Chat with us!We built an autonomous AI support agent that resolves customer tickets end to end — understanding intent, retrieving account context, and taking real actio...
It feels like we hired a tireless senior agent who never sleeps and never gets a ticket wrong.
We built an autonomous AI support agent that resolves customer tickets end to end — understanding intent, retrieving account context, and taking real actions across connected systems. It handles the high-volume repetitive queries that previously consumed most of the support team's day.
| Project Detail | Information |
|---|---|
| Project Type | Web |
| Industry | Customer Service |
| Technologies | GPT-4 class LLM, LangGraph, Pinecone, Next.js, PostgreSQL, Redis |
| Delivery Partner | mTouch Labs |
| Primary Outcome | Within three months the AI agent was resolving the majority of inbound tickets without human touch, freeing agents for complex, high-value work. |
The client's support team was drowning in repetitive tickets, with first-response times stretching into hours and agents burning out on copy-paste answers. Their existing rule-based chatbot deflected almost nothing because it could not understand context or take action.
We designed an agentic architecture where a reasoning LLM plans each resolution, calls tools to fetch live data, and escalates to humans only when confidence is low. Retrieval-augmented generation grounds every answer in the client's real knowledge base to eliminate hallucinations.
The delivered platform pairs a tool-using AI agent with a human-in-the-loop console, secure API connectors, and full audit logging. Support leaders get a live dashboard of deflection rate, sentiment, and escalations.
Reasoning, planning, and natural-language responses
Stateful multi-step agent orchestration
Vector store for knowledge-base retrieval
Agent console and analytics dashboard
Conversation state and audit logging
Low-latency session and rate-limit handling
Clustered 12 months of tickets to find the highest-volume automatable intents.
Chunked and embedded help-center, policies, and macros into the vector store.
Built the planning loop and secure, permission-scoped action tools.
Added grounding checks, refusal rules, and an automated eval suite.
Shadow-mode pilot, then phased rollout with live human oversight.
Within three months the AI agent was resolving the majority of inbound tickets without human touch, freeing agents for complex, high-value work.
Agentic AI turned a reactive, overloaded support queue into a proactive, largely self-serving system — proving that grounded, tool-using agents can safely own real customer outcomes.
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View case studyWhat businesses ask us most often about this project and how we built it.
It is an autonomous system that understands a customer query, retrieves the relevant account and policy context, and resolves the request — including taking actions in connected systems — escalating to a human only when needed.
Every response is grounded with retrieval-augmented generation against the company knowledge base, and low-confidence cases are routed to human agents with full context.
Yes. We build secure, permission-scoped connectors to CRM, billing, order, and helpdesk platforms so the agent can both read context and take approved actions.
A typical pilot is live in 4-6 weeks, followed by phased rollout once deflection and CSAT targets are validated in shadow mode.
All actions are permission-scoped and fully audit-logged, and data handling is designed to meet enterprise security and compliance requirements.
mTouch Labs combines AI-powered development with deep industry expertise to deliver solutions faster.
