AI Training Data

Multi-Modal Training Data Built for AI.

App Store + Google Play full app data × Web-wide text / image / video extraction = High-quality, structured, model-ready training data pipeline.

Not a scraping tool — an AI data supply chain.

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5M+ apps covered · 50+ data sources · Multi-modal output

5M+

apps across App Store & Google Play

100+

global data sources (app stores + web)

modalities: Text / Image / Video

JSON

Structured output, pipeline-ready for training

AntsData App Store Data+Web Extraction=Training Data as a Service

Fuse the world's largest app ecosystem data with multi-modal web extraction, delivering end-to-end data supply for AI model training, fine-tuning, and evaluation.

Data Supply Chain

From data source to model training — full pipeline

Not just raw files — structured data streams ready to enter your training pipeline.

Data Source Layer

AntsData's proprietary App Store + Google Play full database + web-wide extraction from 50+ sources (social media, e-commerce, job boards, news, forums, etc.).

Raw data streams

Collection & Cleaning

Multi-modal extraction: text, HD images, videos. Auto-dedup, denoise, PII redaction. Language detection & classification tagging.

Cleaned data

Structuring

Custom schema based on AI training needs. Auto-annotation (NER, sentiment, classification). Multi-language alignment & translation.

Annotated data

Delivery

API pull / batch export / Webhook push. Integration with HuggingFace / PyTorch / LangChain. Data versioning & incremental updates.

JSON / JSONL / Parquet CSV / Custom

Two Core Pillars

From data source to model training — full pipeline

Standardized JSON output with full training-data pipeline coverage.

AntsData App Store Data

1: Coverage

5M+ apps

App Store (175+ countries) · Google Play (195+ countries)

2: App metadata

Full coverage

App name, description, category, keywords, version, update time, developer info, bundle ID, price

3: Reviews

Billions

Full review text, rating, version, date, developer reply, likes, language

4: Rankings

Daily updates

Download ranking, category ranking, search ranking, historical ranking trends

5: Downloads & revenue

History + real-time

Estimated downloads, estimated revenue, install ranges

6: App relations

Relation graph

Similar apps, same-developer apps, competitor mapping, user overlap analysis

7: App media

Multi-modal

App icons, screenshots, preview videos, promotional assets

Web Multi-Modal Extraction

1: Text

50+ sources

Web page text, comments, posts, Q&A, news, forum discussions, social media content

2: Image

All formats

HD original image direct links, image metadata (size/format), batch download, watermark removal

3: Video

Multi-platform

Video direct links (MP4), thumbnails, subtitles/danmaku, description, comments, resolution info

4: Anti-bot

99%+ success

Built-in residential proxy, TLS fingerprint spoofing, browser-level request simulation, auto CAPTCHA handling

5: Structured extraction

Adaptive

AI-driven content extraction, auto page structure recognition, clean JSON output, no parsing rules needed

6: Real-time & incremental

Automated

Scheduled polling, incremental collection, change detection, URL dedup, data versioning

AI Training Scenarios

Data for every AI training need

Whether it's pre-training corpus, fine-tuning datasets, or evaluation benchmarks — precise data supply.

LLM Pre-training

Massive multilingual text corpus: app descriptions, user reviews, social media posts, news, forum discussions. 100+ languages, deduped and PII-redacted, ready for pre-training.

JSONL text stream
{language, text, source, timestamp}

Instruction Fine-tuning / SFT

Build high-quality instruction-response pairs from Q&A in app reviews and user feedback paired with developer replies. Support custom formats (Alpaca / ShareGPT / OpenAI function calling).

ShareGPT format
{conversations: [{role, content}]}

Sentiment Analysis

Billions of app reviews with built-in star rating labels. Multilingual, multi-category, multi-time-span. Naturally labeled dataset — no manual annotation needed.

{text, rating, language, category, app_id, version, date}

Multi-Modal Models

App screenshots + description pairs for UI understanding, image-text matching, visual QA. App icons + category labels for image classification. Preview videos + descriptions for video understanding.

{image_url, text, category, language}
{video_url, caption, metadata}

Recommendation Systems

App similarity graphs, user behavior signals (reviews/ratings/download trends), category mapping. For app recommendation, content recommendation, user interest modeling.

{app_id, similar_apps, user_signals, category_embedding}

RAG Knowledge Base

Structured app knowledge graph: app info → review summaries → competitor comparison → industry trends. Give AI agents real-time, accurate app ecosystem knowledge.

