Your Vector Search is (Probably) Broken: Here's Why

Vectors are the language of AI, and also the foundation of context engineering. Every enterprise working with AI systems and agents is figuring out how to store and retrieve them. Some are spinning up dedicated vector databases, others are using vector types within their current operational database or other data infrastructure. What many of these projects have in common, though, is they’re unlikely to ever leave the pilot phase due to a shaky foundation.

As they work to move AI apps and agents into production, teams are discovering that their ability to feed LLMs and agents with fresh data so that they can make better decisions – ie, context engineering – is directly tied to the pipelines that keep those vectors up to date.

It’s classic garbage in/garbage out: improperly managed vector attributes don’t provide the fresh, semantically rich data that context engineering requires. The result is irrelevant search results and failed agent responses…and yet another AI initiative that loses trust.

The problem isn’t about streaming data from your operational database to AI models. Moving the data around isn’t the hard part. The struggle is transforming that data into fresh business context and making sure your AI system’s vector pipelines are providing the fresh, accurate information that your model needs to support hybrid search and reranking. So the question becomes, how do you solve the operational database → vector database pipeline problem?

Working with vector databases is conceptually simple: take unstructured data, embed it, and write to your database along with the attributes you assign to it for filtering and reranking based on business logic. AI systems need this vector data to be real-time and correct in two ways: the attributes assigned to the vector, and the vector itself. But building real-time data pipelines that can keep vector embeddings and attributes fresh for accurate, up-to-the-minute AI results is extremely difficult.

AI models, from simple linear regression algorithms to the intricate neural networks used in deep learning, operate through mathematical logic. Any data that an LLM operates on must be expressed numerically, but unstructured data like text, images, and audio are inherently non-numerical.

• Vector embedding is a way to convert unstructured data into a data object – an array of numbers that translates the data’s original meaning – so it can be used as input for an AI agent or model to perform useful real-world tasks.
• Vector attributes are information about the embedding (data object) — structured metadata that gets fed to the agent/model as input, describing specific, measurable properties of that data object.
• Vectors themselves are created by pipelines, which translate unstructured data into vector embeddings with attributes. These vectors are then stored in vector databases or in regular databases with extensions like pgvector for Postgres.

Vector embeddings (generally just called “vectors”) represent an actual, numerical, LLM-readable data object. Attributes are human-defined rules and domain knowledge that describe that data object. Both embeddings and their attributes are subject to change as upstream data changes.

This is an important distinction when it comes to working with vector data in enterprise AI and applications because vector embeddings capture semantic meaning, context, and relationships between data points – but business logic lives in vector attributes.

LLMs work through semantic search: identifying relevant data through its meaning, rather than just matching keywords. For example, if you're using helpdesk software and you search for "billing problems", a semantic search would return tickets that mention "payment declined" or "card rejected" even though they don't contain the word "billing."

• When you give an AI app or agent a prompt, semantic search uses vectors to discover data that directly pertains to your request. The LLM compares vectors to measure how similar two pieces of data are in meaning and then find the most relevant matches.

Hybrid search works by first doing semantic search for similarity within a set of data and then applying filters to the semantic search results to extract the desired data points.

• This semantic information comes from vector attributes. This is why attributes are critical for sorting and reranking AI results based on whatever criteria that is important to you, such as permissions, relevance, or business rules.

In order to deliver the most accurate and up-to-date results, AI agents and applications need the most accurate and up-to-date vector embeddings and attributes.

• Attributes change all the time, because they’re the data about the vector data object (the embedding).
• But embeddings themselves can (and often do) change — for example, when they are the result of upstream joins or data transformations.

The problem most teams face with vector attributes (metadata) and vector embeddings (the numerical object representing a chunk of unstructured data) is knowing which one needs updating when upstream data changes.

Modern vector pipelines typically add other metadata into the embedding itself, separately from filterable attributes: for example, file names and other metadata that may be the result of a join. When source data changes, they don't have a way to know exactly which vectors are affected and what part of those vectors needs updating (just the attributes? the entire embedding?). So they take the safe but expensive route: re-embed everything in batches to ensure freshness.

• Even if you're embedding static text like a product description, many vector pipelines include contextual metadata not just as separate attributes but also inside the embedding itself. For example:
• A product description embedding might include the product's category, brand, or availability status
• A document embedding might include the file name, author, department, or access permissions
• A support ticket embedding might include customer tier or account status

If any of that metadata changes (product goes out of stock, document gets moved to a different department, customer upgrades to premium tier), the embedding itself becomes stale — not just its filterable attributes.

