AI

A Break down of the Automated Statement of Work Generation & AI Project task Creation

AI Abilities

AI Ability Use Case: Auto Triage

The article provides a comprehensive guide to using and customizing AI Auto Triage in HaloPSA, a feature that automatically assigns a category, impact, and urgency to newly logged tickets. This function uses OpenAI to analyze ticket details and suggest appropriate classifications, eliminating the need for pre-existing data. It can be customized in terms of prompt, category usage, and formatting. The guide also includes instructions for downloading the AI Auto Triage Runbook for Halo instances created before September 2025, outlining additional configurations for custom use.

AI Article Creation

AI Article Summary and Article Duplication Detection

This article explains how to use AI in Halo for generating article summaries and detecting duplicate articles. The AI summaries help provide concise content overviews and track duplicate articles to maintain knowledge base quality. To enable these features, users must configure AI connections and select a vector search database. AI summaries are generated for indexed articles, aiding real-time duplication detection by comparing summary similarities. Users can set a similarity threshold to categorize articles as duplicates, promoting efficient content management and reducing redundancy.

AI Azure Translator

This article provides a guide on integrating and using Azure AI Translator in Halo. It explains how to enable and configure the module, highlighting the steps to obtain an API key and set region parameters. The guide includes instructions for translating notes in tickets and using translation features in the Self-Service Portal. It also covers auto-translating language packs and configuring action toggles for translation. The service allows for translations of user and agent notes into the viewer's language, with options for text or HTML translations and a monthly character limit.

AI Client Profiling

The article explains AI Client Profiling, which uses AI to analyze customer tickets and summarize recent experiences in CRM notes. This automates monitoring and provides insights for account managers. Configuration requires an Azure/OpenAI connection and involves setting evaluation frequency and crafting evaluation prompts. The AI model reviews ticket data and updates CRM notes regularly, with options to manually run task scheduling. It highlights customer mood, satisfaction, recurring issues, and agent performance, aiding in understanding and improving customer experiences.

AI Emotion Detection (Old functionality)

AI Functionalities Available When Using Own Connection vs Default Connection

AI Insights

This article provides a comprehensive guide on AI Insights, explaining their role in automating ticket analysis to provide valuable insights like customer sentiment, conversation summary, and prioritization. It covers setup instructions, including how to integrate and configure AI Insights using built-in functionality or runbooks. The guide details features such as sentiment analysis and the AI's ability to suggest categories and resolutions, aiming to improve ticket management efficiency and user satisfaction. The configuration options enhance the adaptability of AI Insights to specific workflow needs.

AI Suggestions (Tickets and Articles)

This guide provides instructions for configuring AI suggestions for ticket and article management. AI can improve ticket handling by matching new tickets with historical ones, using embedding scores to suggest field values or automate actions. To enable this, users must first configure AI ticket matching. AI suggestions can include changes to estimates, assigned agents, categories, priorities, and more based on historical data. For article suggestions, Azure AI Search acts as the vector database. Once set up, AI insights and suggestions appear within the ticket interface, assisting agents in efficient ticket resolution.

AI Surveys

AI Ticket Clustering

Amazon Bedrock Integration

Boost Ticket Processing using AI

This article outlines steps to use AI for enhancing ticket processing by generating automatic user responses and improving agent responses. It involves using Azure OpenAI for automatic responses, configuring actions to send tailored messages, and using an AI model to preview and improve agent replies. Automation workflows can trigger AI-generated responses when tickets are logged, without previewing. AI aids in refining language and format in agent notes. Additionally, AI can detect 'thank you' messages on closed tickets, determining if further action is needed.

Connect AI to Halo using Azure OpenAI

This article provides a guide on connecting an AI model from Azure OpenAI to the Halo platform. It includes steps to create an AI model in Azure, create a resource group, and deploy Azure OpenAI resources. The guide also details deploying embeddings and GPT models, retrieving API keys and endpoints, and entering these details into Halo. Key steps include setting up a Resource Group in Azure, creating an Azure OpenAI resource, deploying models, and configuring them in Halo for AI functionalities.

Connecting AI to Halo

This article provides a step-by-step guide on connecting AI to the Halo instance, detailing various AI connection options available, such as the default Halo connection using OpenAI, the Azure OpenAI connection, and the option to use your own OpenAI account. It covers the security implications, data handling processes, and compliance with data protection regulations. The article also discusses the advantages and disadvantages of each connection method, including costs and required subscriptions, and outlines AI functionalities that can be configured once connected, such as AI suggestions and insights.

