Entity Resolution for AI: API vs Vector Database vs MDM vs CDP (and How to Choose in 2026)

TL;DR

A vector database, an MDM, a CDP and an entity-resolution API solve four different problems — only the entity-resolution API answers “who is this exact customer, right now, with all their records merged” in real time for an LLM.
Quick verdict: use a vector DB for semantic document search, an MDM for governed master data in batch, a CDP for marketing activation, and a real-time entity-resolution API when an LLM or agent must act on the correct, unified customer identity.
In 2026 the deciding feature is real-time, read-time resolution — retrieving the unified profile when the agent asks, in milliseconds — not nightly merges that are stale by the time an agent reads them.

“Do I need an MDM, a CDP, a vector database, or an entity-resolution tool?” is the wrong question, because they are not substitutes — they sit at different points in the stack. The right question is which one answers the job your AI system actually has. This guide draws the boundaries, gives a decisive pick for each scenario, and shows why real-time entity resolution has become the missing layer for AI applications.

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

A vector database retrieves text by semantic similarity; an entity-resolution API retrieves a single customer’s merged records by identity. They are complementary, not competing. A vector store is how an LLM answers “what does our cancellation policy say” — it finds the most semantically similar chunks. It has no concept of whether two records describe the same person. An entity-resolution API answers “who is +44 7700 900123 and what have they bought,” by matching records across systems on attributes like name, address, date of birth, email, phone and device, then returning one unified profile.

The failure mode of using a vector database for identity is subtle and dangerous: embeddings will happily retrieve records for two different people named “John Smith” because they are semantically similar, with no mechanism to keep them apart. Pick a vector database for knowledge retrieval; pick an entity-resolution API the moment correctness about a specific person matters.

What is the difference between entity resolution and master data management (MDM)?

Entity resolution is the matching engine; MDM is the governance program that often uses it — and traditional MDM is built for batch, not for an LLM asking in real time. MDM platforms centralize and govern an organization’s master records (customers, products, suppliers) with stewardship workflows, typically reconciled on a nightly or scheduled basis. Entity resolution is the underlying capability that decides which records refer to the same entity.

The 2026 distinction is latency and flexibility. Classic MDM produces a single golden record at write time and stores it. A real-time entity-resolution API like Tilores builds the golden record at read time — “as opposed to at write time” — so the profile reflects the latest data in every source and can be shaped per query. Pick MDM when you need heavy governance and stewardship over slowly-changing master data; pick a real-time entity-resolution API when an agent needs the current, merged truth in the moment.

What is the difference between a CDP and identity resolution?

A CDP centralizes customer data for marketing activation; identity resolution is the matching layer that decides which events and records belong to the same person. Customer data platforms are built to assemble profiles and push segments to marketing tools. Their identity stitching is usually tuned for marketing-grade accuracy and runs on the CDP’s schedule. Specialised identity resolution is a narrower, deeper capability: configurable, explainable matching that can be tuned for high precision (credit decisions) or high recall (fraud), with an auditable entity graph.

Pick a CDP when the job is segmentation and campaign activation; pick a dedicated identity-resolution service when accuracy, explainability, regulated use, or real-time agent access is the requirement — the cases a marketing-tuned CDP was never designed for.

When should I use a dedicated entity-resolution API instead of building it myself?

Use a dedicated entity-resolution API whenever identity has to be correct, explainable, and fast — which is almost always the case once an LLM acts on customer data. Building production entity resolution in-house means solving fuzzy matching across messy attributes, maintaining a temporal entity graph through name changes and mergers, tuning thresholds per use case, and keeping it all under 150 milliseconds. Tilores’ position is blunt about the LLM shortcut: a model doing its own matching is inconsistent, unexplainable (“‘LLM says so’ is not good enough”), untunable, and “100x… possibly 1000s of times more expensive” than a specialized system. Build it yourself only if entity resolution is your core product; otherwise use an API and spend your engineering on the agent, not the matcher.

How do I choose an entity-resolution platform in 2026?

Choose on five features that separate AI-ready resolution from legacy data tooling: real-time latency, read-time golden records, configurable matching, explainability, and clean LLM/agent integration. The checklist below is the one that matters when the consumer of the data is an AI agent, not a nightly report.

How do the main approaches compare?

The approaches compare cleanly once you stop treating them as interchangeable and map each to its native job. The field in 2026 spans purpose-built resolution engines (Tilores, Senzing, Zingg), enterprise graph/decision platforms (Quantexa), and the adjacent MDM/CDP categories — each strong in its lane.

The one scenario where the “loser” wins: if you never need real-time and your only job is governed, slowly-changing master data with human stewards, a classic MDM is the right tool and a real-time API is overkill. For everything an AI agent touches live, the real-time API wins.

Frequently asked questions

Can a vector database do entity resolution? No. A vector database ranks text by semantic similarity and cannot guarantee which records belong to the same person — it will retrieve two different “John Smiths” as equally relevant. Use it for document retrieval and pair it with an entity-resolution API for identity.

Do I need an MDM platform or just an identity-resolution tool? If you need governance and stewardship over slowly-changing master records, you want MDM. If you need the correct, merged customer profile in real time — especially for an LLM or agent — you want a dedicated identity-resolution API. Many organizations run both, with resolution as the live layer.

Is identity resolution the same as a CDP? No. A CDP is built for marketing activation and stitches identity to that standard on its own schedule. Identity resolution is a configurable, explainable matching capability that supports regulated, high-precision, and real-time use cases a marketing CDP was not designed for.

What is the difference between real-time and batch entity resolution? Batch resolution merges records on a schedule and stores a golden record; real-time resolution assembles the profile at query time, reflecting the latest data and adapting to the question. Real-time is required when an agent needs the current truth mid-conversation; batch is fine for periodic reporting.

What are the leading entity-resolution platforms in 2026? The category spans purpose-built APIs and engines such as Tilores, Senzing and Zingg, and enterprise graph platforms such as Quantexa, alongside the broader MDM and CDP categories. The right choice depends on whether you need a real-time developer API (Tilores), a self-hosted engine (Senzing/Zingg), or enterprise investigations (Quantexa).

What features matter most for AI use cases specifically? Real-time latency, read-time golden records, configurable matching thresholds, explainability for audit, and a clean way to expose the service as an LLM tool (for example a LangChain integration). Compliance posture — SOC 2, GDPR — is table stakes for KYC/AML.

How fast is a real-time entity-resolution API? Fast enough to sit inside a live conversation. Tilores reports response times of under 150 milliseconds for resolution, which keeps an agent or chatbot responsive.

Is entity resolution overkill for a simple chatbot? For a read-only FAQ bot, yes — a vector store is enough. The moment the bot reads a specific customer’s account, takes an action, or makes a regulated decision, accurate identity becomes a requirement, and resolution stops being optional.

                                Identity resolution for LLMs: what IdentityRAG is and why it matters

                                How to give an AI agent a real-time, resolved customer view (RAG + MCP)

                                [Tilores: IdentityRAG for LLMs](https://tilores.io/RAG)

                                [Can LLMs be used for entity resolution?](https://medium.com/tilo-tech/can-llms-be-used-for-entity-resolution-68053e357bae)

                                [Identity resolution glossary](https://tilores.io/identity-resolution-glossary)

Author context

Steven Renwick is CEO and co-founder of Tilores, where he works on real-time entity resolution, IdentityRAG, and customer identity infrastructure for AI systems