h2oGPTe Agentic AI converges generative AI and predictive with purpose-built SLMs

Document AI with multimodal guided JSON generation

h2oGPTe enables grounded query responses from secure, private data sources, including document repositories, knowledge bases, and databases, using advanced vector embeddings and proprietary techniques to mitigate hallucinations and enhance AI reliability. 

Built-in Document AI supports schema-driven JSON generation—ideal for contract summarization, compliance metric extraction, and audit-ready data structuring. Over a dozen specialized models power this pipeline, delivering highly accurate, structured outputs directly from your source documents, tailored to complex enterprise workflows.

Multimodal audio & vision analysis

h2oGPTe’s Audio and Vision Models extract structured data from diverse media, including audio files, images, flowcharts, and handwritten documents—ideal for visual-heavy fields where insights are embedded in diagrams and tables. 

Audio models transcribe and translate recordings in dozens of languages, while vision models enable autonomous content verification by AI agents.

Coding assistant. Rapid prototyping & development.

h2oGPTe's Coding Assistant helps developers quickly prototype ideas by generating starter code and scaffolding for new projects. It provides basic code completion and documentation, helping teams move from concept to working prototype faster. The assistant supports common programming languages and can suggest simple optimizations during development.

Autonomous agentic AI: execute multi-step workflows autonomously

h2oGPTe Agents bring true autonomy to your workflows, enabling LLMs to handle multi-step tasks like web research, data modeling, database access, and iterative code execution. Programmatic and continuous, these agents reduce manual workload by executing tasks requiring sequential logic, real-time decision-making, and data handling. 

h2oGPTe Agents can autonomously generate multi-page PDFs with charts, tables, and flowcharts based on real-time data—complete with source code for full transparency.

Citation-based verification for transparent Retrieval Augmented Generation (RAG)

State-of-the-art multimodal RAG with built-in citation support offers comprehensive traceability for AI-generated responses, with embedded document references that enhance transparency. This feature is ideal for audit-heavy sectors, ensuring each AI response is both accurate and verifiable.

Customizable guardrails for AI safety

h2oGPTe’s Guardrails and PII controls offer fine-grained access management and scoped response restrictions, enabling precise control over input and output boundaries. 

These customizable safeguards mitigate risks in sensitive environments, prevent unauthorized access, and ensure AI responses comply with enterprise policies and ethical standards.

Intelligent model routing: optimal model selection for every task

h2oGPTe's Intelligent Model Routing dynamically directs queries to the most suitable LLM based on real-time assessments of computational cost, latency, and accuracy. 

This system ensures each request is matched with the ideal model architecture, maximizing efficiency and performance across large-scale tasks.

Model risk management for enhanced compliance and interpretability

Transparent assessments with embedding and ML-driven evaluators

Embedding-based metrics complemented with Natural Language Inference provide transparent, explainable and objective model assessments to enhance accountability and clarity.

Calibrated metrics with human feedback

Incorporating sampling of human feedbacks calibrate automated metrics, enabling efficient and trustworthy evaluations crucial for high-stakes applications.

Robust testing through automated question generation

Automated question generation facilitates comprehensive testing to identify model vulnerabilities and improve reliability.

Rapid diagnostics with visual insights

Visualizations to enable quick identification of patterns and weaknesses, supporting efficient diagnostics and model improvement.