Vector embeddings and attributes aren't simple key-value pairs that you can just copy over from your operational database. In practice, vectors often require complex denormalization across multiple operational systems. Your AI application might need to compute priority scores, aggregate metrics across customer touchpoints, or check for SLA breaches — all of which demand pulling data from various sources and applying business logic before you can even assign the attribute to a vector.

This is where context engineering comes in: A single write to your vector database can require scanning millions of records to calculate an attribute correctly.

• For example, when a high-value customer submits a ticket, the AI agent’s context for calculating the "priority" attribute assigned to that ticket's vector embedding includes their contract tier, their lifetime value, their recent satisfaction scores, their account status, and whether they have any open escalations. Calculating that priority score means querying and aggregating across all of them.

This computational complexity makes achieving data freshness and accuracy difficult to achieve. Every minute of lag between when something changes in your operational systems and when that change propagates to your vector attributes means your AI agents are working with stale data. Users can end up missing critical information they should see or, worse, see data that’s wrong.

• In financial services, account status changes when fraud is detected, risk scores get updated as market conditions shift, and compliance requirements change based on regulatory updates. If your vector attributes lag behind these changes, your AI agents might surface sensitive financial information from compromised accounts, or fail to escalate urgent fraud alerts because the risk score attribute is still reflecting yesterday's calculation.
• Healthcare system patient records change as new diagnoses are added, authorization levels shift when insurance approvals come through, and treatment urgency levels escalate. An AI agent searching through patient data with outdated attributes could miss a critical update about a patient's deteriorating condition, or incorrectly delay or deny medical treatment that has actually been approved.

What becomes possible when you actually solve this problem? Here are some opportunities that emerge:

• Competitive advantages through speed: When vector embeddings and attributes accurately reflect live data changes, AI agents become a significant business accelerator (instead of an expensive novelty). 

Customer service teams can resolve issues on the first interaction because agents see complete, current context. Sales teams can act on buying signals as they emerge rather than uncovered in post-mortems. Financial advisors do analysis informed by market changes that happened minutes ago, not yesterday.

This speed advantage compounds. While competitors are still validating whether their AI outputs match reality, teams with accurate vector data are already acting on insights.

New product capabilities that weren't feasible before:

When AI agents are working with live data, they can be applied to automate high-stakes decisions like loan approvals and medical triage. Organizations can expand AI use cases into sensitive areas like legal, medical, and financial decision-making that require accuracy guarantees, transforming AI from a side tool into a "must have" operational system embedded in how work actually gets done.

Tools that actually get used:

Internal stakeholders actually use AI tools when they trust the results. AI initiatives move from pilot to production because they deliver consistent, reliable outcomes. Personalization that reflects what customers did today, not what they did last week. Compliance automation that adapts to regulatory changes as they happen, instead of operating on outdated rules that create exposure.

Real world example: AI-powered product guide

Your customer service staff spends the majority of their time solving the same customer problems over and over. This is the perfect opportunity for an AI agent that can interact with your users to answer questions and guide them in using your product(s).

• You have a product guide
• You break it up into chunks and embed those into vectors
• To get the best results, you include vector attributes: metadata including, for example, the product name, id, and possible accessory items.
• This metadata may be the result of a join or some complex calculation across different vectors

Sounds logical enough so far. Actually, though, this is the point where things can start to go wrong.

• As your business changes, it is extremely difficult (and time-consuming) to figure out which product-related vectors to update and when
• So you update everything in batches, and as a result you have both stale data and wasted inference spend

This process is exactly how too many current enterprise AI initiatives turn into expensive disappointments. First let’s explore why the AI architectures in most common use today fall victim to this problem, and then we will demonstrate how Materialize lets you surgically update the exact vectors – and their attributes – as quickly the world changes around you.

Even as AI is emerging and evolving before our very eyes, it's becoming clear that traditional application and data architectures do not translate. People are trying to build AI systems using two vector pipeline antipatterns that force a choice between speed and accuracy:

• Native filtering (attributes stored IN the vector database): Attributes (priority score, permissions, account status, etc.) are precalculated and stored alongside the vector embeddings in your vector database. When your AI agent searches, it can filter instantly because everything is in one place. But: Those attributes came from your operational databases (CRM, billing system, etc.) and when something changes there, your vector database doesn't automatically know about it. You're stuck choosing between stale data or expensive recalculation on every database write (which gets expensive fast when you have millions of vectors).
• Pre/post filtering (attributes stored externally, joined at query time): Vector embeddings are stored in your vector database, but vector attributes live in your operational databases When your AI agent needs to search, it either:
  • Pre-filters: Checks your operational database first ("show me all tickets from premium customers"), gets those IDs, THEN searches vectors – expensive because you're querying your operational DB every time
  • Post-filters: Searches vectors first, gets results, THEN checks your operational database to filter them ("which of these results are the user allowed to see?") – also expensive, and may retrieve many more vectors than are actually needed (which you pay for).

