Use H2O Generative AI to:
- Inspect legal documents and find missing information
- Analyze financial health, risk or sentiment
- Do routine checks to find inconsistencies in documents
- Generate new content for support responses
- Standardize legal contracts or generate new content
- Summarize complaints and generate escalations
H2O Diff Tool coming soon to h2oGPTe
RAG utilizing VectorDB, embeddings and LLM
Track usage and control cost
Automatically creates descriptions of collections
Toggle between PDF preview and Chunk Text view
Control every aspect of the LLM
Get detailed information about usage and cost for each query
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Built on open source models
We make open source models enterprise ready
We’ve fine-tuned open source models to perform faster and more accurately than any other open source models available in the market today.
We have built-in GuardRails, or choose the GuardRail of your choice.
We’ve designed and developed the world’s best LLM-powered assistant that is grounded in your internal trusted knowledge base.
We made it easy to deploy, install and upgrade. We take the pain out of fine-tuning, testing, deploying and updating foundational models and algorithms with our API library of connectors and pre-built integrations.
Size matters
We understand that size matters. That's why we offer the smallest operational footprint possible, running on the GPUs you already have. With our retrieval augmentation generation (RAG) technology, you can seamlessly integrate our models into your existing data store.
We give customers total control and customization over their AI models. This level of customization and control is unmatched by anything in the market today.
We offer 13b, 34b or 70b Llama2 models that are up to 100x times smaller and more affordable while maintaining human-level accuracy. We deliver the best open source models that efficiently accomplish tasks at a fraction of the cost to run and operate.
OSS models are more transparent
According to the 2023 Foundation Model Transparency Index by Stanford University Center for Research on Foundation Models:
Open models lead the way: two of the three open models score greater than the best closed model.
Create your own large language models, build enterprise-grade GenAI solutions with the H2O LLM Studio Suite
H2O LLM Studio was created by our top Kaggle Grandmasters and provides organizations with a no-code fine-tuning framework to make their own custom state-of-the-art LLMs for enterprise applications.
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Curate
H2O LLM Data Studio -
Prep
H2O LLM Data Studio -
Fine Tune
H2O LLM Studio -
Evaluate
H2O LLM Eval Studio -
Sketch It
H2O LLM App Studio
Convert your unstructured data (documents, audio, files) to Q:A pairs for LLM fine-tuning
Prepare and clean your data for LLM fine-tuning and other downstream tasks
Fine-tune state of the art large language models using LLM Studio, a no-code GUI framework
Get a custom leader board comparing high-performing LLMs and choose the best model for your specific task
Create LLM-powered AI applications as fast as you can sketch it using LLM App Studio!
Complaint Summarizer
BUSINESS PROBLEMS
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NEEDS
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- RAG workflow
- Complaints by state
- Summarizer + topic extractor
- Summary + recommendation
- LLM Agent
RAG workflow
RAG (Retrieval-Augmented Generation) workflow for summarizing customer complaints across various categories and products.
Complaint distribution by state
Displays the breakdown of complaints by state, highlighting the most frequent issues like credit cards, debt collection, and loan services.
Summary of complaints and key issues
Provides a high-level summary of the most common complaints, the top issues faced, and recommended actions to address these concerns.
Actionable insights from complaint summaries
Summarizes key topics from complaints and proposes actionable recommendations for improving services.
End-to-end LLM-powered complaint agent
Showcases the comprehensive AI-driven process from voice-to-text conversion, complaint decision-making, and complaint summarization with proposed actionable recommendations for improving services.
H2OVL Mississippi SVLM Series
Our newest economical multimodal OCR model developed for Document AI
H2OVL Mississippi-2B, based on H2O Danube2, is trained on 17.3M conversation pairs for high-res image handling. The .8B model, built on Danube3, leads OCR benchmarks with 19M pairs, outperforming all SLMs in text recognition.
H2O Danube SLM Series
Our most economical small model for fast, lightweight tasks
We trained H2O Danube3 models from scratch on ~100 H100 GPUs using our own curated dataset of 6T tokens. H2O Danube3-4B and .5B open-weight SLMs outperform the latest Apple OpenELM-3B and .5B instruct models.
Perfect for developers who want to fine-tune their own SLMs for offline use cases.
H2OVL Mississippi SVLM Series
Our newest economical multimodal OCR model developed for Document AI
H2OVL Mississippi-2B, based on H2O Danube2, is trained on 17.3M conversation pairs for high-res image handling. The .8B model, built on Danube3, leads OCR benchmarks with 19M pairs, outperforming all SLMs in text recognition.
H2O Danube SLM Series
Our most economical small model for fast, lightweight tasks
We trained H2O Danube3 models from scratch on ~100 H100 GPUs using our own curated dataset of 6T tokens. H2O Danube3-4B and .5B open-weight SLMs outperform the latest Apple OpenELM-3B and .5B instruct models.
Perfect for developers who want to fine-tune their own SLMs for offline use cases.
H2OVL Mississippi SVLM Series
Our newest economical multimodal OCR model developed for Document AI
H2OVL Mississippi-2B, based on H2O Danube2, is trained on 17.3M conversation pairs for high-res image handling. The .8B model, built on Danube3, leads OCR benchmarks with 19M pairs, outperforming all SLMs in text recognition.
