0to60.ai is an open‑source MLOps platform that automates data de‑identification, synthetic data generation, and transformation through a prompt‑to‑code interface, producing version‑controlled, auditable pipelines. It also includes model‑training assistance, RAG‑enabled knowledge‑base access, and flexible deployment options (SaaS, on‑prem, VPC) to help regulated enterprises prepare compliant AI‑ready data and accelerate trustworthy model delivery.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises struggle to operationalize AI because data preparation, privacy compliance, and governance are fragmented, time‑consuming, and require deep technical expertise. Without a unified, auditable pipeline, organizations face regulatory risk, biased models, and delayed model deployment.
Solution
0to60.ai provides an end‑to‑end, open‑source MLOps platform that automates data de‑identification, synthetic data generation, and transformation through a prompt‑to‑code interface. Users describe data cleaning, enrichment, or privacy rules in natural language, and the system generates version‑controlled pipelines with built‑in validation, lineage tracking, and role‑based access controls. The platform also offers managed SaaS, on‑premise, or VPC deployment options, enabling flexible integration with existing infrastructure while avoiding vendor lock‑in. Integrated model training tools identify optimal algorithms and manage compute resources, and a RAG‑enabled knowledge base leverages open‑source LLMs for fast, governed retrieval. Together, these capabilities reduce data‑prep effort, ensure compliance, and accelerate trustworthy AI delivery across the enterprise.
Target Audience
Primary customers are large enterprises in regulated sectors—such as insurance, healthcare, finance, and supply chain—that need governed, AI‑ready data pipelines and compliant model development, as well as their data science and engineering teams.
Features
- Prompt‑to‑code transformation layer that converts natural‑language specifications into production‑ready ETL code, YAML configs, and validation rules
- Automated data validation framework with null detection, range checks, referential integrity, and outlier detection, all logged for audit
- Synthetic data generation using GANs and Transformers that preserves inter‑column relationships while de‑identifying PII and reducing bias
- Built‑in privacy handling suite offering encryption, tokenization, redaction, and column‑level masking configurable per use‑case
- Model training assistant that recommends optimal algorithms, hyper‑parameters, and manages compute resources during training and evaluation
- Retrieval‑augmented generation (RAG) layer with fine‑tuned open‑source LLMs (e.g., Llama2, Phi‑2) for scalable knowledge‑base access and customizable retrieval strategies
- Modular architecture supporting cloud SaaS, on‑premise, and VPC deployments with UI, API, and chat‑based orchestration for pipeline scheduling, monitoring, and execution
- Full lineage tracking, version control, and role‑based access controls to meet regulatory requirements such as GDPR, CCPA, HIPAA, and industry‑specific AI fairness mandates