Synthetic Generation Engine

Generate unlimited data
with zero privacy risk

Our proprietary synthetic generation platform creates training-grade datasets across any modality — tailored to your exact distributions, edge cases, and annotation schemas.

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Data Type
Text / NLPImageTabularAudioMultimodal
Task Format
Instruction FollowingQ&A PairsConversationReasoning Chain
Languages
EnglishMalayMandarinSpanishArabic+ 89 more
Volume: 500,000 samples
500K
Diversity Score
LowMediumHighMaximum
live_preview.jsonl
User: Explain the concept of retrieval-augmented generation.
Assistant: RAG combines a language model with an external knowledge retrieval system. When a query arrives, relevant documents are fetched and injected into the model's context before generation...
User: Bagaimana cara membuat model AI yang lebih cekap?
Assistant: Untuk meningkatkan kecekapan model AI, anda boleh menggunakan teknik seperti kuantisasi, penambahan pengetahuan, dan penyaringan data latihan...
User: What are the main differences between RLHF and DPO?
Assistant: Both are alignment techniques. RLHF trains a separate reward model then uses PPO to optimize the policy, while DPO directly optimizes on preference pairs without a reward model...
Generation Types

Every modality, mastered

Dedicated generation engines built and optimized for each data type.

Text Generation
Conversations, instructions, reasoning chains, and document formats for any NLP task.
Persona-aware writing styles
94 language support
RLHF-ready preference pairs
Image Generation
Photorealistic and stylized imagery with pixel-perfect bounding boxes and segmentation.
Custom domain styles
Auto-annotation pipeline
Rare & edge-case scenes
Tabular Generation
Statistical-fidelity structured data — correlation-preserving, distribution-matched.
Schema-aware synthesis
Differential privacy mode
Class imbalance control
Multimodal Generation
Cross-modal aligned datasets — image-text, audio-text, and video-caption pairs.
Semantic alignment
Unified annotation schema
VLM & CLIP-ready
Why Synthetic?

Synthetic vs alternatives

See how synthetic data stacks up against traditional data collection methods.

FeatureDitosis SyntheticManual CollectionWeb ScrapingCrowdsourcing
Privacy Compliant✓ Always~ Depends✗ Risky~ Varies
Scalability✓ Unlimited✗ Bottlenecked~ Rate-limited✗ Costly
Edge Case Coverage✓ Configurable✗ Rare/costly✗ Hard to find~ Possible
Annotation Included✓ Auto✗ Manual cost✗ Extra step~ Inconsistent
Time to Delivery✓ Hours✗ Months~ Weeks✗ Weeks
Cost Efficiency✓ Predictable✗ Very high~ Medium✗ High
Bias Control✓ Configurable✗ Hard✗ Uncontrolled~ Limited
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