H2O Danube SLM Series
Our most economical small model for fast, lightweight tasks
We trained H2O Danube3 models from scratch on ~100 H100 GPUs using our own curated dataset of 6T tokens. H2O Danube3-4B and .5B open-weight SLMs outperform the latest Apple OpenELM-3B and .5B instruct models.
Perfect for developers who want to fine-tune their own SLMs for offline use cases.
H2O Danube SLM Series
We trained H2O Danube3 models from scratch on ~100 H100 GPUs using our own curated dataset of 6T tokens. H2O Danube3-4B and .5B open-weight SLMs outperform the latest Apple OpenELM-3B and .5B instruct models.
Perfect for developers who want to fine-tune their own SLMs for offline use cases.
Agus Sudjianto
Senior Vice President, Risk and Technology for Enterprise Services
Use H2O Generative AI to answer questions about your predictive data
“Tell me which accounts are going to churn next quarter and why?”
“Why are my Bay Area retail stores performing better than my East coast stores?”
“Why are my customers not paying in time?”
Becoming a customer obsessed AI-driven organization
Real Time Fraud Detection
Bills Sense
Benefits Finder
Natural Disaster Support
Transaction Abuse Monitoring
In the news
FRAUD & COMPLIANCE
Transaction Fraud Detection
Transaction Abuse Detection
Anti Money Laundering
Scam Detection
CUSTOMER
360
KYC.ai
Customer Behavior
Analysis
Customer Churn
Modeling
Customer Issue Categorization
FRAUD & COMPLIANCE
Transaction Fraud Detection
Transaction Abuse Detection
Anti Money Laundering
Scam Detection
FRAUD & COMPLIANCE
Transaction Fraud Detection
Transaction Abuse Detection
Anti Money Laundering
Scam Detection
FRAUD & COMPLIANCE
Transaction Fraud Detection
Transaction Abuse Detection
Anti Money Laundering
Scam Detection
UPCOMING EVENT
February 29 , 2024 | The Pavilion at Ronald Reagan Building and International Trade Center, Washington DC
Agenda
8:30-9:00 am
Registration
9:00-9:30 am
Keynote
Sri Ambati, CEO & Founder, H2O.ai
9:30-11:30 am
Introduction to Enterprise h2oGPTe, LLM Studio and GenAI App Store
Hands-On Advanced LLM Workshop, Training, and Certification
11:30 am-12:00 pm
EvalStudio Benchmarking
Srinivas Neppalli, Sr. AI Engineer, H2O.ai
John McKinney, Director of Research, H2O.ai
12:00-12:30 pm
GenAI Interpretability
Kim Montgomery, KGM + Sr. AI Engineer, H2O.ai
1:00-2:00 pm
Lunch Break
3:00-4:00 pm
Industry Panel on GenAI Governance and Model Validation
Discuss lessons learned on banking regulations for fintech.
Moderator: Elizabeth Mays, Chief Model Risk Officer, PNC
Bradley Currell, Financial Model Risk Executive, Ally
Jacob Kosoff, Data Science & Model Development Executive, Bank of America
Tarun Joshi, Quantitative Analytics Manager, Wells Fargo
4:00-5:00 pm
Fireside Chat with US Regulators
Moderator: Dr. Agus Sudjianto, Wells Fargo
David Palmer, Board of Governors of the Federal Reserve
Loren Bushkar, Board of Governors of the Federal Reserve
Neil Desai, Business Finance Senior Analyst, Federal Reserve
5:00-5:30 pm
What Responsible AI Means for Financial Services
Doug Hague, Executive Director, School of Data Science at University of North Carolina
5:30-6:00 pm
Closing
6:00-7:00 pm
Networking Happy Hour
H2O GenAI App Store
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Democratizing Generative AI
Own your models: generative and predictive. We bring both super powers together with h2oGPT.
Democratizing
Generative AI
Own your models: generative and
predictive. We bring both super powers
together with h2oGPT.
Speakers
Sri Ambati
CEO & Founder,
H2O.ai
Dr. Agus Sudjianto
EVP, Head of Corporate Model Risk, Wells Fargo
Kim Montgomery
Kaggle Grandmaster + Sr. AI Engineer, H2O.ai
Doug Hague
Executive Director - School of Data Science at University of North Carolina
Elizabeth Mays
Chief Model Risk Officer, PNC
Srinivas Neppalli
Sr. AI Engineer, H2O.ai
Yury Korsky
US Head Model Risk, Barclays
Megan Kurka
Customer Data Scientist, H2O.ai
Jacob Kosoff
Data Science & Model Development Executive, Bank of America
Bradley Curell
Financial Model Risk Executive, Ally
Neil Desai
Board of Governors of the Federal Reserve
Jon McKinney
Director of Research, H2O.ai
Loren Bushkar
Board of Governors of the Federal Reserve
Jacob Kosoff
Data Science & Model Development Executive, Bank of America
Tarun Joshi
Quantitative Analytics Manager, Wells Fargo
David Palmer
Board of Governors of the Federal Reserve