InvGate AI Hub consists of a series of features that enable artificial intelligence for IT teams.
From resolving employee requests before they become tickets to monitoring the service environment continuously, acting on every moment of the ticket lifecycle, and assisting agents as they work, it is built to empower support teams and augment their capabilities.
The AI Hub is available in all InvGate Service Management and InvGate Asset Management plans, for both cloud and on-premise customers.
Let’s see the features it includes in more detail.
InvGate AI Hub: A Practical Adoption Guide
What is InvGate AI Hub?
InvGate AI Hub is a centralized initiative to unify all the AI-powered features our products offer.
Rather than handing you a blank builder to configure your own automations, the AI Hub gives you a collection of purpose-built capabilities already embedded in your platform. Each one is scoped to a specific job, operates inside your existing InvGate Service Management and Asset Managemernt workflows, and is auditable by default.
Generally speaking, the AI Hub has some clear advantages:
- It provides admins with more autonomy and automation capabilities for their teams.
- It decreases your agents’ ticket volume, allowing for even more time saving.
- It offers your end-users faster responses and more self-service resources.
Now, it’s time to see how it works.
InvGate AI Hub features for Service Management
Before we start, you can enable as many features as you’d like from Settings >> AI Hub. And if you haven’t tried InvGate Service Management yet, there’s a 30-day free trial waiting for you!
Agent assistance
The Agent Assistance capabilities work alongside your team inside the ticket workflow, drafting responses, summarizing history, recommending solutions, and generating Knowledge Base articles. The agent stays in control at every step: the AI prepares the work, and the technician reviews it and decides what to send.
1. Solution recommendation
Solution Recommendation analyzes the ticket's content and context, then looks at knowledge base articles, similar previous tickets, and open knowledge from the internet (if authorized) to generate a complete, ready-to-use solution
The key benefits early users are seeing are:
- Improving first-response times by providing agents with detailed, pre-written solutions during the initial interaction.
- Freeing up agents to focus on complex cases and boosting overall team productivity.
2. AI-improved responses
InvGate Service Management's AI-Improved Responses leverage generative AI to improve, shorten, or expand help desk agents’ ticket replies.
Starting with a draft provided by the agent, the feature uses Generative AI to improve it. Once the new text is generated, the technician can tweak it before sending it.
Agents using AI-Improved Responses respond to tickets up to 28% faster*.
This feature gives your teams even more efficiency and time-saving by reducing the time technicians spend preparing the replies while also providing accuracy and consistency across all support.
Plus, Quick Replies offers one-click responses for common situations, such as acknowledging a request, asking for more information, or following up. They keep the conversation moving without breaking the agent's focus.
3. Ticket summarization
Next in line, Ticket Summarization offers a way to automatically generate a recap of the ticket activity so far. In addition, users can choose to post the summary as an internal comment for future reference.
This AI capability allows to onboard someone new to a complex ongoing incident in less than a minute*.
This feature is able to summarize the problem, the steps taken so far, and the people involved, escalations, collaborations, and approvals comes in handy for those complex issues that run for days on with too much activity.
4. Expert collaboration
Expert Collaboration Suggestion proposes collaborators who have resolved similar tickets and received high satisfaction scores to resolve specialized or cross-departmental issues.
The criteria it uses to generate the suggestions are as follows:
- It analyzes ticket details, context, and agent expertise.
- It prioritizes recommendations based on relevant skills, past performance, and ticket complexity.
This speeds up the ticket resolution process by connecting tickets to the most qualified agents. And there's a nice bonus: it also encourages a coordinated approach to problem-solving.
5. Smart Request Assignment
Smart Request Assignment uses artificial intelligence to automatically assign each request to the most suitable agent.
Rather than relying only on classic assignment methods such as round-robin or workload-based rules, it analyzes the request itself along with each agent’s experience, availability, and current workload. It then selects the agent best positioned to handle it.
The result is less manual work for coordinators, a more balanced distribution of requests, and faster resolution times.
6. Keyword generation
Keywords Generation automatically expands your service categories with AI-generated keyword suggestions, so employees always find the right category when submitting a request.
Implementing the Keyword Generation capability reduces misclassified tickets by 32%*.
Admins can quickly add accurate keywords to any category, reducing the time and effort required for manual configuration.
Proactive detection and operational insights
These capabilities watch your entire service environment continuously, scanning ticket streams, tracking SLA timers, and analyzing patterns so nothing slips through unnoticed.
7. Major Incident detection
As the name indicates, Major Incident detection identifies major incident candidates and suggests creating a major incident to prevent them from escalating further and ensure business continuity.
It leverages AI to analyze reported incidents, detects patterns indicating a potential major incident in real-time, and notifies help desk coordinators of potential major incidents for immediate review and action.
Plus, once a major incident is identified, the Major Incident announcement suggestions use AI to generate draft announcements and updates based on the information recorded in the incident. This helps teams communicate with affected users quickly, clearly, and consistently throughout the response.
