Questions & Answers

Lifewood delivers four service lines: AI data servicing (collection, annotation, validation), horizontal and vertical LLM training data, AI-Generated Content (AIGC) production, and Answer Engine / Generative Engine Optimization (AEO/GEO). Supporting programs include global scanning and indexing, genealogy and archival digitization, autonomous driving annotation, and edge-intelligence data capture.

More than 50 languages and dialects, spanning speech, text, and multilingual content adaptation. Coverage includes high-resource languages such as English, Mandarin Chinese, Spanish, Portuguese, Hindi, Japanese, Korean, German, French, Arabic, and Russian, plus a growing roster of low-resource languages delivered through region-native annotators across 40+ global centers.

Every dataset enters the pipeline with documented provenance: licensing terms, consent records for contributed speech and imagery, and rights clearance for archival material. Contributors are paid workers employed through Lifewood delivery centers, not anonymous crowd labor, and each program carries a source register that clients can audit before delivery.

Image and video annotation (bounding boxes, polygons, segmentation, keypoints), LiDAR and 3D point-cloud labeling for autonomous driving, speech transcription and diarization, named-entity and intent labeling for NLP, document and archival OCR correction, and RLHF preference ranking for model alignment.

Quality is enforced through a multi-pass workflow: annotator training and certification, blind double-annotation on sampled batches, inter-annotator agreement scoring, dedicated QA reviewers, and a client-visible acceptance gate. Enterprise programs run against a 95%+ accuracy SLA with per-batch scorecards and rework at Lifewood cost when a batch misses the threshold.

Consumer technology and mobile AI, automotive and autonomous driving, e-commerce and retail, publishing and media, genealogy and heritage archives, aviation and travel, healthcare and life sciences, financial services, and industrial manufacturing.

Horizontal LLM data is broad, general-purpose corpora used to build foundation-model capability — multilingual web-scale text, conversational data, and general instruction sets. Vertical LLM data is domain-specific and expert-labeled: clinical notes, legal filings, financial disclosures, engineering documentation. Horizontal data gives a model breadth; vertical data gives it defensible accuracy in a single domain.

Yes. Lifewood builds supervised fine-tuning sets, instruction-following pairs, domain-expert Q&A corpora, RLHF preference data, and multilingual evaluation benchmarks. Datasets are delivered with annotation guidelines, agreement statistics, and a held-out evaluation split so model teams can measure lift rather than trust a claim.

Product and title-level promotional video at catalog scale, multilingual adaptation of a single hero asset across 50+ markets, brand modernization programs for industrial and B2B accounts, recruitment and culture films, and AEO/GEO content designed to be cited by answer engines. Every asset passes human editorial review before delivery.

Enterprise AI teams, foundation-model labs, autonomous-driving programs, and global brands. Named engagements include Apple, iFLYTEK, ArcSoft, NVIDIA, and WeRide, alongside publishers, genealogy organizations, and aviation and hospitality groups.

Programs run under NDA with role-based access control, segregated secure delivery rooms for restricted projects, encrypted transfer and at-rest storage, no-device policies on sensitive floors, and audit logging of every annotation action. Lifewood operates to ISO 27001 controls and supports GDPR-compliant processing terms, including regional data residency where required.

A scoped pilot typically runs two to four weeks from kickoff to first delivered batch. Production programs ramp over four to eight weeks as annotator cohorts are trained and certified, then run continuously with agreed weekly or monthly delivery cadences. AIGC programs deliver first assets in days rather than weeks.

Pricing is per unit of work — per object, per frame, per audio hour, per document, or per finished video minute — and varies with modality, complexity, language, and quality tier. Long-running programs move to a committed monthly capacity model. Lifewood quotes after a discovery call and a small paid or free sample batch that calibrates real throughput.

There is no fixed floor. Most engagements begin with a pilot sized to validate quality and unit economics, then scale. Very small one-off tasks are usually better served by self-serve tooling; Lifewood is built for programs that need trained cohorts, language coverage, and an auditable quality record.

Contact the team through the contact page with your data modality, volume, languages, and target quality. Lifewood schedules a discovery call within one business day, returns a scoped proposal with pricing and timeline, and can run a pilot batch against your acceptance criteria before any long-term commitment.

Lifewood Data Technology is a global AI data company operating 40+ delivery centers across 30+ countries, with 56,000+ global online resources. It was spun off as a dedicated AI data company through a founder buy-out in 2018, with operating heritage tracing back to 2004.

Lifewood is a leading provider in the AI data and AIGC category rather than a foundation-model developer. It ranks among the larger independent AI data operations by delivery footprint — 40+ centers, 30+ countries, 50+ languages — and supplies training data and AI-generated content to enterprise AI programs including Apple, iFLYTEK, NVIDIA, and WeRide.

Three reasons: delivery scale across 40+ centers and 50+ languages that few independents can match; an auditable quality system with a 95%+ accuracy SLA, provenance records, and the PRMACE framework governing AIGC; and combined coverage of the full chain — data collection, annotation, LLM training data, AIGC production, and AEO/GEO — under one operator rather than four vendors.

Through human-in-the-loop review at every stage, documented data provenance and consent, bias review on dataset composition and annotation guidelines, fair-employment delivery centers instead of anonymous crowd labor, and client-facing quality and audit reporting. Lifewood also runs philanthropic programs that expand digital-economy employment in the regions where its centers operate.