BAOBAB Inc.
BAOBAB Inc. is based in Tokyo, Japan and works in Large Language Models for Data, Analytics & AI Platforms, with further work in AI Ethics & Governance and Computer Vision.
- Average rating
- —
- Reviews
- 0
- Profile coverage
- 80%
- Evidence percentile
- P75
Profile updated · Request a correction
Company & commercials
- Primary category
- LLM Agents & RAG
- Team size
- Not disclosed
- Typical budget
- Not disclosed
- Primary industry
- Technology, Software & Digital Infrastructure
What BAOBAB Inc. does best.
LLM Agents & RAG provider for Technology, Software & Digital Infrastructure
Specialized provider of high-quality AI training data and annotation services, supporting LLM development and multimodal AI through transcription, tagging, and its proprietary app. Its own website shows its strongest capability in Large Language Models within LLM Agents & RAG. The industry it describes most is Data, Analytics & AI Platforms.
1 platforms & methods
Bars show how many mapped capabilities use each one.
- Baobab AutoML Vision Report1 capability
Evidence report
How BAOBAB Inc. compares with peers on evidence, reviews and coverage.
What the evidence says
BAOBAB Inc. presents as a moderate evidence profile for LLM Agents & RAG. It ranks in the 75th percentile of 1,860 published providers in LLM Agents & RAG, and no reviews on the profile yet.
Current positioning: P75 evidence quality · P47 industry breadthAI capabilities.
AI Ethics & Governance
None of the listed technologies is specific to AI Ethics & Governance.
Mapping source · https://baobab-trees.com/ — “、LLM）の開発、モデルの精度を高めるのに不可欠なファインチューニング用のデータセット構築、AIのパフォーマンスを最大限に引き出すRLHF（Reinforcement Learning from Human Feedback）、及びモデルの評価まで一貫して承ります。 詳しく見る > 画像アノテーション／音声書き起こし 2015年以来、バウンディングボックス”
Featured 12 of 17 capabilities, ranked by mapping grade then confidence.
Industries served.
- Data, Analytics & AI PlatformsDirect · Primary95%
Verified 2026-10-21: Training data for AI developers (e.g., Preferred Networks) (https://baobab-trees.com)
What clients say.
Creates high-quality image annotation datasets meeting various requirements.
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How RankGlobal maps this profile.
RankGlobal maps this profile against a controlled taxonomy. Mapping grades on capability and industry cards describe how the profile is classified. Source strength for ranking decisions follows the hierarchy published on Research — a mapping grade is not a claim of discoverability weight.
- T1AuthoritativeTypical evidenceRegulators, registries, certifications, signed client evidence.Scoring useHighest evidentiary weight
- T2IndependentTypical evidenceCredible media, industry bodies, partner directories, third-party datasets.Scoring useStrong with attribution and recency
- T3Provider-controlledTypical evidenceCompany site, case studies, press releases, structured submissions.Scoring useAccepted as a claim; verification raises weight
- T4Discovery-onlyTypical evidenceDirectory listings, aggregations, and early discovery signals.Scoring useUsed for discovery; weighted accordingly in ranking
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- Primary category
- LLM Agents & RAG
- Primary industry
- Technology, Software & Digital Infrastructure
- Typical budget
- Not disclosed
- Team size
- Not disclosed
- 1Name the use caseSay which LLM Agents & RAG problem you are evaluating, so the research team can route your message accurately.
- 2Pressure-test the budget bandNo budget band is on file. Ask what a scope like yours typically lands at.
- 3Ask for a referenceRequest a client reference in Technology, Software & Digital Infrastructure — the strongest industry signal on this profile.
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