Every company will need
synthetic data.
We're building the engine that makes it indistinguishable from the real thing.
Testing without production data. Demos that actually look real. ML training sets that preserve statistical properties. Privacy compliance without losing signal. The synthetic data problem is universal — and currently unsolved.
A $1.8 billion market.
Zero good options.
Every approach to synthetic data is fundamentally broken in a different way.
Enterprise Tools
Tonic.ai, Gretel, Mostly AI
Cost $50K-$500K/year. Black-box ML models. 6-month sales cycles. No transparency.
Open-Source Tools
Faker, SDV, Synthetic Data Vault
Generate random noise, not data. order.total ≠ subtotal + tax. No relational awareness.
LLM Approaches
GPT-4 prompting, fine-tuned models
10 rows/minute. Hallucinated schemas. $100+ per dataset. No deterministic reproducibility.
Three pillars.
One engine.
Intelligence
LLMs understand business logic, not just column types
Describe 'e-commerce with seasonal trends and customer segments' — Misata understands the business semantics, not just VARCHAR(255).
Performance
Vectorized NumPy, not row-by-row Python loops
385,000 rows/second. Entire columns generated at once. Comparable to database bulk inserts, not Python iteration.
Accessibility
Visual schema designer, not YAML configs
Misata Studio: drag-and-drop schema design. Click 'Generate'. Download CSVs. No code required for non-engineers.
Where we're headed
From open-source CLI to enterprise platform. Built transparently, shipped iteratively.
Foundation
- Open-source CLI (pip install misata)
- LLM-powered schema generation (Groq, OpenAI)
- Realism Engine v0.5 — column-aware generation
- Visual Schema Studio (drag & drop)
- 3-tier hybrid data enrichment (Heuristics + LLM + Kaggle)
- SQLAlchemy direct database seeding
Privacy & Compliance
- PII detection and automatic masking
- Differential privacy (ε-budgets per column)
- k-Anonymity enforcement for sensitive fields
- HIPAA / GDPR compliance templates
- Audit logging for SOC2 attestation
Enterprise Platform
- Team workspaces with RBAC
- Schema versioning and collaboration
- REST API for programmatic generation
- CI/CD integration (GitHub Actions, GitLab CI)
- Docker + Kubernetes deployment
- Custom LLM model fine-tuning per org
Data Intelligence
- Data marketplace — share and discover schemas
- Industry-specific foundation models (healthcare, fintech, logistics)
- Time-series and event-stream generation
- Distributed generation for billion-row datasets
- Real-time synthetic data streaming
- Reverse ETL — generate data where your tools already live
We can't do this alone.
And we don't want to.
Misata is MIT-licensed and community-driven. Here's how you can shape the future of synthetic data.
Engineers
Build the core engine, streaming pipelines, and visual schema designer. Python, TypeScript, React Flow.
Browse open issuesData Scientists
Design statistical distributions, privacy algorithms, and realism benchmarks. NumPy, pandas, differential privacy.
Read the architectureInvestors
We're building the synthetic data infrastructure for every development team. The market is $1.8B and growing 35% YoY.
Let's talkDesign Partners
Get early access to enterprise features. Shape the product roadmap. Your use cases become our test cases.
Become a partnerIf you build data-centric products: you can now generate realistic demos for every customer & QA use case in minutes. This is one of our highest ROI use cases right now. Synthetic data used to be brutal... I tried Tonic.ai, Faker, dozens of other open-source and early LLM workflows. They worked, but the cost and time to get usable data was insane.
The best time to join
is right now.
We're pre-seed, open-source, and moving fast. Every contributor and design partner today shapes what this becomes tomorrow.