Skip to main content
Version: v1.6.40-dev2 🚧

Use cases (examples)

Overview​

This page provides an overview of real-world use cases for Enterprise h2oGPTe, a powerful AI-powered search and answer tool. The examples showcase actual customer implementations across industries and domains, demonstrating how Enterprise h2oGPTe streamlines information retrieval, support decision-making, and automate complex workflows.

The objective of this page is to help enterprises use their proprietary data securely and effectively, transitioning from AI consumers to AI creators, and achieving AI maturity and operational self-sufficiency.

By the end of this page, you will have a foundation for developing enterprise-grade AI applications tailored to your specific requirements and environments.

Try These Use Cases Live

Open Enterprise h2oGPTe Freemium in a new window and test the example prompts provided with each use case below.

Benefits of using Enterprise h2oGPTe​

Before diving into the use cases, it's important to recognize the key benefits that Enterprise h2oGPTe provides to organizations.

  1. Complete autonomy & sovereign AI: Enterprise h2oGPTe provides organizations with control over their AI deployments and data. This sovereign AI approach addresses enterprise concerns about data security, privacy, and intellectual property when using third-party AI models or cloud services. For organizations in highly regulated sectors or those handling sensitive information, sovereign AI offers:

    • Secure and customizable AI deployment
    • Enterprise-grade security, data ownership, and governance
    • Regulatory compliance, auditing, transparency, and accountability

  2. Creative productivity in the workplace: The Agentic AI capabilities of h2oGPTe help manage complex tasks with complete control over your configurations for your AI agents. It automates repetitive tasks and enables faster information retrieval, allowing teams in Human Resources, IT support, Sales, and Marketing to focus on essential activities.

  3. Accelerated insight discovery from your knowledge base: h2oGPTe uses multiple top-performing (in-house models, open-source, and best in market) generative AI models to understand complex datasets such as financial reports, balance sheets, documentation sites, and meeting transcripts. This enables faster insights extraction and contributes to more accurate decision-making.

  4. Strategic advantage through customization of LLMs: h2oGPTe provides options for configuring AI agents to meet specific requirements for data types, workflows, security, and compliance standards. This customization supports everything that's needed from airgapped environments to fine-tuned agent behaviors for specific enterprise needs.

    • Deploy in fully airgapped, hybrid, or cloud-native environments for maximum data privacy and compliance
    • Customize every aspect of AI agents, including prompt design, retrieval strategies, and model selection
    • Integrate with enterprise authentication, set granular access controls, and enable detailed audit logging
    • Orchestrate multi-step workflows and securely connect agents to internal APIs, databases, and business tools

  5. Comprehensive file type support: Enterprise h2oGPTe supports various document types for ingestion, including PDFs, text files, Word documents, Excel spreadsheets, PowerPoint presentations, HTML, and image formats. This allows organizations to use their existing data assets without complex conversions or preprocessing. Learn more about supported file types.

Use cases​

Enterprise h2oGPTe addresses real-world business challenges across industries and functions. In each use case below, you’ll see the specific problem organizations face, how h2oGPTe delivers a solution, and what sets our platform apart from other AI tools. For every scenario, we also provide example prompts you can try directly in the freemium version of h2oGPTe so you can experience the platform’s capabilities firsthand.

Use case 1: Financial document analysis & reporting​

Challenge: Financial analysts and risk managers have to extract insights, identify risks, and generate reports from complex financial documents, including regulatory filings such as 10-K and 10-Q reports, earnings call transcripts, equity research, and internal financial statements.

Solution with h2oGPTe:

  • Using advanced RAG strategies like Summary RAG for lengthy documents and HyDE + RAG for complex queries to perform deep semantic searches and extract precise information from financial formats such as PDFs and Excel spreadsheets.
  • Using Agentic AI capabilities to perform multi-step analyses, like comparing quarterly reports, identifying risk trends, and cross-referencing for consistency.
  • Using customizable prompts and LLM personality settings to tailor queries focused on specific financial metrics, ensuring outputs match internal reporting styles.
  • Using vision capabilities to extract and interpret data from charts and tables in financial reports, ensuring all critical information is captured.
  • Real-world example: Covestro used h2oGPTe and Driverless AI to automate ML model development and feature engineering, comparing results with traditional data science approaches for financial and operational reporting.

h2oGPTe Differentiators for this Use Case:

  • Sovereign AI Deployment

  • Granular Access Controls & Auditability

  • Custom RAG for Financial Nuance

Try These Queries in h2oGPTe:

"Analyze the risk factors in [COMPANY_NAME]'s [QUARTER] financial report and identify any significant changes from the previous quarter"

"What were the key revenue drivers for [COMPANY_NAME] in [YEAR] based on the annual report?"