Neither of these pipeline architectures gives you both speed AND accuracy. You're always trading off between "fast queries but stale attributes" and "accurate but slow." But there’s another, usually unrecognized cost: calculating attributes.

To perform attribute calculations, both native and pre/post filtering approaches require writing attributes by joining data from two different systems at query time. While embedding costs are publicized and understood, the cost of calculating correct and relevant attributes from your operational data is hidden — and often larger.

Embedding costs are visible and predictable because you pay per API call to your LLM. Attribute costs, though, are hidden in your infrastructure. The database queries scanning millions of rows, the compute spinning up to join across multiple systems, the engineering hours maintaining fragile pipelines, the stale data: all of these contribute to degraded user experience, failed proof-of-concepts, abandoned agent projects, and unrecognized costs that typically dwarf the per-vector embedding expense.

To keep vector attributes fresh, engineering teams typically cobble together what amounts to a Frankenstein architecture: CDC streams pulling changes from operational databases, read replicas to offload query load, cache layers to speed up attribute lookups, and queue systems to batch updates to the vector database. Each component makes sense in isolation, but together they create a fragile system held together with duct tape and prayer.

• CDC streams introduce race conditions when multiple tables update simultaneously. Cache layers create eventual consistency issues. Queue systems add latency and potential message loss. Every component is another place where data can get stuck, stale, or simply wrong.

Beyond its fragility, this architecture is expensive. Changing a single customer record, for example, can require recalculating attributes for thousands (even millions) of vectors because it's too complex to determine exactly which vectors are affected. Infrastructure costs balloon from compute waste, and engineering time gets consumed maintaining this complexity

• Design patterns exist for building these pipelines correctly (incremental computation, surgical updates instead of batch recalculation). Implementing them, though, requires investing significant engineering effort that most teams simply can't justify and so they burn compute cycles and developer time keeping a fragile pipeline running. Sound familiar?

It doesn’t have to be this way. Materialize can streamline your vector database ingestion pipeline by keeping attributes up to date to support filtering and reranking on fresh, correct data. The key is using incremental view maintenance to move core denormalization work from a reactive approach where attribute and embedding calculations happen on demand, to a proactive one where work happens as source systems change (and only on exactly what has changed).

Traditional vector pipeline architectures force you to choose between expensive denormalization when writing to your vector database or expensive denormalization when reading from it. But with Materialize it’s continual and incremental.

Materialize eliminates the fundamental pipeline tradeoffs for operating with vectors, and also search in general. You can now choose where each attribute lives, whether in your vector database or externally, based on write patterns (rather than computational complexity).

Materialize sits between your operational databases (Postgres, MySQL, etc.) and your vector database (Pinecone, Weaviate, turbopuffer, etc.) as a transformation layer that maintains live, incrementally-updated views of your data.

The shift is simple, yet radical. The way enterprise AI systems are currently being built (and frequently abandoned) is reactive: computing results on demand as queries arrive. Adding indexes to underlying tables can speed things up a bit but, ultimately, every time a vector needs to be written updated, getting the latest attributes requires grinding over millions or billions of rows while applying business logic.

The breakthrough with Materialize is that instead of just indexing tables, you can index the views themselves. When you do this, the view becomes incrementally and continuously maintained as writes (including updates and deletes) happen upstream. Materialize’s proactive computation keeps vector data real-time and always correct as data changes.

Now organizations can build vector pipelines that work proportionally to what changed, vs. ones meant to minimize query complexity.

This pattern is not about real-time data streaming for its own sake. Generic data streaming platforms like Kafka or Flink move data in real-time, but they don't solve the transformation and maintenance problem. While Flink does technically offer transformation capabilities, it’s hard to achieve transactional consistency, and even more complex to attempt incremental computations. You could stream every database change into Kafka instantly, but you still have to write complex code to:

• Join data across multiple sources
• Calculate derived metrics (like priority scores)
• Keep those calculations up-to-date as data changes
• Handle the complexity of incremental updates

Real-time streaming gets you fresh events, but not fresh context. Materialize gives you the context you need.

