Aisera Assistant

I have approximately two years and six months of hands-on experience with Aisera's AI Copilot.

I primarily use Aisera's AI Copilot for automating IT support and employee service requests. It helps users obtain answers, resolve common issues, and create service tickets through conversational interfaces.

Regarding the biggest advantage of Aisera's AI Copilot, IT service desk automation is probably the strongest use case. It helps employees route issues without needing direct support from an agent.

We noticed that with Aisera's AI Copilot, a significant portion of routing requests could be handled automatically. It reduced the workload on support teams, allowing developers and support teams to spend less time handling repeated requests and more time focusing on higher-value projects. That represents the best advantage.

Aisera's AI Copilot integrates with platforms such as ServiceNow, Microsoft Teams, Salesforce, and other enterprise applications, which allows users to access information and automate tasks without switching between multiple tools.

Aisera's AI Copilot uses AI to understand user requests properly, provide relevant answers, automate support tickets, and help employees resolve issues without needing human intervention. For example, if an employee asks how to reset a password, the AI understands the request, finds the correct information, and either provides instructions or automatically creates the password reset process.

From my business perspective, the functions of AI in Aisera's AI Copilot include understanding requests and generating responses. Functions include answering user queries, AI-powered conversation support, automating ticket creation, and resolution workflow automation that enable users to ask normal queries and get instant answers. Another feature is knowledge-based search, employee service, self-service capabilities, and integrations with enterprise applications such as ServiceNow, Microsoft Teams, and Slack, which automates my support requests, resolves common issues instantly, creates tickets, and provides self-service assistance.

In Aisera's AI Copilot, metrics can be thought of as a set of rules and conditions used to route requests, automate workflows, and determine how different support scenarios are handled. Metrics help ensure that the right actions, knowledge-based articles, or support teams are selected based on the user's request, defining and organizing decision-making and automation logic. Metrics enable Aisera's AI Copilot to categorize requests, trigger workflows, route tickets, and provide relevant responses automatically.

Regarding improvements to Aisera's AI Copilot, the main concern is pricing. The enterprise AI platform requires investment, but the productivity gains and reduction in manual support can justify the cost. From a costing point of view, sometimes the results are not what the customer wanted. That is the only concern.

When considering adding features to Aisera's AI Copilot, from my perspective, the overall requirement is that it should be reliable and stable for day-to-day support operations. The only feature I would add is enhanced workflow automation capability because today it is underrated. I would prioritize work on workflow automation.

I have approximately two years and six months of hands-on experience with Aisera's AI Copilot.

I agree that Aisera's AI Copilot is stable. In terms of reliability, it is overall reliable and stable for day-to-day support operations. It handles growth well; as more knowledge articles and service requests are added, performance remains consistent and stable.

The only challenge I face in scaling with Aisera's AI Copilot is maintaining and organizing the knowledge base so that the AI could continue delivering accurate responses.

I am not directly part of conversations with Aisera's AI Copilot support, but some of my colleagues are part of those conversations.

My experience with Aisera's AI Copilot support is generally positive.

I have rated my experience with Aisera's AI Copilot support as 8.5 out of 10.

I am also working with ArangoGraph and have hands-on experience with both ArangoGraph and Aisera's AI Copilot.

You can start with Aisera's AI Copilot. My role is a customer segmented type with Aisera's AI Copilot. I am a user of Aisera's AI Copilot but also a maintainer for my company.

I am not suggesting anyone use another product because I directly work with Aisera's AI Copilot and it is definitely good. It has provided a services management platform that automates employee and customer support processes through conversational AI with self-service capabilities, making it a good solution that I can directly suggest to anyone.

We chose Aisera's AI Copilot because we wanted more automation and a better self-service experience for employees. It is straightforward and not complicated at all. Anyone can implement it, even beginners.

I have given this review an overall rating of 8 out of 10.

Public Cloud

Amazon Web Services (AWS)

We mainly use Aisera's AI Copilot to help us with technical tasks and also retrieval in our RAG system, mostly searching the company's internal knowledge.

