Tungsten Automation, a global leader in intelligent automation software, empowers businesses to streamline operations through AI-driven document processing and workflow automation. But even tech-forward organizations face internal knowledge access challenges. Tungsten Automation users were struggling to find accurate answers in time for their certification exams. A sprawling library of PDFs, manuals, and HTML documents created confusion, not clarity. What they needed wasn’t more information, but smarter access to it. That’s where the GraphRAG chatbot changed everything.

The problem: why Tungsten needed a GraphRAG chatbot to fix fragmented documentation and poor search

Tungsten, a leader in intelligent document processing, had comprehensive certification resources, but they were spread across fragmented PDFs, outdated portals, and siloed help center pages. Users preparing for exams faced long delays, incomplete answers, and a frustrating experience.

Traditional keyword search often returned irrelevant content or missed context entirely. Even basic RAG-based systems failed, retrieving chunks of content without understanding the relationships between concepts, settings, or workflows. The inability to answer even moderately complex queries meant users lost confidence and time. Tungsten needed an AI-powered assistant that could unify all documentation, understand multi-step queries, and deliver precise, contextual answers at scale. They needed the GraphRAG chatbot.

The solution: building a GraphRAG chatbot with graph-powered retrieval and generative reasoning

GoML worked with Tungsten Automation to build a GraphRAG chatbot that revolutionized how users accessed certification-related knowledge. By combining a retrieval-augmented LLM architecture with a knowledge graph layer, the chatbot delivered context-aware, semantically rich responses.

Graph-based knowledge ingestion

• All documentation (PDFs, web help articles, guides) was ingested into a pipeline.

• A semantic parser and entity linker extracted key concepts, topics, and interconnections.

• These were used to create a structured knowledge graph layered on top of the source documents.

Enhanced retrieval using GraphRAG

• Instead of retrieving isolated chunks, the chatbot used graph traversal to fetch related content, enriching the LLM context window with semantically connected nodes.

• This ensured that multi-step answers (e.g., setup, troubleshooting, best practices) were comprehensive and coherent.

Contextual answer generation

• GPT-4o powered the response layer.

• Prompt engineering ensured that responses mimicked certified trainer language, prioritizing accuracy and structure.

The impact: boosting accuracy and user confidence with GraphRAG chatbot

With the GraphRAG chatbot, Tungsten Automation witnessed measurable improvements across user engagement and support efficiency:

• 85% improvement in answer accuracy, especially for multi-part queries

• 70% reduction in time spent searching documentation

• 60% boost in certification exam preparedness scores, based on feedback surveys

Lessons for other organizations

Common pitfalls to avoid

• Relying solely on vector-based retrieval without semantic linking

• Treating PDFs and manuals as unstructured blobs instead of structured knowledge sources

• Ignoring feedback loops in AI-powered bots

Advice for teams facing similar challenges

• Use GraphRAG chatbot to merge structured and unstructured knowledge sources

• Pair LLMs with entity-based reasoning for complex queries

• Choose models like GPT 4o for high-context, enterprise-grade response quality

Want to build a domain-specific GraphRAG chatbot that works?

Let GoML help you deploy AI-powered assistants that understand your data as well as your best expert.