AI customer support agents that resolve requests, not just answer questions
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An AI customer support agent understands natural language, uses approved knowledge and carries out authorized actions such as checking orders or updating records. Loonar designs, deploys, trains and supports agents across service channels, helping resolve routine requests while passing complex cases to people with a clear summary and relevant context.
In short
- Define what resolution means.
- Keep knowledge and policies current.
- Hand complex cases to people with context.
How do you know you need AI customer support agents?
Queues outlast business hours
Customers need help when the team is unavailable and receive only a waiting message. By the next shift, yesterday's requests compete with new arrivals. Straightforward issues become longer conversations because customers have already tried other routes or repeated their request.
Contact volume drives cost
When every request requires manual handling, growth puts pressure on staffing and capacity. Routine questions compete with sensitive cases for attention. Adding people may ease the queue, but it does not address why the same avoidable work keeps reaching the team.
Answers depend on the person
Outdated articles and scattered instructions lead agents to interpret policy differently. Customers contact another channel to verify what they were told. The team then spends time correcting contradictory guidance instead of resolving requests against a shared and maintained source of knowledge.
Menus miss the actual need
Decision trees and voice menus ask customers to fit their situation into internal categories. When none of the choices matches, they repeat steps or abandon the interaction. A later handoff often loses the explanation they already gave to the system.
How does it work?
Shared knowledge across channels
Serve customers through chat, email, voice, WhatsApp and social channels using one approved knowledge base. Adapt delivery to each channel while keeping automated availability distinct from human team coverage.
Grounded answers
Organize articles, policies and authorized historical material for retrieval. Assign content owners and review source quality so past conversations do not silently become current policy or expose information meant for restricted use.
Connected service actions
Check orders, retrieve status, update records or issue duplicate documents through approved integrations. Validate identity and permissions before acting, confirm results and provide a recovery path when systems reject a request.
Natural language voice
Let customers describe their needs rather than navigate rigid menus. Configure recognition, confirmation and escalation to handle background noise, ambiguous wording and requests that require a person's judgment or additional verification.
Handoffs without repetition
Give the service team a useful summary, relevant history and actions already attempted. Define transfer triggers and outstanding tasks so the next person can continue without asking customers to repeat everything.
Boundaries, tone and auditability
Apply brand voice, response restrictions and audit records over the existing help desk where supported. Use least-privilege access and conversation review to find failures without collecting unnecessary personal information.
How does Loonar deliver?
Contact analysis
Loonar reviews recurring requests, operational risk and automation potential with your service team. Together we choose an initial scope and define observable standards for resolution, answer quality and timely human escalation.
Knowledge curation
We review articles, policies and missing guidance before configuring responses. Named owners maintain source validity and access permissions, while explicit boundaries tell the agent when the available evidence is insufficient to proceed.
Actions and integrations
We connect approved queries and operations to the required systems, retaining your help desk where feasible. Configuration covers identity checks, permissions, result confirmation and recovery from service outages or rejected operations.
Simulations and team training
We test routine requests, ambiguity, attempts to bypass restrictions and integration failures before release. We train the team to supervise answers, receive handoffs and report knowledge gaps using clear review criteria.
Gradual rollout and ongoing improvement
We launch by channel and review resolution, quality and transfers together. Conversation evidence guides approved changes to content and actions, with clear owners for requests that still lack an appropriate resolution path.
Which technology does Loonar use to deliver it?
What results do we measure?
- Automated resolution rate
- Measures the share of requests resolved by the agent under explicit completion criteria.
- CSAT for AI conversations
- Tracks customer-reported satisfaction after interactions handled by the agent.
- Average handling time
- Measures interaction duration with attention to scope and human transfers.
- Cost per contact
- Relates operating costs to the contact volume over the period reviewed.
- Volume transferred to humans
- Tracks how many requests reach people and why they require escalation.
Traditional chatbot or AI customer support agent?
Compare understanding, execution and upkeep before setting the scope of support automation.
| Aspect | Traditional chatbot | AI customer support agent |
|---|---|---|
| Understanding | Predefined choices and intents | Natural language within defined boundaries |
| Actions | Configured workflows | Authorized integrated actions |
| Maintenance | Update trees and scripted answers | Curate knowledge and test behavior |
| Experience | Customer navigates options | Customer describes the need |
| Continuity | Transfer based on workflow | Summary, context and attempted actions |
Where do we apply it?
Retail and ecommerce
Answer order-status and returns-policy questions using authorized sources. After appropriate verification, the agent retrieves relevant details and hands exceptions to the service team with history attached. Delivery disputes, billing discrepancies and requests outside policy remain open for review rather than being labeled resolved because an answer was sent.
Financial services
Handle operational questions and route requests according to customer identity and access permissions. The agent retrieves only authorized information and avoids exposing sensitive details through unsuitable channels. Disputes, suspected fraud and decisions requiring specialist review go to people with a record of the steps already completed during the interaction.
SaaS
Help users with access, configuration and usage questions using approved articles and permitted account queries. The agent gathers evidence before escalating issues that need technical investigation. The service team can continue troubleshooting from that history, while resolution criteria prevent a suggested fix from being mistaken for confirmation that the problem is solved.
Frequently asked questions
What is the difference between a chatbot and an AI agent?
A traditional chatbot generally guides customers through predefined choices and scripted responses. An AI customer support agent can interpret natural language, consult approved sources and carry out integrated actions. That still requires careful process design and supervision. Loonar designs, deploys, trains and supports AI customer service with clear boundaries, resolution criteria and escalation points, rather than treating a fluent answer as proof that a request has been resolved.
Do we need to replace our help desk?
Not necessarily. The agent can work with your existing help desk when its interfaces, permissions and features support the required workflow. Discovery checks history access, interaction logging and contextual handoffs, along with limitations that might require process changes. The goal is to preserve continuity and avoid duplicate queues. Compatibility must be verified for the specific environment rather than assumed from a general claim about integration support.
How does the agent avoid making up answers?
Responses should rely on approved sources, with explicit boundaries for unsupported topics and escalation when evidence is missing. Pre-release testing, conversation review and content maintenance help reduce incorrect answers, but no safeguard eliminates the risk entirely. Support automation must also distinguish a retrieved fact from a completed action. The agent should never describe an attempted update as successful unless the connected system has confirmed the result.
Can the agent handle voice calls?
Yes. Voice service can let customers explain their needs naturally instead of selecting menu options. Feasibility depends on the available telephony infrastructure and integrations. The design needs to account for noise, interruptions, data confirmation and human transfer. Sensitive actions require appropriate identity checks and confirmations before execution; understanding a spoken request is not, by itself, sufficient authorization to reveal account information or change a customer record.
What happens to customer data under Brazil's LGPD?
The project should define purpose, legal basis, required data, retention and processing responsibilities with legal and privacy owners. Least-privilege access, audit records and cybersecurity controls must follow the workflow. Personal information should not enter a knowledge source without review. Vendor arrangements, international transfers and data subject rights also need assessment. Deploying an AI customer service agent does not automatically make an operation compliant with the LGPD.
Shall we design the next version of your operation?
Tell us the challenge. One conversation with a specialist gives you a clear path, even if it isn't time to buy yet.