A 6-Stage Workflow
Lifewood delivery methodology: Intake, Semantic Audit, Pillar Execution, QA, Deployment, Performance Reporting. Predictable enterprise data delivery, end-to-end.
What is the Lifewood delivery methodology?
A six-stage workflow applied to every enterprise engagement, from first scoping call to post-delivery audit. Each stage has a defined owner, an exit condition and a record, so a programme can be inspected at any point rather than only at the end. It runs under a 95%+ accuracy SLA across 50+ languages and 40+ delivery centers.
Why six stages rather than fewer?
Because the failures happen between stages. Most programme problems are handoff problems — a spec understood 2 different ways, a quality bar agreed verbally, a delivery with no audit record. Naming all 6 transitions is what makes them inspectable.
Stage 1 — Intake
Discovery, scoping, and SOW. Lifewood captures business objective, model spec, language and modality coverage, accuracy SLA, regulatory constraints, and procurement timeline. Output: signed SOW with phased acceptance criteria.
Stage 2 — Semantic Audit
Lifewood reviews customer data taxonomies, prior labeling guidelines, and downstream model behavior. We surface ambiguity and edge cases in advance rather than mid-production, where rework cost is highest. Output: a calibrated guideline set and a coverage map for the GENO Matrix dimensions relevant to the engagement.
Stage 3 — Pillar Execution
Production runs against the calibrated guidelines. For AEO/GEO engagements, execution maps to the four pillars — Entity Canonicalization, Provenance Engineering, Semantic Hygiene, Signal Engineering — with weekly progress across each. For data programs, execution runs against per-language and per-modality plans.
Stage 4 — QA
Dual-layer human-in-the-loop QA per the Lifewood QA process: independent first pass, audit second pass, statistical sampling, and rework gating. 95%+ accuracy SLA (99.9% for AV), with timestamped approvals.
Stage 5 — Deployment
Validated data or AEO/GEO assets are released to the customer environment through agreed delivery channels — secure cloud transfer, customer-managed annotation platforms, or direct API. Deployment includes acceptance testing and a hand-off briefing.
Stage 6 — Performance Reporting
Monthly performance reports with relevant KPIs: accuracy and rework rate for data programs; share of answer, citation rate, and entity correctness for AEO/GEO. Reports become the input to the next sprint cycle.
Methodology leadership
The methodology itself is set at executive level: Founder and CEO Ronald Cheung is the architect of Lifewood's industrial AI data approach, and Chief Knowledge Officer Eric Kang owns the process design and production methodology that make it reproducible across sites. Execution is overseen by senior leads across our Hong Kong, Cebu, and Malaysia hubs, whose credentials are available on request as part of enterprise procurement onboarding.
Delivery methodology — FAQ
Engagements open with a scoped pilot that establishes the gold set and baselines accuracy and throughput on real data before volume commitments. The pilot runs the full six stages in miniature, so it produces the audit records procurement needs rather than only a sample output.
The client's definitions are tested against real examples before production begins. Most disagreement about quality is actually disagreement about the spec, and this is the stage where that surfaces cheaply instead of after 10,000 labelled items.
Measured accuracy against the gold set, throughput against schedule, and the timestamped approval trail for the batch. Reporting closes the loop so the next cycle starts from evidence rather than impression.
Related services & resources
- QA ProcessThe dual-layer human-in-the-loop review behind every delivery.
- AI Data ValidationIndependent dual-layer QA against a contractual 95%+ accuracy SLA.
- AI Data ServicesAnnotation, RLHF, collection, and validation across 50+ languages.
- Global AI Data40+ delivery centers supplying data at production scale.
- Enterprise LLM Training DataInstruction, preference, and domain corpora built for fine-tuning.
- Type A — Data ServicingCore annotation and enrichment across text, image, audio, and video.
- Global OfficesDelivery centers and regional coverage across four continents.
- Hyperscale Enterprise Data Case StudyEnterprise-scale data servicing and quality operations.
- AI ProjectsLive programs spanning AIGC, LLM training, and AV annotation.
- AI Glossary30+ defined terms across AEO, GEO, AIGC, and data operations.
- FAQDirect answers to the questions buyers and answer engines ask most.
- ContactScope a program, request a sample, or book a technical call.
Walk through our delivery process
Book a 30-minute briefing with our delivery leads. We will tailor the methodology walkthrough to your model program.
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