One of the world's largest technology companies, serving millions of customers across hardware, software, and IT infrastructure, needed to modernize and scale its AI support experience in an era of rising product complexity, content sprawl, and growing customer expectations. With thousands of products and configurations and a vast body of technical documentation, the company needed a way to connect customers to the right answers quickly. Traditional search tools and out-of-the-box GenAI solutions frequently returned irrelevant or misleading answers, eroding trust and pushing customers toward costly live support. The company partnered with Implicit to deliver an AI-powered virtual expert grounded in structured, product-aware knowledge and engineered for precision.
The challenge
The organization launched the initiative with a clear set of objectives, all centered on improving the accuracy, usability, and impact of its self-service support experience. It wanted to reduce the time customers spent finding accurate answers so they could resolve technical issues quickly and independently. It needed to improve the precision of AI responses by minimizing false positives, hallucinations, and vague results, which is critical for trust in high-complexity environments. It also aimed to automatically organize and tag support content for smarter retrieval, filtering, and routing, and to identify documentation gaps and duplicates so the AI always had access to complete, up-to-date knowledge.
The company had invested heavily in documentation and support infrastructure, but several deep-rooted issues limited the effectiveness of its self-service experience. Documentation spanned a wide range of products and configurations with inconsistent structure, language, and formatting. Poor metadata and tagging made it difficult to surface the right content at the right time. Standard GenAI solutions returned overconfident, inaccurate answers that eroded trust and increased escalation rates. Without precision and relevance, customers reverted to contacting support directly, driving up costs.
The solution: a structured, product-aware knowledge layer
The company implemented Implicit Knowledge, a specialized AI-powered virtual expert for product support that brings structure, accuracy, and context to self-service interactions. Rather than relying on generic vector search or LLM prompts, Implicit grounds every response in a curated knowledge graph and exposes the capability through APIs the company integrated into its customer-facing chatbot, internal tools, and knowledge portals. Content flows through a governed pipeline:
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Product and situation taxonomy preloading
Implicit discovers and preloads a highly accurate, domain-specific taxonomy of both products and support situations, enabling precise extraction of product-contextual knowledge from unstructured content. The system can identify what a document is about and which problems it addresses.
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Knowledge extraction and normalization
The Implicit Knowledge Extractor processes manuals, knowledge articles, and historical cases to extract and normalize key content, mapping each section to product and situation entities and converting raw content into a structured graph of support knowledge.
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Knowledge graph construction
Implicit creates a graph of relationships between products, situations, and the relevant sections of documents. This lets the system run graph queries that pinpoint the most semantically relevant sections without relying solely on keyword matching.
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Graph and vector hybrid querying
A key differentiator is how Implicit combines graph queries, which understand product-situation relationships, with vector queries, which fine-tune semantic relevance. This hybrid retrieval delivers highly accurate, grounded responses and avoids the hallucinations that come from relying on vectors or generic LLM prompts alone.
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Graph-grounded RAG generation
Once relevant sections are retrieved, Implicit Graph RAG generates responses where large language models summarize only from trusted, pre-vetted sources identified by the graph and vector query. This keeps answers relevant, explainable, and factually correct.
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Delivery through Implicit APIs
Every capability is exposed through Implicit APIs, enabling seamless integration into the company's customer-facing chatbot, internal tools, and knowledge portals, so trusted answers reach customers wherever they ask.
The results
The Implicit Knowledge virtual expert produced measurable improvements across support operations, customer experience, and content quality. As accuracy and trust improved, customers and internal users adopted the AI-driven virtual expert more readily, with usage rising as hallucination rates dropped and answers became reliably grounded.
Increase in issue resolution
Customers resolved more issues directly through the virtual expert, lifting first-contact resolutions and improving digital containment.
Increase in CSAT
Faster, more accurate answers, especially in moments of urgency, drove a 20% improvement in CSAT for self-service interactions.
Cost gap closed
Every successful virtual expert interaction meant one fewer live case, where human-assisted support can be up to 120x more expensive than self-service.
Improved content organization and hygiene
Automated tagging and graph-based structuring brought clarity and maintainability to support content, letting content teams fill gaps and resolve redundancies efficiently.
Proactive product quality insights
The knowledge graph surfaced recurring issues linked to specific products and situations, giving engineering and QA teams early warning signals and actionable insights.
Why Implicit was the right fit
Many vendors offer AI-powered support solutions, but Implicit was uniquely suited for this enterprise environment, delivering the precision, explainability, and product-context awareness that generic LLM toolkits could not match. By combining structured knowledge engineering through taxonomy and graphs with advanced GenAI methods like RAG and vector search, Implicit turned the company's chatbot into a trusted, scalable, and cost-effective support channel.