Aisera

What is Aisera?

Aisera is an AI service platform that resolves employee and customer requests across service desks. It answers questions and acts on tasks for IT, HR, and customer support, and it does so from a single platform that spans multiple domains. When someone submits a request, Aisera reads the intent, draws on your approved knowledge and connected systems, and either resolves the task or routes it to a human with the full history attached.

Aisera makes the platform. The company built its product around autonomous resolution, which means the goal is to close requests without a human where it can, then pass the rest to agents with context. That design suits organizations whose request volume would swamp a service desk that leans on people for every ticket.

The audience is large service desks. Aisera targets service and support teams that field high, repeat request loads across IT, HR, and customer channels, where multidomain coverage and system integrations matter as much as raw resolution. These teams want to deflect the common cases so agents can spend their hours on the requests that need judgment.

Key features

Aisera centers on a set of capabilities that work together to automate resolution at scale:

• ▸Service automation: Aisera resolves common requests on its own, from password resets and access grants to status checks and policy questions, so routine work leaves the queue.
• ▸Multidomain coverage: one platform serves IT, HR, and customer service desks, so a single deployment supports employee and customer requests.
• ▸Agent assist: for the cases that reach a person, Aisera drafts replies and surfaces relevant context, so human agents respond with less digging.
• ▸Resolution analytics: dashboards show resolution rate, deflection, and topic trends, so leaders see what Aisera closes and where gaps remain.
• ▸System integrations: Aisera connects to identity, service management, and business systems, so it can act on a request rather than point at an article.
• ▸Human handoff: when a request sits outside its scope, the conversation routes to an agent with the full thread and context in place.

Service automation matters most for the value case. Because Aisera acts on requests through connected systems, it closes tasks end to end rather than answering with a link, which is where the deflection comes from. Agent assist covers the remainder: on the requests that need a person, it shortens the reply by drafting a response and surfacing context, so the platform lifts both the automated and the human side of the desk.

How well does it work?

Aisera performs well on the high-volume, repeatable requests that fill a large service desk. For organizations with solid knowledge and connected systems, it resolves a meaningful share of requests without a human, and it does so across IT, HR, and customer domains from one platform. The multidomain reach is a standout: teams running several desks get one deployment instead of a separate tool per function.

The limits track the automation-first model. Aisera depends on your source content and the systems you connect, so gaps in either become gaps in what it can resolve. Nuanced or account-specific requests tend to route to a person, which is the intended behavior but caps how high the resolution rate can climb. The platform aims at larger operations, so a small team with modest volume may not use enough of its breadth to justify the fit.

A practical way to raise Aisera's resolution rate is to review the requests it hands off, then add the missing integration or knowledge article for the patterns you see. The assistant improves as your content and connected systems grow.

Aisera pricing

Aisera uses custom pricing. There are no public rates. The platform scopes a quote to your account based on the domains you cover, the systems you connect, request volume, and the modules you deploy. To get a number, you talk to the Aisera sales team.

Here is how the main cost drivers fit together so you can prepare for that conversation:

Because the model is quote-based, treat the drivers above as the levers you can size. A focused deployment on one desk with a few integrations costs less than a full multidomain rollout wired into many systems, so scope the pilot to the volume you want to prove first.

Who should use Aisera?

Aisera fits organizations that run large service desks and want autonomous resolution across employee and customer requests. The clearest matches share a few traits:

• ▸Enterprises with high IT and HR request volume that want to deflect the common, repeatable tasks from the queue.
• ▸Customer support operations that field large, repeat query loads and want automation to close the routine cases.
• ▸Teams running several service desks that prefer one multidomain platform over a separate tool per function.
• ▸Organizations with connected systems that want the assistant to act on requests, not point at articles.

Aisera is a weaker match for small teams with light request volume, or for organizations with few systems to connect, where most requests would route to a human anyway. In those cases the enterprise scope and custom pricing have fewer wins to justify the spend.

Alternatives and how it compares

Aisera competes with a growing field of enterprise service and support agents. The right comparison depends on your stack and how deep you want the automation to run.

• ▸Moveworks: an employee support agent focused on IT and HR resolution inside chat, aimed at teams that want automation built around their own systems.
• ▸ServiceNow Now Assist: a strong fit for teams already on ServiceNow, with deep ties to its service management platform and workflows.
• ▸Freshworks Freddy: an AI assistant across the Freshworks suite that handles employee and customer support for teams on that platform.

The edge for Aisera is its multidomain reach and the breadth of systems it connects to for autonomous resolution. If you run several busy desks at scale and want one platform across them, it is built for the job. If you sit on a broad service management platform, the native agent from that vendor may integrate with less work, so weigh platform fit alongside the depth of automation.

Limitations and getting started

Be honest about the trade-offs before you commit. Aisera resolution rate rides on the systems you connect and the content you supply, so a thin setup limits results. The custom pricing means a sales call before you see cost, and the enterprise focus makes it a bigger lift than lighter chatbot tools for small teams.

Getting started follows a clear path:

1. 1Book a scoping call so the team can size the deployment and map the domains and systems to connect.
2. 2Connect your identity, service management, knowledge, and business systems so the assistant has sources and actions.
3. 3Set guidance and topic scope so the assistant stays on policy and hands off where you want a human.
4. 4Turn Aisera on for a subset of desks or request types, then review deflection reports and fill gaps before a wider rollout.

A staged rollout keeps risk low: start with one desk and a few request types, confirm the resolutions hold up, then widen coverage as your numbers earn trust. The early phase is where you tune content, connect systems, and prove the deflection before you scale across the organization.