"Compare the financial performance of [DEPARTMENT/PRODUCT] between [PERIOD_1] and [PERIOD_2]"

Use case 2: Compliance & regulatory search intelligence​

Challenge: Many organizations and institutions face the ongoing challenge of adapting to complex regulations from entities such as the SEC, FINRA, and the ECB. Aligning internal policies with these mandates and responding to regulatory inquiries demands substantial resources and meticulous attention to detail.

Solution with h2oGPTe:

  • Using Collections to create a dedicated database of regulatory texts (e.g., specific acts, circulars), internal policies, compliance guidelines, and historical rulings. These Collections serve as focused, trusted knowledge domains for targeted and intelligent search results.
  • Using extensive document sets, compliance officers and legal teams can use natural language queries to quickly find specific clauses, obligations, interpretations, or precedents.
  • Using Agentic AI features to monitor regulatory updates through APIs and automate cross-referencing new regulations against existing internal policies to identify gaps or required amendments.
  • Real-world example: Banco do Brasil developed a tool with h2oGPTe to automate legal document analysis, summarizing appeals and judgments, and supporting compliance teams in regulatory environments.

h2oGPTe Differentiators for this Use Case:

  • RAG Evaluation for Accuracy

  • Sovereign AI for Document Handling

  • Customizable Collections & Prompts

Try These Queries in h2oGPTe:

"What are the current [REGULATION_TYPE] reporting thresholds for [TRANSACTION_TYPE] exceeding [AMOUNT]?"

"Find all compliance requirements related to [SPECIFIC_REGULATION] for [BUSINESS_TYPE]"

"What are the penalties for non-compliance with [REGULATION_NAME] in [JURISDICTION]?"

Use case 3: Knowledge base review & website data synthesis​

Challenge: Researchers, scientists, and pharmaceutical clinicians are overwhelmed by the vast amount of journals, clinical trial data, patents, and internal documents. Manually extracting relevant information and synthesizing it into actionable insights is a significant bottleneck in the discovery and development process.

Solution with h2oGPTe:

  • Using Collections to create intelligently searchable storage of diverse literature papers, including PDFs, articles, and structured data from clinical trials or internal databases.
  • Using advanced RAG strategies helps the system to search through extensive collections of information and answer complex scientific questions. This means it can find and put together information from different sources to give complete answers.
  • Using Agentic AI for sophisticated research tasks, such as synthesizing information from multiple papers, identifying conflicting findings, tracing citation networks, and formulating novel hypotheses based on literature patterns.
  • Using multimodal capabilities to analyze and extract key information from figures, tables, charts, and diagrams in research papers, which often include essential experimental results or data summaries.

h2oGPTe Differentiators for this Use Case:

  • Handling Diverse Scientific Formats

  • Customization for Scientific Querying

  • Sovereign AI Environment for Proprietary Research

Try These Queries in h2oGPTe:

"What are the latest findings on the interaction between [GENE_X] and [PATHWAY_Y] in [DISEASE_Z] regarding [DRUG_CANDIDATE_A]'s mechanism?"

"Summarize the clinical trial results for [DRUG_NAME] in treating [CONDITION]"

"What are the current research trends in [RESEARCH_AREA] based on recent publications?"

Use case 4: Information technical (IT) support & network operations​

Challenge: Field technicians, NOC staff, and customer support agents need easy access to the correct information from technical manuals, equipment diagrams, troubleshooting guides, standard operating procedures (SOPs), and incident records to do their core operations.