For example, some of our customers want to implement an LLM in their chatbot, so when a user types a request like 'I need a new bag,' Aisera uses the LLM models specifically trained on the customer's enterprise data to understand the context instead of relying on strict keyword matching.

We also use the Dynamic Action Agents with Aisera's AI Copilot, which we use to customize our agentic use case.

This approach has saved us time and also improved accuracy compared to how we handled those requests before.

Previously, it used to take our developer or one of our workers maybe three minutes to search for a product or to search for something, but using Aisera's AI Copilot, they can do it within seconds.

Aisera's AI Copilot has increased the auto-resolution rates to about 65 to 80%, which in turn reduces the mean time to resolution ratio and frees up human agents for complex work.

Some of this auto-resolution involves ticket deflection, where Aisera's AI Copilot handles low-hanging fruit, such as requests for password resets or software access requests, which in turn prevents a massive volume of Level 1 support tickets from ever reaching the human desk.

Aisera's AI Copilot positively impacts our organization mostly by helping the Agent Assist.

Agent Assist has helped us to offset some of the workloads reaching our human customer support, which in turn has made our customer support quite effective and timely.

We have increased our response time and also received positive feedback from our customers since using Aisera's AI Copilot.

Mostly, Aisera's AI Copilot has saved a lot of time on our side.

Aisera's AI Copilot can be improved by fine-tuning, since it is not a plug-and-play solution.

They can simplify some of the tasks with Aisera's AI Copilot, because the system is very rigid. While it is excellent at complex API workflows, most of the time it struggles with deceptively simple natural language variations.

I have been using Aisera's AI Copilot since the beginning of the year, this January.

Aisera's AI Copilot has been stable for most of the time with no issues in my experience.

As of now, Aisera's AI Copilot has been quite scalable since we are a growing organization, and we have not had any issues with scalability yet.

The customer support for Aisera's AI Copilot was excellent. I would rate the customer support for Aisera's AI Copilot a 9 on a scale of 1 to 10.

We were using Microsoft Copilot before Aisera's AI Copilot.

I decided to switch from Microsoft Copilot to Aisera's AI Copilot because Aisera's AI Copilot looks more modernized, its accuracy score is higher than the previous one, and we have had good results with it.

The pricing and licensing for Aisera's AI Copilot were great, and the setup was smooth.

We did not evaluate other options before choosing Aisera's AI Copilot.

I give Aisera's AI Copilot an 8 on a scale of 1 to 10.

I chose an eight because it serves the purpose at the moment, and it has really helped us work efficiently.

At the moment, we trust Aisera's AI Copilot with our data, and we trust the founders and them with our data regarding Aisera's AI Copilot's governance and security.

The output from Aisera's AI Copilot has been quite well-received from our managerial position, so we do not have any complaints regarding its accuracy and reliability of output.

I advise that Aisera's AI Copilot is the best solution for your enterprise, and if you want to build any chatbot or RAG system, it is the best.

My overall rating for this product is 8 out of 10.

Private Cloud

Amazon Web Services (AWS)

I use Aisera's AI Copilot mainly for three things: automating repeated IT support tickets, providing instant AI-powered answers to employee queries, and integrating with existing tools such as Slack and ServiceNow to streamline helpdesk operations.

Integration with both Slack and ServiceNow is one of the highlights of my Aisera experience. Starting with the Slack integration, this was completed seamlessly. Employees can simply message Aisera's AI Copilot directly in Slack exactly the way they would message a colleague. There is no need to open a separate portal or remember a different URL. The conversation interface feels completely natural. For example, an employee would type something, "I cannot access the VPN" directly in Slack, and Aisera's AI Copilot would respond with step-by-step troubleshooting instructions, check their access permissions automatically, and either resolve the issue instantly or escalate to a human agent, all without leaving Slack. The biggest win with Slack integration was employee adoption. Because it lived inside a tool people already used every day, adoption was almost immediate. No training was required, no behavior change needed.

