Technical consumer products companies face increasing pressure to deliver fast, accurate, and consistent support as products grow more complex and teams scale globally. Most internal support systems struggle with fragmented knowledge, rapidly changing documentation, and limited access to subject matter experts, which leads to long handle times, unnecessary escalations, and uneven customer experiences. Generative AI offers new possibilities, but most agent-assist tools fall short without access to structured, contextual knowledge. This leading global device company turned to Implicit, an AI-powered copilot purpose-built to assist customer support teams with real-time, product-aware expertise.
The challenge
The company had previously launched an internal GenAI chatbot to assist Customer Support Agents (CSAs), but it fell short in several critical ways. Responses were unreliable, the tool returned incorrect or incomplete answers nearly 40% of the time, which led to mistrust and low usage. Agents frequently bypassed the chatbot, reverting to manual search, peer Slack channels, or direct escalation to subject matter experts.
The underlying problem was the knowledge itself. Product information was scattered across unstructured internal documents, manuals, tickets, and engineering notes. High-value insights were hard to extract from domain experts and even harder to scale across the organization. The company needed a solution that could reliably surface accurate, context-specific guidance to agents without sacrificing speed or confidence.
The solution: an embedded copilot built on structured knowledge
To rebuild their internal support assistant, the company implemented Implicit Support, a product expert copilot that works alongside CSAs within their desktop workflow. Built on proprietary AI technology that prioritizes accuracy above all else, it was not just a chatbot. It was a trusted teammate that could answer, explain, and guide with product-specific clarity. The platform turns raw, scattered content into governed answers through a deliberate process:
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Knowledge extraction and structuring
Implicit ingests content from manuals, internal wikis, historical tickets, and engineering notes, then structures that knowledge using automated tagging and entity mapping.
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Product and situation taxonomy
A predefined taxonomy of devices, issues, configurations, and symptoms classifies knowledge so responses stay consistent and tied to real product context.
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Knowledge graph and GraphRAG
Implicit maps relationships between products, error states, resolution paths, and symptoms into a knowledge graph. GraphRAG, combining graph and vector retrieval, feeds the copilot only the most relevant, validated knowledge, avoiding the hallucinations and noise common in generic LLM setups.
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Curated walled garden
The customer's support operations team curated the walled garden of content, ensuring relevance, accuracy, and control over what the copilot is allowed to draw from.
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Explainable, embedded copilot
The copilot is embedded directly in the agent's support desktop via API, returning answers in real time, citing source content, and walking agents through troubleshooting steps. No more bouncing between tools or searching multiple sources.
The results
The transformation was immediate and measurable. Once agents saw that answers were accurate and grounded in real knowledge, the copilot became a daily tool rather than a backup plan.
Increase in issue resolution rates
Agents resolved more issues faster, with fewer escalations or dead ends.
Boost in first contact resolution
Armed with structured, guided answers, CSAs handled more cases on the first attempt, reducing customer effort and support costs.
Significant increase in adoption
When the copilot proved accurate and grounded in real knowledge, usage rose quickly and it became part of agents' daily workflow.
Organizational trust in AI rebuilt
With source-backed, explainable answers, leaders regained confidence and began expanding AI usage into other parts of the support ecosystem.
Why structuring knowledge came first
By implementing Implicit Support, this global tech leader turned a low-adoption GenAI tool into a high-trust, high-impact copilot for Customer Support Agents. The result was faster resolutions, more confident agents, and a measurable step forward in AI-powered support. For any company supporting complex technical products at scale, success does not come from layering AI onto a messy knowledge base. It comes from structuring that knowledge first, then embedding it into every support interaction. That is the power of Implicit's KnowledgeOS.