AI projects
An AI project at Lifewood is a named delivery programme with its own accuracy bar, staffing and audit trail. The active ones span four flagship domains: AIGC video and content production, enterprise LLM training datasets, autonomous driving annotation, and multilingual speech and NLP data. Powered by 50+ supported languages and 40+ delivery centers, these projects underpin frontier model programs at Apple, iFLYTEK, ArcSoft, NVIDIA, and WeRide, alongside enterprise customers in publishing and hospitality.
Global Data Engineering
We deliver end-to-end AI data solutions — from privacy-safe collection and large-scale annotation to managed dataset pipelines that power enterprise-grade models across every domain we serve.
What we currently handle
A selection of our active projects and capabilities.
AI projects, answered
What counts as an AI project at Lifewood?
A named delivery programme with its own accuracy bar, staffing and audit trail — not a capability statement. The active ones span four domains: AIGC video and content production, enterprise LLM training datasets, autonomous driving annotation, and multilingual speech and NLP data.
How large do these programmes get?
Large enough that unit economics decide the approach. One current framework covers up to 3,000 titles at approximately USD 3 million across two years, after a pilot of 10 titles and 70 deliverables at USD 16,500. Autonomous driving work is benchmarked at 99.9% annotation accuracy for L4-level scenarios, against the 95%+ SLA that governs general programmes.
How does a project start?
With a scoped pilot that establishes the gold set and baselines accuracy and turnaround on real deliverables before any volume commitment, inside the six-stage delivery methodology. Pilots are deliberately small so procurement can compare output against existing suppliers rather than against a promise.
Frequently asked questions
Four flagship domains: AIGC video and content production, enterprise LLM training data including RLHF and supervised fine-tuning corpora, autonomous driving and in-cabin annotation, and multilingual speech and NLP collection across 50+ languages.
A 95%+ accuracy SLA and 95%+ inter-annotator agreement as standard, rising to 99.9% benchmarked accuracy for L4 autonomous driving scenarios where the error budget is physical rather than statistical.
Yes — text, image, audio, video and LiDAR, under one quality standard. Cross-modality consistency is the hard part, since each modality has its own tooling and failure modes, so the same dual-layer review is applied across all of them.
As case studies published under sector descriptors, with the engagement values, deliverable counts and accuracy benchmarks stated. Timestamped approval records travel with delivery, so any individual asset can be audited long afterwards.
Related services & resources
- AI Data ServicesAnnotation, RLHF, collection, and validation across 50+ languages.
- AIGC ServicesAI-generated video, voice, and script production under human direction.
- Enterprise LLM Training DataInstruction, preference, and domain corpora built for fine-tuning.
- Autonomous Driving AnnotationLiDAR, camera, and radar perception labeling for AV stacks.
- Multilingual Data CollectionNative-speaker collection across 50+ languages and dialects.
- Global AI Data40+ delivery centers supplying data at production scale.
- QA ProcessThe dual-layer human-in-the-loop review behind every delivery.
- Case StudiesDelivery outcomes across LLM training, AIGC, AV, and AEO/GEO programs.
- Autonomous Vehicle Perception Case StudyAutonomous vehicle perception annotation at scale.
- AIGC Deep DiveA full walkthrough of the Lifewood generative content pipeline.
- AI Glossary30+ defined terms across AEO, GEO, AIGC, and data operations.
- ContactScope a program, request a sample, or book a technical call.