields, each with a confidence score and its source line.
IDs, scans, stamps, signatures and inspection images, labelled region by region.
Always-on monitoring of transactions, covenants and AML flags, with the reason attached.
Decisions that never drift. Replay any decision from any date and get the same result.
Precision matching for entities, sanctions lists and reconciliation.
Train your own model on about 200 examples, on a CPU, in under an hour.
Fifteen steps, each right 90% of the time, gives you 0.9 to the fifteenth. Twenty-one percent of files come out clean.
That is the hidden bill in every LLM-first pipeline: retries, human review and errors that escape to customers. Tokens are the smallest part of it. Past two chained calls, a deterministic engine wins and keeps winning.
Our models were built inside Synapze, running origination, servicing and monitoring at European and US lenders. Measured on real SME loan files: 15 documents, about 264 pages a case.
Your documents, a tinyAI Labs model and your current LLM pipeline, side by side. Accuracy, cost per case and reproducibility, measured inside your environment.