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SGP provides a comprehensive suite of capabilities for building, deploying, and managing AI applications at scale. Our platform combines powerful tools for development, evaluation, and production deployment.

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📄 Document Understanding

Advanced document processing and information extraction capabilities. Learn more about Document Understanding → Key Features:
  • Universal Format Support: PDFs, images, spreadsheets, presentations
  • Intelligent Parsing: Convert documents to structured JSON
  • Custom Extraction: Define schemas for specific data needs
  • High Accuracy OCR: Advanced text recognition and extraction
  • Project Management: Secure, isolated document processing

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⚙️ Workflows

AI-powered low-code workflow builder that enables enterprise operators to import data, transform it, call LLMs or Agents, analyze or evaluate the output, & automate the entire workflow. Learn more about Workflows → Key Features:
  • Importing data: Read data from various sources like the agent traces, CSVs, or queryable data sources.
  • Transforming data: Transform your data using custom Python code or simple functions.
  • Call LLM or Agents: Generate output and traces against input from each row of your dataset using LLMs or Agentex agents.
  • Analyze data: Evaluate your outputs using LLM as Judge and access the evaluation anytime on SGP.
  • Visualize data: Generate visualizations like charts & build custom dashboards.
  • Export the data: Export the data as a CSV or SGP dataset.
  • Automate & monitor: Schedule the workflow to periodically. Monitor logs & get alerts.

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Training

Train custom models on your own data without managing cloud infrastructure. Submit training jobs through a single API and Train handles the rest, whether your workload runs on GCP, AWS, or Azure. Learn more about Training Key Features:
  • Any cloud backend: Run jobs on Vertex AI, SageMaker, or Azure ML with a single API
  • Bring your own image: Push a Docker image once and reference it in any job
  • Portable data access: Mount GCS, S3, or Azure Blob Storage into your container using consistent environment variables
  • Simple job lifecycle: Track progress from submission to completion with a consistent status model across all backends