Solution with h2oGPTe:

  • Using Collections to ingest all relevant technical documentation (manuals, guides, logs, best practices) to transform static documents into a dynamic, queryable resource.
  • Using natural language search to quickly find solutions to specific error codes, device configurations, or customer-reported problems.
  • Using Agentic AI features to guide users in multi-step troubleshooting procedures, suggest relevant diagnostic tests based on initial issues, and correlate current problems with historical incident patterns.
  • Using third party integrations with existing ticketing systems (e.g., ServiceNow, Jira), network monitoring tools, or customer support portals via APIs, users can create a seamless workflow, allowing h2oGPTe to access ticket information or populate solutions directly into their support interfaces.

h2oGPTe Differentiators for this Use Case:

  • Customizable RAG for Technical Content

  • Scalability for Large Document Sets

  • Integration Potential with Existing Operational Support Systems (OSS) and Business Support Systems (BSS)

Try These Queries in h2oGPTe:

"What are the troubleshooting steps for error code [ERROR_CODE] when accessing [SYSTEM_NAME]?"

"How do I configure [DEVICE_TYPE] for [SPECIFIC_FUNCTION] according to our technical documentation?"

"What is the standard operating procedure for [PROCESS_NAME] in case of [SCENARIO]?"

Use case 5: Enterprise-wide document & content discovery​

Challenge: Employees often waste time searching for information spread across various internal systems, shared drives, company intranets, public-facing websites, and a multitude of document formats, leading to lost productivity, duplicated efforts, and poor decision-making due to incomplete information.

Solution with h2oGPTe:

  • Using Collections that bring together documents from various repositories searches for data in a unified, intelligent search layer that spans all ingested enterprise content sources.
  • Using natural language queries to allow users to ask questions in their own words instead of relying on precise keywords or complex folder navigation.
  • Using h2oGPTe for a deep understanding of more relevant, precise, and ranked search results to provide direct answers or summaries instead of just document lists.
  • Using integrations with various file types, including PDF, Microsoft Word, Excel, PowerPoint, plain text, HTML, and images to maximize the value of existing information assets.
  • Real-world example: H2O.ai (Internal Engineering) uses h2oGPTe to manage and search engineering documentation and assets, improving internal knowledge discovery and productivity.

h2oGPTe Differentiators for this Use Case:

  • Comprehensive File Type Support

  • Robust Data Security & Permissions

  • Scalability for Enterprise Data Volumes

Try These Queries in h2oGPTe:

"What was our [METRIC] growth in the [REGION] region in [YEAR] compared to the previous year?"

"Find the latest [DOCUMENT_TYPE] for [DEPARTMENT] regarding [TOPIC]"

"Summarize all policies related to [POLICY_AREA] across our organization"

Use case 6: HR process automation & employee self-service empowerment​

Challenge: Human Resources departments are frequently inundated with repetitive employee queries regarding company policies, benefits, leave requests, payroll details, and onboarding procedures. Employees need quick, easy, and reliable access to accurate HR information without long wait times, and HR teams need to reduce manual workload and focus on strategic initiatives.

Solution with h2oGPTe:

  • Using Collections to create an AI-powered HR assistant by ingesting all relevant HR policies, employee handbooks, benefits documentation, FAQs, and internal process guides into a dedicated and secure knowledge base.
  • Providing a conversational interface (e.g., a chat window on the HR portal) where employees can ask questions in natural language 24/7 and receive instant, accurate responses.
  • Using prompt configurations and customizable LLM personality to automate responses to common queries, ensuring the HR assistant communicates in a tone that aligns with company culture (formal, friendly, or highly professional).
  • Integrating with HR systems to provide real-time information about benefits, leave balances, payroll, and policy updates.
  • Enabling self-service for HR forms, onboarding, and process guidance, reducing HR team workload and improving employee satisfaction.
  • Ensuring data privacy for sensitive HR information and supporting personally identifiable information (PII) redaction.
  • Real-world example: Singtel deployed an HR AI Assistant with h2oGPTe, supporting more than 14,000 employees with instant, accurate HR information and self-service capabilities.

h2oGPTe Differentiators for this Use Case:

  • Data Privacy for Sensitive HR Information
  • Customizable LLM Personality
  • Personally Identifiable Information (PII) Redaction
  • 24/7 Employee Self-Service
  • Consistent Policy Information
  • Reduced HR Workload

Try These Queries in h2oGPTe:

"What is the company policy on [LEAVE_TYPE] for [EMPLOYEE_CATEGORY]?"

"How do I submit an [EXPENSE_TYPE] report for [PURPOSE]?"

"Where can I find the form for updating my [BENEFIT_TYPE] beneficiaries?"

