An AI support agent that answers from your approved knowledge, resolves routine questions across chat and email, and hands complex cases to your team with full context.
The problem
Support teams answer the same questions every day across channels, while complex issues wait in the same queue. Customers wait, and agents burn out on repetition.
How it works
Routine questions answered from approved knowledge; complex cases handed over with the full thread.
- GroundWe connect the agent to approved help content, policies and product data.
- AnswerIt answers routine questions and cites the source it used.
- Look upWith permission, it checks order or account status in your systems.
- Hand offAnything sensitive or unclear goes to a person with the conversation attached.
- LearnUnanswered questions become a list of content gaps to fix.
Where people stay in control
- Refunds, exceptions and complaints go to people
- The agent says when it does not know
- Conversations can be reviewed by the team
What it connects to
How we deliver ai customer support agent
The same method as every AI Agents & Automation engagement: each stage ends with a decision you sign off. We work with teams in Bengaluru, across India and abroad.
We study the problem with the people who live it: workflow, customers, constraints.
Problem brief agreedWe agree what success means, what is in scope and the hypothesis we are testing.
Scope signed offFlows, interfaces and architecture, tested as a prototype before engineering starts.
Prototype approvedWorking software and artefacts, demonstrated every week.
Features acceptedRelease to real users with documentation, monitoring and a runbook.
Release acceptedWe measure what happened and turn it into the next roadmap.
Roadmap reviewedRelated insights
- What is an AI-native company?An AI-native company redesigns workflows, decisions, data access and software so AI is part of how the business runs. What it means, how it differs from adding AI tools, and how to start.
- What should an AI agent be allowed to do? A practical modelA practical permission model for AI agents: four levels of action from read-only to high-impact, how to enforce them in code rather than prompts, and how to design the handoff to people.
- The anatomy of a production AI agentKnowledge, tools, permissions, a provider abstraction, evaluation and observability: the parts that turn an AI demo into a production AI agent a business can rely on.
How do you stop an AI support agent from making things up?+
By grounding it in approved content, requiring it to answer only from retrieved sources, testing it against known questions, and making 'I don't know, let me connect you' an explicit, acceptable outcome.
Does it replace the support team?+
It removes repetition so the team can focus on the cases that need judgment. The handoff is designed as carefully as the automation.
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