Vertical LLM data is the domain-specific counterpart to a general corpus: data built for one industry rather than all of them — autonomous driving annotation, in-vehicle collection, and specialised corpora for enterprise or private models, delivered under a 95%+ accuracy SLA.
Autonomous driving and Smart cockpit datasets for Driver Monitoring System
China Merchants Group: Enterprise-grade dataset for building "ShipGPT"
TARGET
Target
Annotate vehicles, pedestrians, and road objects with 2D & 3D techniques to enable accurate object detection for autonomous driving. Self-driving cars rely on precise visual training to detect, classify, and respond safely in real-world conditions.
Vertical LLM data, answered
What is vertical LLM data?
Vertical LLM data is domain-specific training data built for one industry rather than all of them — autonomous driving, in-vehicle systems, or a private enterprise model trained on its own field. It is what turns a generally capable model into one that is correct about a specific subject, and it is where accuracy requirements are usually strictest.
Why do accuracy thresholds rise in vertical programmes?
Because the error budget is physical rather than statistical. In autonomous driving, a mislabelled pedestrian is not a percentage point, it is a failure mode — which is why Lifewood benchmarks annotation accuracy at 99.9% for L4-level scenarios, against the 95%+ SLA that governs general programmes.
What does a private or enterprise LLM programme involve?
A corpus assembled from an organisation’s own material — documents, records, transcripts — cleaned, structured and labelled so a model can be trained or fine-tuned on it without leaking or hallucinating around it. The work is governed by the same dual-layer review process and audit records as every other programme, which is what procurement and compliance review.
Frequently asked questions
When the model has to be correct rather than merely fluent about a specific field. Horizontal data establishes general competence; vertical data supplies the domain knowledge. Most production programmes use both, and buying only horizontal is the usual cause of a model that sounds authoritative and is wrong in the one area that matters.
LiDAR point clouds for 3D structure, multi-camera detection for semantics, radar for velocity and adverse weather, and driver-monitoring data for the cabin. The hard part is fusion — making the modalities agree — which is where single-modality vendors usually stop. Lifewood delivers this through dedicated AV centers in Malaysia and Indonesia.
Through demographically balanced subject panels recruited in-region and controlled in-cabin recordings, with consent recorded for the specific use. Balance has to be designed into recruitment: a dataset collected conveniently in one location reproduces that location’s demographics no matter how large it grows.
Yes. Enterprise and private-model programmes run under the same audit-record process as the rest of Lifewood’s work, with timestamped approvals per batch, so a delivered dataset can be traced to the terms it was gathered and reviewed under years later.
Related services & resources
- Type A — Data ServicingCore annotation and enrichment across text, image, audio, and video.
- Type B — Horizontal LLM DataBroad-domain corpora for general-purpose foundation models.
- Type D — AIGCGenerative production pipelines for content at scale.
- Enterprise LLM Training DataInstruction, preference, and domain corpora built for fine-tuning.
- AI Data ValidationIndependent dual-layer QA against a contractual 95%+ accuracy SLA.
- Edge IntelligenceData programs for on-device and latency-constrained models.
- QA ProcessThe dual-layer human-in-the-loop review behind every delivery.
- Delivery MethodologyThe six-stage pipeline from scoping through post-delivery audit.
- Case StudiesDelivery outcomes across LLM training, AIGC, AV, and AEO/GEO programs.
- AI Data ServicesAnnotation, RLHF, collection, and validation across 50+ languages.
- AI Glossary30+ defined terms across AEO, GEO, AIGC, and data operations.
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