Structured data processing, labelling, classification, and annotation workflows — with built-in QA review and a 95% minimum accuracy standard from Week 3 of any engagement.
Most companies sit on growing volumes of unstructured or semi-structured data — product catalogues, images, documents, customer records, training data for AI models — that needs to be reviewed, labelled, categorised, or cleaned before it's usable. We build a dedicated Nepal-based team to handle this work on an ongoing or pilot basis, with daily QA built into the workflow from day one.
E-commerce companies with large product catalogues, logistics and manufacturing companies digitising records, AI/ML teams needing labelled training data, and any company with a recurring data-cleaning or classification backlog.
A retail client has 50,000 product listings needing review, standardised categorisation, and metadata tagging. Three Prime Quest associates handle roughly 120 records each per day — about 1,800 per week — accessing the data through the client's own EU-hosted cloud environment via named, MFA-protected accounts. No data is stored locally in Nepal. Weekly QA samples are reviewed by the client's regional governance partner before sign-off.
Our default model keeps your data inside your own systems — our team accesses it through your environment rather than transferring it to ours. Where a transfer is unavoidable, a signed DPA and Standard Contractual Clauses apply before any work begins. See our full data protection framework.
We'll come back with a realistic team size, timeline, and pilot price within a few business days.