Provides an auto-documentation platform for AI/ML models that integrates with existing tools like Python, MLFlow, and cloud platforms to automatically log model lineage, generate compliance-ready documentation, and maintain audit trails. This reduces documentation time by 90%, accelerates model production by 25%, and ensures regulatory adherence through real-time governance and traceability.
Funding
$15.6M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
SVFounders
Product
Problem
AI/ML model development and deployment often lack adequate documentation, making it difficult to track model lineage, ensure compliance, and facilitate collaboration. The manual effort required for comprehensive documentation is time-consuming, error-prone, and struggles to keep pace with rapid model iteration. This lack of transparency and traceability increases the risk of regulatory violations and hinders effective model risk management.
Solution
Vectice provides a Regulatory MLOps platform that automates the generation of AI/ML model documentation throughout the model lifecycle. The platform integrates with existing data science tools and platforms to continuously capture model lineage, dependencies, and performance metrics. By automating documentation, Vectice enables data science and model risk management teams to streamline compliance workflows, accelerate model validation, and improve collaboration. The platform's features include automated model dependency mapping, documentation co-pilot for generating first drafts, and project governance tools to ensure adherence to regulatory standards.
Target Audience
Vectice targets AI/ML developers, model validators, data scientists, and governance teams within financial services, regulated industries, and enterprises building AI/ML models.
Features
- Automated logging of AI assets, including model lineage, model cards, and datasheets
- Auto-generation of documentation based on model metadata
- Model dependency mapping to visualize relationships between models, data, and code
- Documentation co-pilot to automatically create first drafts of model documentation
- Flex connector for seamless integration with existing tools, frameworks, and platforms
- Project governance features to ensure adherence to regulatory and internal governance standards
- Customizable Model Development Document (MDD) templates library based on SS1/23, SR 11-7, and EU AI ACT
- Integration with MLOps platforms like MLflow, Vertex AI, SageMaker, and Azure ML
- Support for data platforms like Snowflake, Amazon S3, Databricks, and Google Cloud Platform