8. Common problem detection
Common problem detection uses AI to analyze the service desk’s incident data at scale and identify patterns that indicate an underlying problem. It examines incidents across the service desk, finds similarities between separate issues, and identifies recurring patterns that point to a common root cause. When it detects a potential problem, it suggests creating a problem ticket for further investigation.
AI gives Problem Management teams an overview of incident patterns across the service desk without requiring them to manually review and correlate individual incidents. This helps them identify recurring problems earlier and focus investigation on their underlying causes.
9. Smart request escalation
Smart Request Escalation is a feature that contributes to preventing service level agreement (SLA) breaches.
It tracks ticket progress and analyzes historical resolution data to predict requests at risk of missing SLA deadlines. Once spotted, it suggests a ticket escalation, to allow for timely intervention by agents or supervisors and SLA compliance.
10. Predictive risk and impact analysis
Predictive Risk And Impact Analysis proactively assesses and suggests the risk and impact of change requests based on historical cases and similar requests.
It leverages predictive analysis to improve Risk Management by proactively identifying high-impact and high-risk requests. The goal is to prevent your team from underestimating the risk or impact of a request and ensure business continuity.
11. Predict Estimated Time to Resolution
As the name implies, Predict Estimated Time to Resolution estimates and displays the estimated time for a ticket to be resolved.
It leverages AI to analyze historical cases and similar requests to predict the ETTR. After calculation, it displays the predicted time so that end-users can estimate how fast that may be resolved.
Its benefit is significant since it can decrease the "Any updates?" messages that distract the agents from actually resolving the issue.
12. Sentiment analysis
This capability uses AI to understand and display the general sentiment expressed by the person creating a ticket, helping agents quickly understand the emotional context of each request.
It classifies the overall tone as positive, neutral, or negative, and allows prioritization based on this diagnosis.
Sentiment Analysis' potential lies in its influence on improving CSAT scores by allowing agents to address angry customers first.
Knowledge Management
The Knowledge Management capabilities create, extract, and deliver knowledge across the service desk, turning the work your team already does into answers that resolve future tickets.
13. Knowledge article generation
The Knowledge article generation capability transforms incident resolutions into knowledge articles. After resolving an incident, agents will get the chance to use the details from the initial request and the most relevant activity to solve it and generate a first knowledge article draft with AI.
The first version of the article is generated in less than 30 seconds* for them to review, edit, and submit for approval.
14. Knowledge Discovery
Knowledge Discovery analyzes your resolved tickets and extracts the guidance inside them into Snippets: short, reusable pieces of knowledge that capture how an issue was solved. It builds these automatically from the work your team has already completed, without asking anyone to write documentation from scratch.
Your team reviews and approves each Snippet before it goes live, so only vetted knowledge makes it into circulation. Once approved, Snippets can feed Solution Recommendation to support agents inside a ticket, power the Virtual Service Agent's responses to end-users directly, or both.
15. Contextual knowledge article summaries (by Virtual Agent)
InvGate Service Management Virtual Agent can provide end-users with contextual knowledge article summaries so that they quickly get the information they need to solve an issue without contacting IT support.
This functionality not only decreases the number of submitted tickets through the chat by 15% but also increases the chatbot adoption by 40%*.
Virtual Service Agent
InvGate Service Management's Virtual Service Agent is the front door of your service desk. It lives in Microsoft Teams, WhatsApp, Slack, and your self-service portal, understands requests in natural language, and resolves common issues on the spot, so employees get help without opening a ticket.
Its answers draw on both your Knowledge Base and the Snippets that Knowledge Discovery mines from resolved tickets, so the guidance that closes real cases reaches your end-users directly. When resolution isn't possible, it routes the request with full context.
When interacting with the Virtual Service Agent, users can:
- Create requests
- View request details
- Retrieve assigned requests
- Retrieve "My Requests"
- Comment on existing requests
- Accept or reject approvals
- Receive notifications, and more.
InvGate AI Hub features for Asset Management
How to Enhance Your IT Asset Management Strategy With AI
Asset intelligence is built into InvGate Asset Management, surfacing what matters across your inventory automatically, with no separate configuration required.
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AI Smart Search: Lets you search and filter assets using natural language. It works by writing "@" and your query, for example: "@laptops assigned to John Doe", and InvGate Asset Management will translate it into the correct structured search parameters to provide you with the data.
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Smart Recommendations: Surfaces prioritized insights from your asset data so teams can replace, update, reassign, or fix assets proactively, acting on what matters most without manual analysis.
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Atlas: This feature adds vendor details, end-of-life dates, and vulnerability alerts to your asset profiles automatically, enriching your inventory with the context your team needs to make decisions.
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CMDB Auto-Mapping Suggestions: CMDB Auto-Mapping Suggestions proposes the most important relationships between your assets, keeping your CMDB accurate with less manual work.
*All the data is based on internal testing.
