LABELVIBE AI PTE. LTD. is a Singapore-based AI data services company. We design, collect, annotate and evaluate the datasets behind production AI systems, delivered through the LabelVibe group's established production network.
LABELVIBE AI PTE. LTD. is the Singapore company of the LabelVibe group, serving international clients who need reliable, production-grade data for artificial intelligence. From Singapore, we manage client relationships, project design and quality assurance for data programmes delivered across the group.
The group's operating affiliate in China, Lingbo Weibu (Beijing) Technology Co., Ltd., has specialised in AI foundation data services since 2017. Its delivery network — a Beijing R&D centre together with data delivery centres across seven cities — gives our clients access to a full-time production team of around 1,000 people, with the processes and supervision that large, long-running annotation programmes require.
That structure is deliberate: Singapore provides an accountable, English-speaking contracting and coordination point, while the group's established production base provides scale. Clients get one partner to talk to, and a delivery engine that has been running for close to a decade.
Five service lines covering the full life of a dataset — from raw collection to the human judgements that align and evaluate a finished model.
Precise labelling of image, video, text, audio and point-cloud data against a written guideline — bounding boxes, segmentation, keypoints, classification and transcription, with edge cases defined before production starts, not discovered during it.
Collection of source data to specification — imagery, speech, video and text gathered under defined scenarios, demographics and consent requirements, then screened before it enters the annotation pipeline.
End-to-end production of training datasets: guideline design, pilot batches, scaled annotation, quality sampling and delivery in the formats your training pipeline consumes. Built for teams training vision, language and multimodal models.
Structured human judgement for model alignment: response ranking, preference pairs, instruction-following assessments and safety reviews, produced by trained reviewers working to calibrated rubrics with agreement monitoring.
Benchmark and evaluation sets that measure what a model actually does: curated test data, human scoring of model outputs, and error categorisation that shows your team where the model fails — and why.
One contracting point in Singapore; a production network behind it that has been delivering AI data since 2017.
LABELVIBE AI PTE. LTD. (Singapore) is the group's international-facing company: client communication, project scoping, guideline design and final quality sign-off are coordinated here, in English, on a schedule that suits overseas teams.
Production runs through the group's China delivery network, operated by Lingbo Weibu (Beijing) Technology Co., Ltd. A research and development centre in Beijing supports tooling and process engineering, while dedicated delivery centres run annotation and review work with trained, full-time teams.
For clients, that means scale without the usual trade-off: programmes can grow from a pilot batch to sustained, high-volume production without changing partner or re-teaching guidelines.
| Location | Role in the group |
|---|---|
| Singapore | LABELVIBE AI PTE. LTD. — international headquarters, client management & QA |
| Beijing | R&D centre — tooling, process engineering, project design |
| Changzhi | Data delivery centre |
| Yuncheng | Data delivery centre |
| Datong | Data delivery centre |
| Jinan | Data delivery centre |
| Yantai | Data delivery centre |
| Taiyuan | Data delivery centre |
| Chongqing | Data delivery centre |
Dataset quality is a process outcome, not an inspection outcome. Every programme follows the same four stages, whether it is a 500-item pilot or a multi-month production run.
We define the taxonomy, labelling rules, edge cases and acceptance criteria with you in writing — the document every later decision is checked against.
A small batch is produced and reviewed first. Ambiguities surface here, while they are cheap to fix, and the guideline is revised before scale-up.
Trained annotators produce the data; reviewers check it in a second pass. Disagreements are adjudicated against the guideline, not left to individual judgement.
Before delivery, batches are sample-audited against the acceptance criteria and exported in your required format, with a summary of what was checked.
Tell us what you are building and what data it needs. We reply with an honest read on scope, a proposed pilot, and the guideline questions we would want answered before production — no obligation attached.