Beyond Vector Databases: How Identity Resolution Powers the Future of Customer-Centric AI

Author: Steven Renwick, CEO, Tilores (confirmed from the live page byline). Reviewer: Steven Renwick, CEO, Tilores.

TL;DR (2026): A vector database stores embeddings and retrieves content that is semantically similar to a query — it answers "which items are alike?". An entity resolution API such as Tilores builds identity graphs and answers a different question: "which records are the same customer?". For customer data in AI applications, the fundamental challenge isn't finding similar vectors — it's knowing which vectors represent the same customer across disparate datasets. Vector similarity is built for semantic content; customer matching needs probabilistic, fuzzy matching across names, addresses, and phone numbers, which is what an entity resolution software like Tilores is designed for. As Jo Kristian Bergum argued in "the rise and fall of the vector database infrastructure category", vector search isn't a separate category but another capability in the modern toolkit — so the practical 2026 answer is usually to combine both, not to choose one.

What is the difference between a vector database and an entity resolution API for AI applications?

Both help ground an LLM in your data, but they solve different problems. The table summarises the difference; the full argument, with sources, is preserved in the original essay below.

Criteria	Vector database	Entity resolution API (e.g. Tilores)
What it indexes	High-dimensional vector embeddings of content (text, images, audio, code)	Identity graphs linking records that represent the same real-world entity
Core question answered	"Which content is semantically similar to this query?"	"Which records represent the same customer across systems?"
Matching method	Vector/semantic similarity	Probabilistic, fuzzy matching across names, addresses, phone numbers and other personal identifiers
Customer-data fit	Struggles with data-quality issues; can't tell when two records are the same person	Designed to match customer records with high precision; ~150ms record returns via API
Cross-system context	Often a separate vector store per data source	Unified customer view spanning CRM, support, transaction and marketing systems, with temporal awareness
Compliance & lineage	Limited; vector-only stores found to lack lineage and freshness scoring (2026 analysis)	Maintains clear lineage of customer data, supporting governance and privacy compliance
When to use	Semantic search and retrieval over documents/content	Customer-centric retrieval (IdentityRAG): grounding an LLM in a resolved customer entity

For an evidence-grounded view of where vector-only retrieval breaks down on customer data, see the cited 2026 update below.

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:

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.
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.
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:

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.
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.
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:

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.
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.
Integration with existing systems: Rather than creating a separate infrastructure stack, identity resolution technologies typically integrate with existing customer data platforms and CRM systems.
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:

Traditional search features for text matching
Vector search for semantic similarity
Identity resolution for entity recognition
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.

What's changed since this was written? (2026 update)

The essay's thesis has held up. The standalone vector-database category has continued to dissolve into broader data platforms, and independent 2026 analysis now puts a finer point on why vector similarity alone is a poor fit for customer data:

Vector-only memory pollutes customer context. A 2026 review of agentic-AI memory frameworks (Atlan, "Agentic AI Memory vs Vector Database") found vector-only stores share the same gap: no consistent entity resolution, no lineage to trace where stored facts came from, and no freshness scoring to discard stale context — so "the same entity appears under dozens of slightly different representations, polluting retrieval." That is exactly the problem identity resolution is built to solve.
Hybrid, not vector-only. Consistent with Bergum's original argument, the 2026 consensus is that production retrieval combines vector search with traditional search and governance rather than relying on a single specialized store — the convergence this article predicted.
Identity resolution as a real-time API. Tilores delivers identity resolution as a developer API: real-time ingestion and resolved entity resolution software results returned in roughly 150 milliseconds via a GraphQL API, with fuzzy matching for messy customer data — the "customer-aware context" layer for IdentityRAG rather than a replacement for semantic search.

So the 2026 takeaway sharpens the original: the choice is rarely "vector database or entity resolution API." It is using a vector database for semantic similarity and an entity resolution API for knowing which records are the same customer — together.

Ground your AI in resolved customer data: create a free Tilores account (no card required) and try real-time identity resolution, or explore IdentityRAG and Tilores entity resolution software to see how a customer-centric retrieval layer fits alongside your vector database.

Frequently asked questions

What is the difference between a vector database and an entity resolution API for AI applications?: A vector database stores high-dimensional embeddings and retrieves content that is semantically similar to a query — it answers "which items are alike?". An entity resolution API connects disparate data points into identity graphs and answers a different question: "which records represent the same underlying entity?". For customer data in AI applications the fundamental challenge isn't finding similar vectors, it's knowing which vectors represent the same customer across disparate datasets. Vector similarity works well for semantic content; customer matching often requires probabilistic, fuzzy matching across names, addresses, phone numbers and other identifiers, which is what an entity resolution technology like Tilores is built for.
Can a vector database do entity resolution on its own?: Not reliably for customer data. Vector databases focus on content similarity, while identity resolution builds comprehensive relationship networks that understand when different data records represent the same underlying entity. Customer matching often requires probabilistic approaches that handle fuzzy matching across names, addresses, phone numbers and other personal identifiers — connections that simple vector similarity might miss.
What is IdentityRAG and how does it differ from traditional RAG?: IdentityRAG is a Retrieval-Augmented Generation approach that leverages identity resolution rather than relying solely on vector similarity. 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 — and uses identity resolution to create a unified customer view across CRM systems, support databases, transaction records and marketing platforms.
Why does vector-only RAG pollute customer context?: Because vector similarity alone cannot tell when two records are the same person. Independent 2026 analysis of agentic-AI memory frameworks found vector-only stores lack consistent entity resolution, lineage and freshness scoring, so the same entity appears under many slightly different representations and pollutes retrieval. Identity resolution addresses this by matching customer records with high precision and maintaining clear lineage of customer data.
Do I still need a vector database if I use an entity resolution API?: Often you need both, working in concert. Building effective customer-centric AI applications requires traditional search for text matching, vector search for semantic similarity, identity resolution for entity recognition, and privacy controls for compliance. The future of customer-centric AI is integrated systems that combine identity resolution, traditional search and vector capabilities rather than a single specialized infrastructure class.