For ServiceNow integration, this worked beautifully as a two-way connection. When Aisera's AI Copilot could not resolve an issue automatically, it creates a properly formatted ServiceNow ticket automatically with full conversation context already populated. Human agents receiving those tickets had complete background information, with no back-and-forth asking basic questions. One improvement I would suggest is deeper ServiceNow workflows triggered. Currently, Aisera's AI Copilot creates and updates tickets well, but triggering complex, multi-step ServiceNow workflows automatically still requires some custom configurations.

Several features genuinely stand out in my experience with Aisera's AI Copilot. The first and most impressive is generative AI-powered conversations. Unlike traditional chatbots that rely on rigid decision trees, Aisera's AI Copilot understands natural language intent. Employees can phrase the same questions ten different ways and get the same accurate answer. That flexibility is a complete game changer compared to previous rule-based systems.

Another important feature is the auto-resolution engine, which is the core value driver. Aisera's AI Copilot does not just answer questions; it actually resolves issues end-to-end. Password resets, access provisions, and software installations are handled completely automatically without any human involvement. That capability for autonomous actions is what separates Aisera's AI Copilot from basic question-answering chatbots.

The third feature is continuous learning. Every resolved ticket, whether by AI or human agent, makes the system smarter. I see measurable improvement in auto-resolution rates month over month without any manual retraining. The system generally gets better over time.

A few smaller but appreciated details include smart escalation routing. When Aisera's AI Copilot cannot resolve an issue automatically, it does not just create a generic ticket; it intelligently routes escalations to the most appropriate human agent based on issue type, agent expertise, and current workload. The right ticket goes to the right person automatically, saving significant coordination overhead.

Another detail is conversation memory. If an employee has raised the same issue previously, Aisera's AI Copilot remembers the context and previous resolutions, allowing returning employees to get faster responses. It might say something such as, "I see you had a similar issue last month. Let me try the same resolution first." That personal touch in an enterprise tool is genuinely surprising.

Finally, I appreciate the response formatting. Aisera's AI Copilot never bombards employees with text. Responses are always clearly formatted, with numbered steps for technical instructions, bullet points of options, and bold text for important warnings. That attention to communication quality makes employees actually read and follow the responses rather than ignoring them.

The most underrated feature that I wish more people talked about is the knowledge gap identification capability. More people evaluate Aisera's AI Copilot purely on what it can resolve automatically, but what nobody talks about is how valuable it is for what it cannot resolve. Every time Aisera's AI Copilot fails to answer a query confidently, it flags that as an unresolved intent. Over time, these unresolved intents accumulate into a clear picture of exactly what knowledge is missing from documentation. Before Aisera's AI Copilot, my team had no systematic way of knowing what employees were struggling with that was not documented anywhere. I was essentially flying blind on knowledge gaps.

After three months of Aisera's AI Copilot deployed, I ran a report on top unresolved intents and discovered something fascinating. The three most common knowledge gaps were around newly deployed internal tools that nobody had documented properly, confusing VPN configurations for remote workers, and an unclear expenses reimbursement process that kept generating IT-adjacent queries. I fixed all three documentation gaps within one week, and the auto-resolution rate jumped by nearly twelve percentage points immediately after.

These features together, knowledge gap identification and executing reports, are what I would highlight to anyone evaluating Aisera's AI Copilot. They are not the flashy features in the demo, but they deliver some of the most profound long-term organizational value.

There are several areas for improvement. First, hallucination control: being an LLM-based system, Aisera's AI Copilot occasionally generates confident but incorrect answers. Better guardrails and uncertainty flagging would improve reliability. Second, custom model training: while it learns from the knowledge base, deeper customization of the underlying model for highly specific organizational contexts requires significant effort. Third, multilingual support: for a global organization with non-English speaking employees, language support could be more comprehensive. Another area is integration depth: while major ITSM tools are supported, some niche internal tools require custom API work to integrate properly. Finally, explainability: when Aisera's AI Copilot gives an answer, it does not always clearly cite which knowledge source it drew from, and better source attributions would increase user trust.

