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Synthetic Data API Overview

What Is Synthetic Data?

Synthetic data is artificially generated data that preserves the structure, distributions, and semantics of real-world data—without containing real or sensitive records.

The Anote Synthetic Data API and the anote-generate SDK allow developers to programmatically generate task-specific, structured synthetic datasets for training, testing, evaluation, and benchmarking AI systems.

The API is designed to be:

  • Simple: one generate() method

  • Flexible: supports multiple modalities

  • Human-centered: compatible with validation and annotation workflows

Why Use Synthetic Data?

Privacy & Compliance

  • No real user records or PII
  • Suitable for regulated domains (healthcare, finance, government)
  • Safe for sharing and early experimentation

Faster Iteration

  • Bootstrap models before real data exists
  • Avoid long data collection and labeling cycles
  • Generate data on demand for rapid prototyping

Control & Coverage

  • Enforce class balance and schema consistency
  • Generate rare or edge cases intentionally
  • Create repeatable datasets for benchmarking

Core Concepts

Task-Driven Generation

Each request is scoped by a task type: 

"text" | "image" | "video" | "audio" | "agent" | "reasoning"

This ensures outputs are aligned with downstream model usage.

Schema-First Outputs

You define dataset columns up front: 

columns=["input", "label", "explanation"]

The API guarantees structured outputs, returned as JSON or downloadable CSV.

Few-Shot Control (Optional)

Provide a small set of examples to guide style and format, labeling behavior, and reasoning depth

Supported Modalities

Text

  • Classification
  • Entity extraction
  • Question–answering
  • Document understanding

Image

  • Classification
  • Detection
  • Segmentation

Video

  • Action recognition
  • Object tracking
  • Scenario simulation

Audio

  • Speech recognition
  • Audio classification
  • Emotion or speaker analysis

Agent & Reasoning

  • Multi-step reasoning traces
  • Tool-using agents
  • Workflow and automation scenarios

SDK at a Glance

from anotegenerate.core import AnoteGenerate

sdk = AnoteGenerate(api_key="YOUR_API_KEY")

data = sdk.generate(
    task_type="text",
    columns=["question", "answer"],
    prompt="Generate Python interview Q&A",
    num_rows=100,
    examples=[
        {"question": "What is a list comprehension?", "answer": "..."}
    ]
)

Integration with Anote

Synthetic datasets can be imported into Anote projects and combined with:

  • Human annotation

  • Model fine-tuning

  • Evaluation and benchmarking

  • Dataset and model versioning