Signify and Microsoft Research Asia Enhance Customer Service with PIKE-RAG Technology

Signify and Microsoft Research Asia Enhance Customer Service with PIKE-RAG Technology

Signify, a global leader in connected LED lighting, partnered with Microsoft Research Asia to implement PIKE-RAG (a Retrieval-Augmented Generation system) in its knowledge management platform. This integration addressed critical challenges in handling technical documentation and complex customer queries, resulting in a 12% improvement in answer accuracy. The solution leverages advanced AI to process multimodal data, perform multi-step reasoning, and adapt to domain-specific engineering logic, setting a benchmark for industrial knowledge systems.

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Challenges of Applying RAG in Lighting

Traditional RAG systems faced significant limitations in Signify’s context due to the complexity of its product data and customer inquiries. Key challenges included:

• Multimodal Data Complexity: Product documentation contained nonstandard tables, circuit diagrams, and unstructured technical parameters, which traditional systems struggled to parse effectively.
• Scalability and Customization: Continuous updates to product specifications and the need for domain-specific reasoning slowed development and limited system scalability.
• Reliance on Erroneous Data Sources: Discrepancies between synchronized databases and external sources led to inconsistent or inaccurate answers.

Despite efforts like keyword optimization and prompt refinement, Signify required a more robust solution to meet the demands of professional users requiring precise technical information.

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How PIKE-RAG Addressed Signify’s Pain Points

PIKE-RAG overcame these challenges through three core innovations:

1. Multimodal Document Parsing and Industry-Specific Reasoning

• Integration of Document Intelligence: Combines Microsoft Azure OpenAI models with Microsoft Research Asia’s Document Intelligence to extract structured data from tables and diagrams.
• Example Use Case: When querying “What is the output voltage of a specific driver model at 0.15A current?” PIKE-RAG locates the relevant voltage-current curve in the document and infers a range (e.g., 40–54V), a task traditional systems fail due to inability to interpret visual data.

2. End-to-End Knowledge Loop with Multi-Source Validation

• Direct Document Parsing: Uses original product manuals and PDFs as primary data sources, avoiding reliance on potentially outdated or inconsistent databases.
• Citation Relationships: Establishes links between data sources to ensure answers are grounded in verified information, improving trustworthiness.

3. Dynamic Task Decomposition and Multi-Hop Reasoning

• Complex Query Handling: Breaks down multi-step questions into subtasks and uses multi-hop reasoning to derive answers from indirect information.
• Example Use Case: For the query “List all bases compatible with the G8 series lamps,” PIKE-RAG:
  1. Identifies that G7 and G8 series share dimensions.
  2. Retrieves G7-compatible bases.
  3. Maps abbreviations to full names using a reference table, generating a complete list.

This automated reasoning process ensures accuracy even when direct answers are absent.

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Beyond Lighting: Generalization Across Industries

PIKE-RAG’s success in Signify’s use case highlights its adaptability to other complex domains. Key generalization capabilities include:

1. Self-Evolution and Continuous Learning

• Automated Optimization: Analyzes interaction logs to refine knowledge extraction strategies (e.g., adjusting table parsing methods or multimodal content weights).
• No Manual Intervention: Validated strategies are automatically integrated, enabling the system to adapt to new knowledge types dynamically.

2. Modular Architecture for Flexibility

• Component-Based Design: Combines modules for document parsing, storage, retrieval, and reasoning, allowing dynamic configuration based on use cases (e.g., fact retrieval vs. multi-hop reasoning).
• Scalability: Efficiently handles diverse tasks, from simple lookups to complex engineering queries.

3. Domain-Specific Reasoning Adaptation

• Real-Time Logic Updates: Enterprises can inject domain-specific rules (e.g., “LED driver max voltage is the operating range’s maximum”) via the Domain Tips feature.
• Industry Compliance: Ensures outputs align with professional standards and conventions.

PIKE-RAG has been piloted in sectors like manufacturing, mining, and pharmaceuticals, consistently improving Q&A accuracy and reducing customization efforts.

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Recommendations for Implementation

• Use Case Fit: Ideal for industries with complex technical documentation (e.g., engineering, healthcare, law) requiring precise, multi-step reasoning.
• Best Practices:
  • Integrate domain-specific rules to align with industry conventions.
  • Prioritize continuous learning to refine knowledge extraction strategies.
  • Leverage modular architecture to adapt to evolving use cases.
• Pitfalls to Avoid:
  • Over-reliance on unstructured data without validation mechanisms.
  • Neglecting to update domain-specific logic as standards evolve.

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Working Example (Conceptual)

While no code is provided in the context, a simplified Python-like pseudocode illustrates PIKE-RAG’s multi-hop reasoning:

def answer_complex_query(query, documents):
    subtasks = decompose_query(query)  # e.g., "G8 bases" → ["G7 bases", "G7-G8 compatibility"]
    results = []
    for task in subtasks:
        doc = find_relevant_document(task, documents)
        if doc:
            info = extract_info(doc, task)
            results.append(info)
    final_answer = synthesize_results(results)  # Combine G7 bases and compatibility rules
    return final_answer

This approach demonstrates how PIKE-RAG dynamically breaks down queries and synthesizes information from multiple sources.

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Reference

https://www.microsoft.com/en-us/research/blog/when-industry-knowledge-meets-pike-rag-the-innovation-behind-signifys-customer-service-boost/