Regarding additional improvements, a few more specific enhancements and future capabilities I would love to see include voice interface support. Currently, Aisera's AI Copilot is entirely text-based, but many employees, especially in manufacturing or field operations, have their hands full and cannot type. A voice-activated version of Aisera's AI Copilot would dramatically expand its usefulness beyond desk-based workers. As voice AI improves, this feels a natural next step.

The second improvement is predictive support: right now, Aisera's AI Copilot is reactive; it waits for employees to raise issues. The next level would be proactive predictions, detecting patterns that historically precede common issues and reaching out to employees before they even experience the problem. For example, if system logs show a particular application behaving unusually, Aisera's AI Copilot could proactively message affected users with a heads-up and solution before they even notice.

Lastly, offline or low connectivity mode: for employees in areas with poor internet connectivity, having cached responses for common queries available offline would significantly improve accessibility. These improvements would transform Aisera's AI Copilot from an excellent IT support tool into a truly universal intelligent workplace assistant.

I have been using Aisera's AI Copilot for approximately one year, primarily for automating IT helpdesk workflows and employee support processes in my organization.

The reliability of the platform itself is excellent, with 99.9% uptime and no unplanned outages during my deployment.

The impact of Aisera's AI Copilot is immediate and significant. First, ticket deflection: roughly sixty to sixty-five percent of routine IT tickets are automatically resolved by Aisera's AI Copilot without human agent involvement, dramatically reducing the workload on my IT team. Second, response time improvement: employee queries that previously waited hours for human responses are now answered instantly, twenty-four hours a day, seven days a week. Third, employee satisfaction has greatly improved; people stopped feeling frustrated waiting for IT support, and instant resolution significantly improved the overall employee experience. Fourth, IT team productivity has surged; freed from repeated tickets, my IT team can focus on strategic infrastructure improvements. Finally, cost reduction: a lower ticket volume handled by humans means significant savings in support operational costs.

When discussing metrics, Aisera's AI Copilot provides very comprehensive metrics including ticket deflection rate, the percentage of tickets resolved without human intervention, mean time to resolution, user satisfaction score, post-resolution feedback from employees, auto-resolution rate broken down by category such as password resets, access requests, and software issues, knowledge base coverage, escalation rate, and an ROI dashboard showing cost savings from automated resolutions versus human-handled tickets.

I would add that in my team or my perspective, a few additional things are worth sharing. One thing that genuinely surprised me was how different team members interacted with Aisera's AI Copilot differently, and the system handled all of them well. For example, my junior developers used it heavily for environment setup queries, thinking configuring local development environments, setting up AWS credentials, and resolving dependency conflicts. Aisera's AI Copilot handled their queries consistently and accurately, providing what felt having a senior developer available twenty-four hours a day, seven days a week for basic questions. My non-technical team members such as HR, finance, and operations used it completely differently, asking simple things such as how to request software licenses, how to reset passwords, and how to connect to printers. Aisera's AI Copilot adapted its response style automatically—more technical for developers, simpler language for non-technical users. That adaptive communication was genuinely impressive.

Another use I discovered was onboarding new employees. New juniors had hundreds of basic questions about where to find documents, how to set up tools, and who to contact for what. Aisera's AI Copilot has become the first point of contact for every new employee. Onboarding time reduced by roughly thirty percent because new joiners received instant answers instead of waiting for someone to respond. Overall, Aisera's AI Copilot has become much more than a helpdesk tool for me; it is a central nervous system connecting employees to information and IT support across the entire organization.