"How many vacation days do I have remaining and what is the process for requesting time off?"

"What are the current health insurance options and how do I update my coverage?"

Use case 7: Sales & marketing content strategy and operations​

Challenge: Marketing teams should analyze trends, create engaging content, tailor campaigns, and measure impact. Meanwhile, sales teams need quick access to updated product info, marketing materials, case studies, competitive insights, and pricing to engage prospects and close deals effectively.

Solution with h2oGPTe:

  • Using Collections to create a rich repository of all sales and marketing assets, including product brochures, technical specifications, presentations, white papers, customer testimonials, approved messaging, and competitive battle cards.
  • Using natural language search to quickly find the most relevant materials or brainstorm a new content idea based on best-performing posts in the past.
  • Using LLM customizations to summarize market research reports, create initial drafts for blog posts or articles, brainstorm social media updates, and develop outlines for email campaigns based on specific prompts, target audience profiles, and ingested data.
  • Using customer feedback from surveys, reviews, or support interactions (if ingested) to identify key themes, sentiments, and emerging needs for product development and marketing.
  • Real-world example: Singtel deployed a Sales AI Assistant with h2oGPTe, enabling sales staff to access up-to-date product and service information, improving customer engagement and satisfaction.

h2oGPTe Differentiators for this Use Case:

  • Customization for Brand Voice

  • Integration with Analytics

  • Control over Proprietary Marketing Data

Try These Queries in h2oGPTe:

"Find case studies for [PRODUCT_NAME] relevant to the [INDUSTRY] sector in [REGION]"

"What are some [PLATFORM] post ideas for [SOLUTION] compared to [COMPETITOR] for our [TARGET_AUDIENCE]?"

"Analyze customer feedback trends for [PRODUCT/SERVICE] in [TIME_PERIOD]"

Use case 8: Customer retention analysis & churn prevention​

Challenge: Businesses across industries face the challenge of customer churn, which impacts revenue and growth. They need to proactively identify at-risk customers, understand the drivers of dissatisfaction, and implement targeted retention strategies.

Solution with h2oGPTe:

  • Using Agentic AI capabilities to analyze diverse customer data, including demographics, purchase history, usage patterns, and support ticket interactions.
  • Using predictive models to score each customer's churn risk based on the analyzed data, identifying complex patterns that may not be obvious to human analysts.
  • Using personalized retention recommendations, such as targeted marketing offers, proactive support outreach, or feature tutorials for under-engaged users.
  • Using customizable prompts to simulate different retention scenarios and forecast the potential impact of various strategies on overall churn rates.

h2oGPTe Differentiators for this Use Case:

  • Sovereign AI Deployment for secure analysis of sensitive customer data

  • Agentic AI for multi-step analysis and proactive insights

  • Customizable LLM personalities to tailor communication and strategy

Try These Queries in h2oGPTe:

"Identify customers with declining usage patterns in [TIME_PERIOD] and suggest personalized retention strategies"

"What are the common reasons for customer churn in [SEGMENT] based on our support ticket data?"

"Analyze customer satisfaction scores for [PRODUCT/SERVICE] and recommend improvement areas"

Use case 9: Energy production & grid optimization​

Challenge: Energy producers and grid operators need to continuously optimize operations to maximize output, reduce costs, and ensure grid stability. This requires real-time analysis of sensor data from across their infrastructure and the ability to make rapid, data-driven adjustments.

Solution with h2oGPTe:

  • Using Agentic AI to monitor real-time data streams from sensors measuring temperature, pressure, humidity, and equipment performance.
  • Using time-series forecasting models to predict energy demand and renewable energy generation (e.g., solar, wind) with high accuracy.
  • Using operational data to analyze and identify anomalies, predict potential equipment failures, and recommend optimal parameters for turbines, transformers, and other critical assets.
  • Using reports and alerts to summarize complex operational states and provide clear, actionable recommendations for control room operators.

h2oGPTe Differentiators for this Use Case:

  • Real-time data processing and analysis at scale

  • Agentic AI for autonomous monitoring and operational recommendations

  • Integration with industrial data sources and IoT platforms

Try These Queries in h2oGPTe:

"Analyze sensor data from [EQUIPMENT_NAME] and recommend optimal operating parameters for maximum efficiency"

"What are the predicted energy demand patterns for [LOCATION] in [TIME_PERIOD]?"