Vector-ready chunks
{content, metadata, source, embedding}

Evaluation Benchmarks

Build eval sets by task type: code generation (from app API docs), multilingual understanding (from multilingual reviews), fact-checking (from app metadata).

{prompt, expected_output, metric, difficulty}

Agent Tool Use

App store operation sequence data: search → filter → compare → download decision. Train AI agents to use app store related tools and APIs.

{steps: [{action, params, result}], goal, success}

Data Fields Detail

App Store Data — Full Field Coverage

App Basics

name / bundle_id / developer / category / sub_category / description (short+long) / keyword_tags

Text classification, app description generation

Reviews & Ratings

review_text / rating / app_version / date / language / developer_reply / helpful_count / verified

Sentiment analysis / SFT, dialogue generation

Rankings & Trends

download_rank / category_rank / search_rank / historical_rank_series / rank_change_rate

Time-series forecasting, trend models

Downloads & Revenue

est_downloads / est_revenue / install_range / download_trend_series

Business forecasting, market sizing

Competitor Graph

similar_apps / same_developer_apps / competitor_map / user_overlap

Graph neural networks, recsys

Media Assets

icon_url / screenshot_urls / preview_video_url / promotional_text / asset_dimensions

Multi-modal training, image-text pairs

Version & Updates

version / update_date / release_notes / version_diff

Change detection, code evolution analysis

Permissions & Safety

permissions / privacy_labels / data_safety / tracking_policy

Compliance analysis, privacy classification

Customer Scenarios

Who uses AntsData to train AI?

From foundation model companies to vertical app developers — a data supply chain for every scenario.

Foundation Model Co.

Massive multilingual pre-training corpus. App store reviews + web text = high-quality, multi-domain, multilingual training data.

AI App Developer

Fine-tune vertical models: app store analysis assistant, ASO optimization AI, competitive analysis agent. Precise app ecosystem data for SFT.

Multi-Modal AI Co.

Train UI understanding, image-text matching, visual QA. App screenshots + descriptions + icons + categories = natural multi-modal paired dataset.

Quant / Hedge Fund

Build alternative data factors: app download trends → revenue forecasts → stock signals. Structured, frequently updated app market data.

Market Research

Generate industry reports, competitive analysis, user insights. Auto-summarize millions of reviews to extract product improvement directions.

ASO / Marketing Tools

Train ASO models: keyword effect prediction, description generation, screenshot A/B test evaluation. Massive app store metadata for training.

Academic Research

Study mobile ecosystem evolution, user behavior patterns, cross-cultural differences. Large-scale, long-period, multi-region app datasets.

Data Labeling Co.

Use AntsData's semi-structured data as a pre-annotation foundation, significantly reducing manual labeling cost and improving efficiency.

Data Quality Assurance

For AI training data, quality is the lifeline

Completeness

>95% fields

Full coverage of all public apps on App Store + Google Play. Field coverage > 95%, missing fields auto-flagged.

Accuracy

>99%

Multi-source cross-validation (app store + official site + third-party). Automated anomaly detection flags suspicious data points.

Deduplication

<2% dup

URL + content hash dual dedup. Semantic dedup auto-merges near-duplicate reviews / descriptions.

Compliance

100% PII

Auto PII redaction (name, email, phone, address). GDPR / CCPA compliant. Only public data, no login state involved.

Freshness

<24h

App metadata daily incremental update. Review data real-time collection (Webhook push). Ranking data hourly refresh.

Format Consistency

100% schema

Unified JSON Schema output. Multi-language UTF-8 encoding standardization. Timestamps unified to ISO 8601.

Service Models

Flexible data delivery options

Standard API

Pay-per-call, real-time data access. Python / Node.js SDKs. Pay per request — use what you pay for.

AI app developers · SMB teams

Batch Data Pack

One-time delivery of large-scale datasets. Customized by category / region / time range. Parquet / JSONL / CSV. Ideal for pre-training corpus procurement.

Foundation model cos · research

Continuous Data Stream

Webhook real-time push + scheduled batch sync. Incremental updates, only new data transferred. Versioning with lookback. For continuous training.

Quant funds · ASO tools

On-Premise

Deploy to the customer's private cloud / VPC. Data stays within the network boundary. Custom collection frequency and concurrency. For finance & government compliance.

Large enterprises · regulated industries

FAQ

Frequently Asked Questions

Covers 5M+ apps across App Store (175+ countries) and Google Play (195+ countries). Includes full fields: app metadata, reviews, rankings, download estimates, media assets. Data updated daily.

Feed your AI model with high-quality training data

5M+ app data × web-wide multi-modal extraction = the training data you need, one click away.

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