Out of all those features, the one that has had the biggest impact on my team is definitely the auto-resolution engine. The reason is simple: it delivers immediate, tangible value from day one. Continuous learning is powerful, but its impact compounds gradually over months. The auto-resolution engine changed my team's daily reality instantly. Let me share a specific story that captures this perfectly. In my first week of deployment, my IT team handled roughly eighty to ninety tickets per week manually, with password resets alone accounting for nearly twenty-five to thirty percent of that volume. Every single password reset required an IT agent to verify identity, reset credentials, and respond to the employee, taking roughly fifteen to twenty minutes per ticket. After Aisera's AI Copilot's auto-resolution engine went live, every password reset was handled automatically. Employee requests involved identity verification through Slack, where Aisera's AI Copilot verified through existing identity providers, reset passwords, and notified the employee. The entire process was completed in under two minutes with zero human involvement. That single automation saved my IT team approximately six to eight hours per week, just on password resets alone. The deeper impact was psychological; my IT team stopped dreading Monday mornings. Previously, weekends meant accumulated tickets and backlogs waiting on Monday. After Aisera's AI Copilot had already resolved most routine issues automatically, team morale visibly improved.

Aisera's AI Copilot is deployed as a SaaS public cloud solution, fully managed by Aisera with no on-premises option required for my use cases. I connect it to my existing AWS infrastructures through API integrations. That SaaS model means zero infrastructure management on my side, which is ideal for my team size and operational capacity.

I purchased Aisera's AI Copilot directly through Aisera's sales team. The enterprise nature of the product means I needed custom contract terms and SLA agreements that are better handled through direct procurement than marketplace listings.

Several practical pieces of advice for those looking into using Aisera's AI Copilot include investing heavily in knowledge base quality before going live. Aisera's AI Copilot is only as good as the information it has access to; garbage in, garbage out applies strongly here. Start with the highest volume, repeated tickets: identify your top ten most common IT requests and configure Aisera's AI Copilot to handle those perfectly before expanding the scope. Set clear employee expectations by communicating what Aisera's AI Copilot can and cannot do; employees who understand the system use it more effectively. Monitor hallucinations actively in the early weeks by reviewing AI responses regularly during initial deployment to catch and correct inaccuracies before they erode user trust. Finally, keep a human in the loop for sensitive issues; never fully automate HR, legal, or security-related queries, and always route those to human agents.

From a security perspective, Aisera's AI Copilot takes AI governance seriously, which is especially important since it handles sensitive employee and organizational data. Data encryption at rest and in transit, SOC 2 Type II compliance, and role-based access control are all in place. Employee query data is handled with appropriate privacy controls. From a governance perspective, administrators can define guardrails around what topics the AI can and cannot respond to; that level of control is essential for enterprise deployments. However, AI transparency could improve understanding of exactly how the model makes decisions and which training data influence responses, which would strengthen governance for compliance-heavy organizations. Overall, the foundation for governance is solid, but as AI capabilities expand, more granular oversight tools will be needed.

The accuracy of Aisera's AI Copilot is generally strong. For common IT scenarios such as password resets and access requests, accuracy is consistently above ninety percent. The system handles well-defined, repeated queries accurately. However, for complex, ambiguous queries, accuracy drops to around seventy to seventy-five percent, as the system sometimes misunderstands context or generates plausible but incorrect responses. Overall, I would rate accuracy an eight out of ten for standard queries and a seven out of ten for complex ones.

My overall review rating for Aisera's AI Copilot is eight out of ten.

Public Cloud

Amazon Web Services (AWS)

What do you like best about the product?

Use of LLM Models and integration of the same in Multi Agent Mode

What do you dislike about the product?

Work on more in person support rather offline

What problems is the product solving and how is that benefiting you?

Aisera solves the problem of inefficient, manual and slow employee/customer support by providing an Agentic AI platform that automates resolutions rather than just answering questions.

Problems Aisera Solves:
1. High Vilume of Repetitive Tickets
2. Slow Mean Time to Resolution (MTTR)
3. 24/7 Support Demands

Key Benefits of Aisera:
1. Auto-Resolution of Tickets (65% - 80%)
2. Increased Productivity
3. Improved Employee/Customer Satisfaction (CSAT)

What do you like best about the product?