Materialize specifically solves a central AI data challenge: taking normalized operational data (customer tables, order tables, ticket tables spread across multiple databases) and transforming it into the denormalized, enriched attributes that your vector database needs, continuously and correctly.

For example, a support ticket's "priority" attribute might require joining 5 tables, aggregating historical data, and applying business logic. Transforming data like this is a stumbling block for too many enterprise AI initiatives. Materialize maintains that transformation as a live view.

Materialize is purpose-built for the vector pipeline problem of tracking which vectors need updating when source data changes, enabling you to:

• Update attributes when metadata changes (customer upgrades to premium → update ticket priority attribute)
• Know when to re-embed (product description changes → re-embed that specific product vector)

"Real-time correct" means both fresh (reflects recent changes) and accurate (the calculation is right). Both matter for context engineering to provide AI systems with the information they need to efficiently return high-quality results.

Native filtering becomes practical: You can store attributes in your vector database AND keep them fresh because Materialize incrementally updates only what changed — without expensive denormalization.

External filtering becomes fast: You can join against Materialize's maintained views instead of your slow operational databases — no more paying for over-querying or retrieving exponentially more vectors than are actually required for the computation.

Finally, let’s put all of this together in a step-by-step architectural pattern for building a production-grade vector database pipeline with Materialize.

Materialize isn't opinionated about your downstream consumption pattern. It simply connects to your source systems — Postgres, MySQL, Kafka topics, etc. — and continuously ingests changes as they happen.

To encode business logic, you write standard SQL queries that join, aggregate, and transform your operational data into meaningful business entities. For example,

• A "customer" view that joins customer records with their lifetime value, support history, and account status
• A "ticket" view that calculates priority scores based on customer tier, SLA deadlines, and escalation history
• An "order" view that enriches order data with product details, shipping status, and payment information

These views represent the semantic model of your business—the enriched, denormalized data products your AI agents actually need.

In a normal database, views are just saved queries that run when you access them. In Materialize, when you create an index on a view, it becomes incrementally maintained:

• Materialize computes the view results once up front
• As source data changes, it updates only the affected rows in the view
• The view stays fresh automatically, with minimal computation

So instead of recalculating a priority score by scanning millions of tickets every time one customer's data changes, Materialize updates just that customer's tickets incrementally.

Now you connect Materialize to your vector database (Pinecone, Weaviate, turbopuffer, etc.). You subscribe to changes in your maintained views, and when attributes change, you push those updates to your vector database.

Materialize doesn't dictate how you consume the updates downstream. You have flexibility to:

• Subscribe to a live SQL query that pushes changes as they happen
• Batch updates together for efficiency
• Push changes to Kafka and handle them in your own application code

At scale, a common pattern is to batch these updates for throughput, but the key is: you're not updating everything, only those vectors whose attributes actually changed.

Finally, when your AI agent queries the vector database, it gets:

• Fresh results (attributes reflect changes from milliseconds ago)
• Correct results (the complex joins and business logic were computed right)
• Fast results (no expensive joins at query time)

Your AI systems and agents can perform tasks and make decisions with confidence because the context they work with is trustworthy and appropriate.

For production AI initiatives using vector databases, your entire vector pipeline matters. Bottlenecks in your ability to ingest context quickly and correctly will fundamentally limit the experiences you can deliver.

This architecture moves the expensive transformation work from vector computation on-demand (when writing to or querying vectors) to continuous and incremental (Materialize handles it automatically as source data changes). That fundamental shift is the difference between an AI agent in production vs. an abandoned PoC.

Materialize offers a solution by providing incrementally-updated views that keep your vector database attributes fresh. Beyond just fresh attributes, Materialize opens the door to extremely efficient pre- and post-filtering by enabling complex joins against live tables. Finally, by tracking exactly when important context changes, Materialize provides a foundation for surgical re-embedding that keeps context fresh while massively reducing inference costs compared to wasteful batch approaches.

Adding Materialize to your stack does involve additional cost but typically pays for itself through reduced compute infrastructure and dramatically improved developer productivity. Many companies find that adding Materialize ultimately reduces complexity in their data transformation pipeline.

Whether you're building complex agent workflows or simple semantic search features in your applications, adding Materialize into your vector database pipeline gives you fresher context, better recall, and lowers the total cost of your entire vector stack.

Ready to deliver a better search experience to your customers? Try Materialize on your laptop, start a free cloud trial, or deploy to production with our free-forever community edition.