Global Tech Leader - Scaling Enterprise-Wide AI Accuracy

From Scattered Docs to Structured Intelligence: How a Global Tech Leader Scaled AI Accuracy

Industry

Consumer Electronics

Services

Enterprise AI Integration

Support Workflow Design

AI Support

Virtual Expert

Approach

Enterprise technology client implements an AI-driven knowledge layer designed to handle complex, technical queries - and increases resolution rate by 25%.

One of the world’s largest technology companies, serving millions of customers across hardware, software, and IT infrastructure, faced a critical challenge: modernizing and scaling its AI support experience in an era of rising complexity, content sprawl, and increasing customer expectations.

With thousands of products and configurations and a vast body of technical documentation, the company needed a solution that could connect customers to the right answers quickly. However, traditional search tools and out-of-the-box GenAI solutions frequently returned irrelevant or misleading answers, undermining trust and pushing customers toward costly live support.

To address this, the organization partnered with Implicit to implement a more intelligent and reliable AI-powered virtual expert grounded in structured, product-aware knowledge and engineered for precision.

Try Implicit Free

No items found.

THE CHALLENGE

Goals:

‍

The organization launched the initiative with four key objectives, all centered around improving the accuracy, usability, and impact of their self-service support experience:

Reduce time to find accurate answers in the support experience, allowing customers to resolve technical issues quickly and independently.
Improve the precision of AI responses in the customer interface by minimizing false positives, hallucinations, and vague results - critical for trust in high-complexity environments.
Automatically organize and tag support content to enable smarter retrieval, filtering, and routing inside the chatbot and knowledge base.

Identify documentation gaps and duplicates to ensure that the AI responses have access to high-quality, complete, and up-to-date knowledge.

While the company had invested heavily in documentation and support infrastructure, several deep-rooted issues limited the effectiveness of its self-service experience:

Complex, Technical Data: Documentation spanned a wide range of products and configurations with inconsistent structure, language, and formatting.
Disorganized Content: Poor metadata and tagging made it difficult to surface the right content at the right time.
LLM Hallucinations: Standard GenAI solutions returned overconfident, inaccurate answers—eroding trust and increasing escalation rates.

Low Self-Service Effectiveness: Without precision and relevance, customers reverted to contacting support directly, driving up costs.

THE SOLUTION

To solve these challenges, the company implemented Implicit Knowledge, a specialized AI-powered chatbot for product support that brings structure, accuracy, and context to self-service interactions.

‍

Key Components and Technical Differentiators of Implicit Knowledge

Product + Situation Taxonomy Preloading
Implicit begins by discovering and preloading a highly accurate and domain-specific taxonomy of both Products and Support Situations. This enables precise extraction of product-contextual knowledge from unstructured content, making it possible to identify what the document is about and which problems it addresses.
Implicit Knowledge Extractor
This module processes manuals, knowledge articles, and historical cases to extract and normalize key content. Mapping each section to product and situation entities converts raw content into a structured graph of support knowledge.
Implicit Knowledge Graph
Implicit creates a graph of relationships between Products, Situations, and relevant Sections of Documents. This allows the system to perform Graph Queries to pinpoint the most semantically relevant sections without relying solely on keyword matching.
Graph + 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 ensures that the self-service chatbot delivers highly accurate, grounded responses while avoiding hallucinations from relying solely on vectors or generic LLM prompts.
Implicit Graph RAG (Retrieval-Augmented Generation)
Once relevant sections are retrieved, responses are generated using Graph-grounded RAG, where large language models summarize only from trusted, pre-vetted sources identified by the graph + vector query. This ensures high relevance, explainability, and factual correctness.

Implicit APIs
All of these capabilities are exposed via APIs, enabling seamless integration into the company’s customer-facing chatbot, internal tools, and knowledge portals.

RESULTS

The Implicit Knowledge virtual expert produced measurable and transformative improvements across support operations, customer experience, and content quality. Estimated benefits include:

Higher AI Tool Adoption
As accuracy and trust improved, customers and internal users more readily adopted the AI-driven virtual expert. Usage increased significantly as hallucination rates dropped and answers became reliably grounded.
25% Increase in Issue Resolution via Virtual Expert
Customers were able to resolve more issues directly through the virtual expert, leading to a 25% increase in first-contact resolutions, reducing frustration and improving digital containment.
20% Increase in Customer Satisfaction (CSAT)
The virtual expert delivered faster, more accurate answers - especially in moments of urgency - driving a 20% improvement in CSAT for self-service interactions.
Lower Support Costs
Every successful virtual expert interaction meant one fewer live agent case. The cost savings quickly became significant because human-assisted support can be up to 120x times more expensive than self-service.
Improved Content Organization & Hygiene
Automated tagging and graph-based structuring brought clarity and maintainability to the company’s support content, enabling content teams to fill gaps and resolve redundancies efficiently.
Proactive Product Quality Insights
The Knowledge Graph surfaced recurring issues linked to specific products and situations, providing engineering and QA teams with early warning signals and actionable insights.

Why Implicit Was the Right Fit

While many vendors offer AI-powered support solutions, Implicit was uniquely suited for this enterprise environment - delivering the precision, explainability, and product-context awareness that generic LLM toolkits couldn’t match. By combining structured knowledge engineering (taxonomy + graphs) with advanced GenAI methods (RAG + vector search), Implicit enabled the company to turn its chatbot into a trusted, scalable, and cost-effective support channel.

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

Help us Build the Future of Product Support AI

Get a Demo