"Identify potential equipment failures in [SYSTEM_NAME] based on historical maintenance data"

Use case 10: Social media management & content creation​

Challenge: Marketing teams are under constant pressure to create a steady stream of engaging content, monitor brand perception, and manage audience interactions across multiple social media platforms. Scaling these activities while maintaining a consistent brand voice is a significant operational burden.

Solution with h2oGPTe:

  • Using Agentic AI to monitor social media channels for brand mentions, keywords, and sentiment trends, providing a real-time pulse on audience perception.
  • Using LLM models to produce varied, high-quality content such as social media posts, blog articles, and advertising copy, achieved through straightforward prompts and adherence to brand guidelines.
  • Using customization options for language models to generate automated responses to frequently asked questions and common customer service inquiries, allowing team members to concentrate on more strategic interactions.
  • Using engagement metrics to identify top-performing content and provide data-driven recommendations to refine the social media strategy.

h2oGPTe Differentiators for this Use Case:

  • Custom RAG and prompts to maintain a consistent brand voice

  • Agentic AI for automating workflows like content scheduling and reporting

  • Vision capabilities to analyze image-based content and trends

Try These Queries in h2oGPTe:

"Generate [NUMBER] [PLATFORM] posts about our new [SOLUTION] for the [INDUSTRY] industry"

"Analyze sentiment trends for [BRAND/TOPIC] on [PLATFORM] in [TIME_PERIOD]"

"Create a content calendar for [CAMPAIGN_NAME] targeting [AUDIENCE] on [PLATFORMS]"

Use case 11: Agentic AI – autonomous workflow execution​

Challenge: Organizations need to automate complex, multi-step workflows that involve data gathering, analysis, and decision-making processes. Manual execution of these workflows is time-consuming, error-prone, and requires significant human resources to coordinate across multiple systems and data sources.

Solution with h2oGPTe:

  • Using Agentic AI capabilities to autonomously execute multi-step workflows by integrating LLMs, tools, and memory systems for comprehensive task automation.
  • Using autonomous agents to conduct market research by gathering data from various sources, performing sentiment analysis, and generating comprehensive reports without human intervention.
  • Using workflow automation to handle iterative processes such as data modeling, code execution, and result validation across multiple iterations.
  • Using integration capabilities to connect with external APIs, databases, and tools to create end-to-end automated workflows.

h2oGPTe Differentiators for this Use Case:

  • Autonomous Multi-Step Execution

  • Tool Integration & Memory Systems

  • Scalable Workflow Automation

Try These Queries in h2oGPTe:

"Conduct a comprehensive market analysis for [PRODUCT/SERVICE] in [REGION] including competitor research and trend analysis"

"Automate the data collection and analysis workflow for [PROJECT_NAME] across [NUMBER] data sources"

"Execute iterative model training and validation for [ML_MODEL_TYPE] with performance optimization"

Use case 12: Agentic AI – financial analysis and risk assessment​

Challenge: Financial institutions and investment firms require sophisticated analysis of market trends, risk assessment, and investment decision-making processes. Traditional approaches often lack the speed and comprehensiveness needed for real-time financial decision-making, especially when dealing with complex regulatory requirements and market volatility.

Solution with h2oGPTe:

  • Using Agentic AI to autonomously analyze financial reports, assess compliance requirements, calculate exposure levels, and produce comprehensive investment decision reports.
  • Using advanced analytics to evaluate market trends, perform risk modeling, and generate predictive insights for portfolio management and investment strategies.
  • Using regulatory compliance automation to monitor and assess adherence to financial regulations, ensuring timely reporting and risk mitigation.
  • Using real-time data processing to analyze market conditions, identify anomalies, and provide immediate risk alerts and recommendations.

h2oGPTe Differentiators for this Use Case:

  • Real-Time Financial Analysis

  • Regulatory Compliance Automation

  • Advanced Risk Modeling

Try These Queries in h2oGPTe:

"Analyze the risk exposure for [PORTFOLIO_TYPE] and generate a comprehensive risk assessment report"

"Evaluate market trends for [SECTOR] and provide investment recommendations based on current conditions"

"Assess regulatory compliance for [FINANCIAL_PRODUCT] and identify potential compliance gaps"

Use case 13: Agentic AI – policy development and impact analysis​

Challenge: Government agencies, non-profits, and large organizations need to develop comprehensive policies that consider multiple stakeholders, regulatory requirements, and potential impacts. Traditional policy development processes are often slow, lack comprehensive analysis, and may miss critical factors that could affect policy outcomes.