Aisera is a fruitful AI driven approach that foster automation in both generative and conversational AI, to manage customer interactions
Aisera works great with popular solutions like Microsoft Teams, ServiceNow, among others
The tool has a remarkable self service functionality that helps employees and clients solve issues with human interactions

What do you dislike about the product?

Aisera is a technically solid approach that demands extensive and detailed fine tuning, which is complex and draining
To firms with financial problems, the cost for running Aisera is not too basic

What problems is the product solving and how is that benefiting you?

We use Aisera to automate tasks that occurs often and that requires customization to meet personalized needs
We get instant self service solutions from the app, without the hassle of human agent
The systematic automation ensure employees are constantly working towards other responsibilities other than the automated ones
The customer satisfaction levels have significantly improved due to timely

What do you like best about the product?

I love the way it has a nice interfaces b

What do you dislike about the product?

I liked everything about it it is so good

What problems is the product solving and how is that benefiting you?

A lot for problems

What do you like best about the product?

I like how Aisera automates repetitive service desk requests across IT and HR. The AI can understand natural language pretty well and gives quick responses, which makes it easier for employees to get help without waiting on a support agent.

What do you dislike about the product?

The platform is powerful, but it takes some effort to set up and fine-tune for specific business needs. The initial configuration and training the AI to match our workflows needed extra time.

What problems is the product solving and how is that benefiting you?

Aisera is helping reduce repetitive IT and HR support requests by giving employees an AI-powered self-service option. Many common issues get resolved instantly without waiting for an agent, which saves time, lowers ticket volume, and improves overall productivity for both employees and support teams.

What do you like best about the product?

I like how you can save time and boost productivity by searching or modifying pre-built agent templates that help your team get started fast and see value in days, not months.

What do you dislike about the product?

As of now, I have no complaints about utilizing this platform.

What problems is the product solving and how is that benefiting you?

It helps retrieve information from any knowledge source with the ability to trigger actions concurrently for end-to-end task completion.

What do you like best about the product?

What I like best about Aisera is how seamless it makes the whole support and service experience. It feels less like a traditional ticketing system and more like having a smart, always-available teammate who can handle the routine stuff instantly and still learn from every interaction. I admire how it blends AI with a real understanding of how people work—it’s not just about automating tasks, but making everyday work smoother and freeing people up to focus on the things that really matter.

What do you dislike about the product?

Honestly, Aisera is pretty powerful and does a lot with automation, but it’s not something you can just plug in and forget about. It takes a fair bit of setup and you’re constantly tweaking things to keep it working the way you want. Their customer support can be a bit slow, which is annoying when you hit a problem. Plus, the pricing feels a little murky — there are extra charges for things like setup or expanding to more teams, so it’s hard to know the real cost upfront. It’s a great tool if you’ve got complex needs, but it can feel like a lot to manage (and pay for).

What problems is the product solving and how is that benefiting you?

Aisera automates routine IT and support tasks, freeing a data analyst from constant interruptions. It quickly provides answers, documentation, and even code snippets, helping speed up Python development. With integrations and instant alerts, it reduces context switching and keeps workflows smooth. Overall, it makes coding and data analysis faster, easier, and more focused.

What do you like best about the product?

It’s made a real difference in how quickly we can respond to requests. The virtual assistant understands most questions really well and points people to the right answers without involving the helpdesk every time. Integrations with tools we already use, like ServiceNow and Slack, were smooth, and the reporting gives us a good idea of where we can improve.

What do you dislike about the product?

Getting everything set up the way we wanted took more time than expected, and some of the advanced reports aren’t as easy to use as they could be. It’s also on the pricier side if you’re in a smaller company.

What problems is the product solving and how is that benefiting you?

Aisera is helping us handle repetitive IT support requests without always needing a human agent. Password resets, software access requests, and basic troubleshooting are resolved faster through the AI agent, which frees up our support team to focus on complex issues. This has reduced ticket backlog, improved response times, and made the overall experience smoother for end users.