Connecting Claude to Halo's MCP Server

Connecting Copilot Studio to Halo's MCP Server

Contents of AI Guides

Conversational Agents

The guide provides a comprehensive overview of virtual agents in Halo's chat bot, detailing their functions for both end-users and agents. It explains how to use virtual agents to search knowledge bases, log or update tickets, and manage service requests. The article also covers customizing virtual agent functions and creating new agents, including setting up AI connections with OpenAI or Azure OpenAI. Specific features like uploading attachments and custom function setups are highlighted, with instructions on configuring these tools for enhanced efficiency and service delivery within chat profiles.

Forethought AI Solve Integration

Halo AI - Security Implications

The article discusses the security implications of connecting AI models to a Halo instance, focusing on data storage, transmission, and processing. It covers different types of AI connections, including default Halo, OpenAI, and Azure OpenAI connections. Key points include data encryption during transmission, temporary vs. permanent storage policies, and customer control over data. The document emphasizes adherence to regional data protection regulations like GDPR and outlines access restrictions and compliance standards for data security and privacy in AI integrations.

Halo MCP Server Connection

The OpenAI MCP Server Connection allows AI assistants to connect and dynamically invoke tools in the Halo API, available from version v2.204.1+. By enabling the MCP endpoint in the configuration menu, AI can trigger various functions such as knowledge search, ticket logging, and running runbooks similar to virtual agents. Example usage includes creating and assigning tickets through AI. For authentication, OAuth flows or a static API Key can be used. Users can add and customize available functions, which need to execute quickly to prevent timeouts, especially when using 'Execute and await completion' for longer tasks.

Improve Search Using AI

This guide explains how to enhance search capabilities using AI in the Halo platform. It covers setting up AI search by connecting to Azure AI Search or AWS OpenSearch, which improves results by understanding the context rather than just keywords. The article details prerequisites and steps for connecting AI models, indexing content, and using AI search. The guide emphasizes its benefits, particularly for virtual agents that utilize AI-driven search to respond to user queries effectively. It also provides troubleshooting tips for ensuring correct setup and operation.

Leveraging AI in Halo

This article provides guidance on integrating and using AI within Halo to enhance productivity and innovation. It covers understanding AI capabilities such as automation and predictive analytics, initial setup and configuration, best practices, and common use cases across various industries. The resolution section details specific AI functionalities like ticket matching, AI insights, and article suggestions. Users are guided through connecting AI to Halo, managing ticket data, and utilizing AI for efficient ticket resolution and knowledge creation. Ethical considerations and data privacy are also emphasized.

MCP Endpoint: Use the Chatbot to Query and Update Data in your Instance

Nixtla TimeGPT Integration

The Nixtla TimeGPT Integration allows Halo users to forecast future ticket volumes based on historical data. To integrate, users must generate an API key in Nixtla TimeGPT and configure it within Halo's AI integration settings. Forecasting options are expanded with Nixtla TimeGPT models, which allow users to incorporate events that might impact ticket volumes, such as holidays or marketing campaigns. Users can create and run new forecasts, selecting models that best suit their forecasting needs and utilizing features like event integration for accuracy.

Open AI Integration

This article provides a guide on integrating OpenAI with a system, focusing on connecting, configuring, and using the integration. It explains how OpenAI can be utilized in ticket responses, live chats, and improving agent replies. The guide outlines the steps for enabling OpenAI in the system, obtaining an API key, selecting an AI model, and configuring these settings in Halo. It also discusses the functionality of AI, like enhancing agent notes and chatbot interactions, with a focus on practical applications and limitations for effective use.

Process Agents

Use AI to Analyse Business Performance

This article provides a guide for using AI to analyze business performance using HaloPSA. It explains how AI can generate and analyze reports from your Halo instance, with results automatically formatted into PDFs for user review. The process can be customized in multiple ways, including the triggering event for report generation, the specific report analyzed, and how AI interprets the data. Customization options include modifying ticket types, selecting different reports, and adjusting AI prompts for analyses. Pre-requisites include setting up an AI connection and configuring specific system actions.

Vector Search Databases and Embeddings (used for AI Search)

This article provides a guide on vector search databases, essential for AI search functionalities. It explains that a vector search database stores and searches numerical representations, allowing semantic searches rather than exact matches. The guide covers how to index data such as tickets and articles for quick retrieval and provides options for vector search databases, including Halo Vector Store, Azure AI Search, and AWS Opensearch. It also includes procedures for setting up connections with external services and emphasizes the importance of indexing for optimizing AI capabilities in the Halo platform.