Solution with h2oGPTe:

  • Using Agentic AI to research existing policy frameworks, evaluate public impact, and assess economic and social outcomes systematically.
  • Using comprehensive analysis to review current policies, analyze community accessibility, calculate economic and social outcomes, and generate data-driven policy decision reports.
  • Using stakeholder impact modeling to assess how proposed policies will affect different groups and identify potential unintended consequences.
  • Using regulatory compliance checking to ensure proposed policies align with existing laws and regulations across multiple jurisdictions.

h2oGPTe Differentiators for this Use Case:

  • Comprehensive Impact Analysis

  • Stakeholder Modeling

  • Regulatory Compliance Integration

Try These Queries in h2oGPTe:

"Analyze the potential impact of [POLICY_NAME] on [STAKEHOLDER_GROUP] and provide recommendations"

"Review existing policies related to [TOPIC] and identify gaps or areas for improvement"

"Assess the economic and social outcomes of implementing [POLICY_TYPE] in [REGION]"

Use case 14: Agentic AI – medical research and clinical recommendations​

Challenge: Healthcare professionals and researchers face overwhelming amounts of medical literature, clinical trial data, and patient information. Manually synthesizing this information to provide evidence-based clinical recommendations is time-consuming and may result in missed opportunities for improved patient care.

Solution with h2oGPTe:

  • Using Agentic AI to conduct comprehensive literature searches, evaluate study quality, synthesize findings, and generate evidence-based clinical recommendations.
  • Using advanced research capabilities to search for the latest medical research, evaluate study methodologies, and synthesize findings into actionable clinical insights.
  • Using evidence synthesis to analyze multiple studies, identify consensus findings, and provide graded recommendations based on evidence quality.
  • Using clinical decision support to integrate patient-specific data with current medical knowledge to provide personalized treatment recommendations.

h2oGPTe Differentiators for this Use Case:

  • Evidence-Based Clinical Support

  • Literature Synthesis & Analysis

  • Personalized Treatment Recommendations

Try These Queries in h2oGPTe:

"Search for the latest research on [TREATMENT] for [CONDITION] and provide evidence-based recommendations"

"Analyze clinical trial results for [DRUG_NAME] and synthesize findings for clinical practice"

"Review the evidence base for [DIAGNOSTIC_METHOD] and provide recommendations for implementation"

Use case 15: Sales AI assistant for retail operations​

Challenge: Retail organizations struggle to provide consistent, accurate product information and recommendations across multiple locations. Sales staff often lack access to real-time information about products, pricing, and inventory, leading to poor customer experiences and lost sales opportunities.

Solution with h2oGPTe:

  • Using AI assistants to provide real-time, accurate information to improve customer experiences in retail environments.
  • Using AI assistants to help sales staff access product information, pricing, and recommendations instantly, ensuring consistent information delivery across all retail locations.
  • Using integration capabilities to connect with inventory systems, pricing databases, and customer relationship management tools for comprehensive sales support.
  • Using personalized recommendations to suggest relevant products and services based on customer preferences and purchase history.

h2oGPTe Differentiators for this Use Case:

  • Real-Time Product Information

  • Consistent Cross-Location Support

  • Personalized Customer Recommendations

Try These Queries in h2oGPTe:

"What are the current pricing options for [PRODUCT_CATEGORY] and what promotions are available?"

"Find product recommendations for customers interested in [FEATURE] within [PRICE_RANGE]"

"What is the inventory status for [PRODUCT_NAME] across all retail locations?"

Use case 16: RFP and procurement automation​

Challenge: Organizations spend significant time and resources on Request for Proposal (RFP) processes, vendor evaluations, and contract negotiations. Manual review of proposals and vendor comparisons is time-consuming, error-prone, and may result in suboptimal vendor selection.

