The rapid evolution of embedding technologies has fundamentally transformed how developers build AI applications. What was once the exclusive domain of tech giants has become accessible to developers everywhere, leading to an explosion in embedding-based applications. However, as we've witnessed with the rise and fall of the vector database category, not every technological advancement requires a completely new infrastructure class. In fact, when it comes to leveraging customer data in large language models (LLMs), identity resolution technologies like Tilores offer a compelling alternative that addresses fundamental limitations of the vector database approach.
The Embedding Revolution and Its Limitations
For years, companies like Google, Meta, and Amazon have used embedding techniques to power recommendation systems and search features. These deep learning methods transform content - text, images, video, audio, code - into vector representations that capture patterns and relationships. With powerful pre-trained models and intuitive APIs, these techniques have become practical tools for everyday developers.
The explosion of embedding applications created a clear need: efficiently storing, indexing, and searching high-dimensional vectors at scale. This gap sparked the vector database gold rush, particularly after ChatGPT's launch triggered widespread adoption of Retrieval-Augmented Generation (RAG). Companies rushed to build specialized infrastructure for vector operations, fueled by massive investment.
However, as Jo Kristian Bergum, former Chief Scientist at Vespa.ai, in his article "The rise and fall of the vector database infrastructure category" observed, we've since witnessed a market correction. Vector search providers have rapidly added traditional search features, while established search engines have incorporated vector capabilities. The market is recognizing a fundamental truth: vector search isn't a separate category but simply another capability in the modern search toolkit.
The Customer Data Challenge: Where Vector Databases Fall Short
When working with customer data in particular, vector databases reveal significant limitations that traditional search augmentation can't fully address. Customer information exists across multiple systems, often with varying identifiers, incomplete records, and inconsistent formats. The fundamental challenge isn't just finding similar vectors - it's knowing which vectors represent the same customer entity across disparate datasets.
This is where identity resolution technologies like Tilores enter the picture, offering a specialized approach for customer-centric AI applications.
Identity Resolution: The Missing Piece in Customer-Centric RAG
Identity resolution is the process of connecting disparate data points to form a unified view of customers across interactions, channels, and systems. Tilores technology approaches this challenge by:
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Creating identity graphs rather than just vector embeddings: While vector databases focus on content similarity, identity resolution builds comprehensive relationship networks that understand when different data records represent the same underlying entity.
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Handling probabilistic matching: Unlike vector similarity which works well for semantic content, customer matching often requires probabilistic approaches that can handle fuzzy matching across names, addresses, phone numbers, and other personal identifiers.
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Maintaining context across data sources: Identity resolution preserves the crucial relationships between customers and their interactions, purchases, support tickets, and other touchpoints - connections that simple vector similarity might miss.
IdentityRAG: Enhancing LLMs with Customer-Aware Context
The emergence of IdentityRAG represents a specialized approach to Retrieval-Augmented Generation that leverages identity resolution rather than relying solely on vector similarity. This approach offers several key advantages:
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Customer-centric rather than content-centric retrieval: Traditional RAG focuses on finding semantically similar content. IdentityRAG prioritizes retrieving information about the specific customer entity relevant to the current context, even when that information doesn't match the semantic query.
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Cross-system data integration: Rather than creating separate vector databases for each data source, IdentityRAG uses identity resolution to create a unified customer view that spans CRM systems, support databases, transaction records, and marketing platforms.
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Temporal awareness: Identity resolution naturally preserves the timeline of customer interactions, allowing LLMs to understand a customer's journey and history chronologically rather than just matching similar content.
Why Identity Resolution Outperforms Vector Databases for Customer Data
Just as Bergum's article noted that we "overcomplicated things" with vector databases, many organizations are discovering they don't need specialized vector infrastructure to enhance LLMs with customer data. Identity resolution offers several advantages:
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Accuracy: Identity resolution technologies like Tilores are specifically designed to match customer records with high precision, addressing data quality issues that vector similarity often struggles with.
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Compliance: By maintaining clear lineage of customer data and supporting proper data governance, identity resolution helps organizations stay compliant with privacy regulations when using customer data in AI applications.
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Integration with existing systems: Rather than creating a separate infrastructure stack, identity resolution technologies typically integrate with existing customer data platforms and CRM systems.
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Contextual relevance: By understanding customer identity across touchpoints, IdentityRAG can retrieve information based on the customer's relationship to the organization, not just content similarity.
The Convergence of Search, Identity, and AI
The vector database correction mirrors a broader trend: specialized infrastructure is giving way to integrated capabilities within existing systems. Just as PostgreSQL, MongoDB, and Redis added vector support, customer data platforms are incorporating both identity resolution and AI capabilities.
This convergence makes sense. Building effective customer-centric AI applications requires multiple capabilities working in concert:
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Traditional search features for text matching
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Vector search for semantic similarity
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Identity resolution for entity recognition
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Privacy controls for regulatory compliance
Conclusion
The rise and fall of vector databases teaches us an important lesson: new capabilities don't always require new infrastructure categories. For customer-focused AI applications, identity resolution technologies like Tilores offer a more targeted approach than general-purpose vector databases.
As organizations move beyond the initial RAG hype cycle, they're discovering that the quality of retrieved information matters more than the infrastructure used to store it. Identity resolution addresses the fundamental challenge of customer data - knowing when different records represent the same person - in ways that vector similarity alone cannot.
The future of customer-centric AI won't be built on vector databases alone, but on integrated systems that combine the best of identity resolution, traditional search, and vector capabilities. As with many technology trends, what started as a gold rush for specialized infrastructure is evolving into a more nuanced understanding of how different technologies can work together to solve real business problems.
