QCRCE Pte. Ltd. — Technical Introduction
A small, R&D-driven AI engineering team in Singapore. We build practical AI systems for business documents and knowledge. We test them with the same verification discipline we use in our own research.
How we work: multi-AI collaboration with cross-review
- Several AI models from different vendors each do a defined job: extract, cross-check, or review. No single model's output is trusted on its own.
- Where independent models agree, the result is accepted automatically. Where they disagree, a person reviews it. Engineers own the design and the final sign-off.
Research-grade verification
- Preregistration: before a run, we write down the hypothesis, dataset (with file hashes), metrics, and pass bars.
- Independent replication: a result is promoted only when a second, independent run agrees.
- Evidence labels: every claim is marked OBSERVED (one run), REPLICATED (independently confirmed), or PROPOSED (not yet tested).
- Reproducible bundles: inputs, code, outputs, and model settings are hashed and archived with each delivery.
Services
- Document extraction: invoices, delivery orders, packing lists, bills of lading, and similar documents (PDF, scan, or photo) turned into structured Excel/CSV or ERP/TMS import files. Arithmetic checks run on every document. Each batch comes with a two-model cross-checked accuracy report.
- RAG and knowledge assistants: search and Q&A over your own documents. Answers cite their sources and are evaluated on your questions.
- Pilots on your own data: we report accuracy on your documents, not on ours.
In-house R&D
We run a long-term research program in physics-based simulation and computing methods. It is the source of our engineering habits: careful measurement, independent checks, and honest reporting.
Contact
QCRCE Pte. Ltd., Singapore · mark@qcrce.com