Solution with h2oGPTe:

  • Using automated proposal reviews, vendor comparisons, and contract negotiations by analyzing RFP documents and procurement materials using LLMs and document AI capabilities.
  • Using intelligent document analysis to extract key information from vendor proposals, compare offerings, and identify the best value propositions.
  • Using automated contract analysis to review terms and conditions, identify potential risks, and ensure compliance with organizational requirements.
  • Using vendor evaluation frameworks to systematically assess vendor capabilities, track records, and alignment with organizational needs.
  • Real-world example: Singtel uses h2oGPTe for RFP/Procurement GPT, streamlining procurement processes and improving accuracy in vendor selection and contract analysis.

h2oGPTe Differentiators for this Use Case:

  • Automated Proposal Analysis

  • Intelligent Vendor Comparison

  • Contract Risk Assessment

Try These Queries in h2oGPTe:

"Compare vendor proposals for [SERVICE_TYPE] and identify the best value proposition"

"Analyze the contract terms for [VENDOR_NAME] and identify potential risks or compliance issues"

"Review the technical specifications in [RFP_NUMBER] and assess vendor alignment with requirements"

Use case 17: Insurance document analysis and claims processing​

Challenge: Insurance organizations struggle with manual processing of medical bills, forms, and documentation. The process is time-consuming, error-prone, and often results in delayed claims processing and increased administrative costs.

Solution with h2oGPTe:

  • Using RAG (Retrieval-Augmented Generation) to analyze medical bills, forms, and documentation, extracting key information and linking it with beneficiaries and providers for efficient insurance claims processing.
  • Using intelligent document processing to extract relevant information from medical forms, ensuring accurate data capture and reducing manual data entry errors.
  • Using automated insurance claims validation to verify information accuracy, check for completeness, and identify potential issues before processing.
  • Using integration with various insurance healthcare systems to streamline the claims workflow and improve operational efficiency.
  • Real-world example: Unimed implemented h2oGPTe for medical claims analysis, automating extraction and validation of medical data for faster, more accurate claims processing.

h2oGPTe Differentiators for this Use Case:

  • Automated Medical Document Processing

  • Intelligent Claims Validation

  • Healthcare System Integration

Try These Queries in h2oGPTe:

"Extract key information from [MEDICAL_FORM_TYPE] and validate against patient records"

"Analyze medical claims for [PROVIDER_NAME] and identify any missing or inconsistent information"

"Process medical bills for [BENEFICIARY_GROUP] and generate claims processing recommendations"

Use case 18: Automated ML model development and feature engineering​

Challenge: Data science teams face significant challenges in developing machine learning models efficiently. Traditional approaches require extensive manual feature engineering, model selection, and hyperparameter tuning, which is time-consuming and often requires specialized expertise.

Solution with h2oGPTe:

  • Using natural language understanding capabilities to automatically develop machine learning models and perform feature engineering, comparing performance against traditional data science approaches.
  • Using automated model development to accelerate the ML pipeline, reduce data science workload, and enable rapid prototyping and deployment.
  • Using intelligent feature engineering to automatically identify relevant features, create new features, and optimize feature selection for improved model performance.
  • Using performance comparison tools to evaluate different modeling approaches and provide insights for optimization.

h2oGPTe Differentiators for this Use Case:

  • Automated Model Development

  • Intelligent Feature Engineering

  • Performance Comparison & Optimization

Try These Queries in h2oGPTe:

"Develop an ML model for [PREDICTION_TASK] using [DATASET] and compare performance with traditional approaches"

"Perform automated feature engineering for [MODEL_TYPE] and identify the most important features"

"Optimize hyperparameters for [ALGORITHM] and provide performance comparison across different configurations"

Next steps & resources​

To effectively use h2oGPTe, organizations should start by identifying key challenges or opportunities where generative AI can provide value. There are several approaches to examine the platform's capabilities and facilitate implementation.

  • Explore a Demo and Contact Sales: Request a demo of Enterprise h2oGPTe tailored to your industry by contacting the H2O.ai sales team. They can assist with consultations, pricing, and deployment planning.

  • Freemium Access: For initial exploration and hands-on evaluation, a freemium tier of Enterprise h2oGPTe is available. This version provides access to the product's feature set, enabling users to explore the platform's capabilities.

  • Dive Deeper into Documentation: Explore the detailed technical documentation for h2oGPTe, which outlines the specific components and functionalities of the platform. Browse the complete documentation.

Feedback