Consumer Electronics Copilot - Internal Knowledge & Support

Enhancing Accuracy and Reducing Hallucinations By Using a Knowledge Layer to Structure the Unstructured

Industry

Global Consumer Electronics Manufacturer

Services

Support Workflow Design

Enterprise AI Integration

Software

Approach

AI Copilot embedded in agent workflow leveraging a "Walled Garden" AI approach to enhance understanding and context, increase accuracy, and reduce hallucinations.

Introduction

Technical consumer products companies face increasing pressure to deliver fast, accurate, and consistent support experiences, especially as products grow more complex and support teams scale globally. Yet, most internal support systems struggle with fragmented knowledge, rapidly changing documentation, and limited access to subject matter experts. The result? Long handle times, unnecessary escalations, and uneven customer experiences.

While generative AI offers new possibilities, most agent-assist solutions struggle without access to structured, contextual knowledge. That’s why 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.

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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:

Inaccurate Responses: The tool returned incorrect or incomplete answers nearly 40% of the time, leading to mistrust and low usage.
Low Adoption: CSAs frequently bypassed the chatbot, reverting to manual search, peer Slack channels, or direct SME escalation.
Fragmented Knowledge Sources: Product knowledge was scattered across unstructured internal documents, manuals, tickets, and engineering notes.
Bottlenecked Expertise: 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

Implicit Support as an AI Copilot for Agents

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To rebuild their internal support assistant, the company implemented Implicit Support, a product expert copilot that works alongside CSAs within their desktop workflow. Implicit Support is built on proprietary AI technology that prioritizes accuracy above all else.

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This wasn’t just a chatbot. It was a trusted teammate that could answer, explain, and guide with product-specific clarity.

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Key Solution Elements

Knowledge Extraction & Structuring
Implicit ingested content from manuals, internal wikis, historical tickets, and engineering notes, then structured that knowledge using automated tagging and entity mapping. The customer’s support operations team curated this “walled garden” of content—ensuring relevance, accuracy, and control.
Product + Situation Taxonomy
Implicit used a predefined taxonomy of devices, issues, configurations, and symptoms to classify knowledge and ensure consistent responses tied to real product context.
Knowledge Graph + GraphRAG
The platform mapped relationships between products, error states, resolution paths, and symptoms into a Knowledge Graph. Using GraphRAG (graph + vector retrieval), it fed the copilot only the most relevant, validated knowledge—avoiding hallucinations and noise common in generic LLM setups.

Explainable, Embedded Copilot

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The copilot was 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.

RESULTS

The transformation was immediate and measurable:

Significant Increase in Agent Copilot Adoption
Once agents saw that answers were accurate and grounded in real knowledge, usage of the copilot rose quickly. It became a daily tool—not a backup plan.
25% Increase in Issue Resolution Rates
Agents could resolve more issues faster, with fewer escalations or dead ends.
10%+ Boost in First Contact Resolution (FCR)
Armed with structured, guided answers, CSAs handled more cases on the first attempt—reducing customer effort and support costs.
Organizational Trust in AI Rebuilt
The improved copilot set a new standard for AI in support. With source-backed answers and explainability, leaders regained confidence and began expanding AI usage into other parts of the support ecosystem.

Conclusion

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.

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For any company supporting complex technical products at scale, success doesn’t come from layering AI onto a messy knowledge base. It comes from structuring that knowledge first—and embedding it into every support interaction.

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That’s the power of Implicit's KnowledgeOS.

Testimonials

"I'm impressed by Implicit"

"I'm impressed by Implicit, a company that uses Al to help find, target, and summarize information for the Dept. of Defense and do the same for our enterprise data sources (structured and unstructured)."

Ted Danner

Senior Director, Circuitry.ai

"Without generating a ton of noise"

"The system can identify entities really only related to our taxonomy...without generating a ton of noise."

Anonymous

ML & NLP Technical Program Manager

"Better than any results I’ve seen"

"Implicit’s generative AI produces accuracy and precision results that are better than any results I’ve seen to date across a wide swath of intelligence-focused technologies."

Terry Bush

General Partner, Wave Data

"Trillions of data points"

"My team owns the taxonomy and relationships for trillions of data points across these different products. So this very much resonates with me."

Anonymous

Platform Product Manager

"I wish I had found Implicit years ago!"

"I wish I had found Implicit years ago! They are the first company I have evaluated that has true, dynamic Al technology that has been proven in other data-rich sectors."

Jason Payne

CEO, JP Strategy

"20-30% of our time chasing support-related issues"

"We spent 20-30% of our time in customer success chasing support-related issues. The value of automating that is significant."

Anonymous

VP, Global Customer Success

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