Chamberlain Group achieves fewer escalations and higher customer satisfaction with agentic AI

This company’s support complexity is unusual for a consumer brand. When a residential customer asks why their garage door opener isn’t responding, the answer often depends on their specific model, firmware version, connectivity setup, and whether they’ve already attempted a reset. When a commercial customer asks about a gate operator malfunction, the diagnostic path is entirely different and may pull from different product documentation and integration systems.

The previous chatbot couldn’t navigate this complexity. Its limitations created specific problems that compounded across their large customer base:

• Rigid conversation flows: The chatbot operated on pre-built intent trees. Multi-turn troubleshooting, where the next question depends on the customer’s last answer, required static flows that broke whenever a customer’s situation didn’t match the script.
• No differentiation between customer segments: Residential homeowners and commercial facility managers have fundamentally different needs. The existing system treated them the same, routing both into generic support paths regardless of product complexity or account type.
• Separate architectures across brands: Multiple brands each had distinct product lines and documentation sets. Managing separate support infrastructure for each, while also serving international markets in multiple languages, created mounting maintenance overhead.
• Inability to use account-level data: When a customer had a known device registered to their account, the old system couldn’t pull that data into the conversation. Human agents had to ask basic questions that a system with CRM access could have answered automatically.

Quiq built two AI agents, one for residential customers and one for commercial, sharing the same core architecture but equipped with distinct process guides, knowledge sets, and escalation logic suited to each segment.

The deployment was built around four capabilities:

• Data Transformation: Their support content includes detailed product manuals, troubleshooting guides, and device specifications across multiple brands and product lines. Quiq’s platform uses large language models and data processing techniques to ingest this content in its original format, chunk it into focused sub-articles, attach related questions, and make it usable for the AI agents without requiring the team to manually reformat anything.
• Platform Integrations: Quiq’s platform connects to Genesys, their IVR system, and the company’s Salesforce Service Cloud instance. This integration enables the AI agents to pull account-specific information during a conversation, route escalations to the right team with full context attached, and generate reporting across all interaction channels.
• Orchestration Layer: Each AI agent is equipped with specific Process Guides, tailored sets of instructions and troubleshooting steps for relevant product lines. For every incoming message, the orchestration layer classifies the customer’s intent, identifies the product and issue type, and selects the appropriate Process Guide. It also manages multi-turn conversation flows without hard-coded decision trees, allowing the agent to adapt based on what the customer says next.
• Agentic Reasoning: Both AI agents use multi-turn diagnostic conversations rather than single-answer lookups. They evaluate each customer message against the current troubleshooting guide, the product and account information available, and the full conversation history to provide step-by-step guidance, ask targeted clarifying questions, and adapt their approach as the diagnosis progresses. A response is validated before it’s sent to prevent inaccurate answers.

The team manages both AI agents through Quiq’s AI Studio. Because the residential and commercial agents share the same underlying architecture, content updates that apply to both (shared knowledge base articles, for example) propagate automatically. The team uses AI Studio to monitor each agent’s decision-making in real time, run test scenarios before deploying changes, and iterate on process guides without requiring engineering support.

The most significant change from the customer’s perspective is that troubleshooting now happens in a real conversation rather than a static flow.

Residential device troubleshooting: A homeowner contacts support because their garage door opener stopped responding. The residential AI agent asks which model they have, looks up the device specifications, confirms whether it’s Wi-Fi enabled, asks about recent changes to their home network, and walks them through a targeted diagnostic sequence based on their specific setup. If the issue isn’t resolved in chat, the AI agent escalates to a human agent with a full summary of the diagnostic steps already taken, so the human doesn’t repeat questions the customer has already answered.

Commercial account support: A facilities manager for a commercial property asks about a gate operator that isn’t cycling correctly. The commercial AI agent identifies the product from their registered account, pulls the relevant technical documentation, and walks through a diagnostic sequence specific to commercial-grade operators. It can distinguish between power supply issues, sensor misalignment, and control board errors in a way that a generic troubleshooting flow cannot.

Cross-brand knowledge: A customer contacts support with a question that spans both a residential product and a commercial component connected to the same system. The AI agent resolves it using the shared architecture, pulling from both brand knowledge bases without requiring the customer to be transferred between systems.

The support team’s workload shifted from first-line triage to focused handling of genuinely complex escalations. Routine questions, including product FAQs, connectivity troubleshooting, and account lookups, are handled by the AI agents before a human is involved. When the AI agents do escalate, the human agent receives a full transcript, a structured summary of the issue, and the diagnostic steps already completed. The handoff takes seconds instead of minutes.

The team also has more visibility into what’s actually going wrong with customers than they did before. Quiq’s AI analyst surfaces patterns across thousands of conversations, making it easy to identify which product issues are driving the most inbound volume and whether the current process guides are resolving them.

This Smart Access Manufacturer now operates two specialized AI agents that handle residential and commercial support across multiple brands and international markets simultaneously. The results compared to their previous chatbot include:

• Fewer escalated conversations: Multi-turn troubleshooting that previously required human agents is now resolved by the AI agents
• Higher customer satisfaction compared to the previous-generation chatbot
• International and multilingual inquiries handled across chat and voice without separate system maintenance
• Human agents freed to focus on complex, strategic customer issues requiring discretion and judgment
• AI Studio gives the team full visibility and control to update, test, and improve agent behavior without engineering support