Real-Time RAG Pipelines: How CDC Keeps Your AI Context Fresh

Your AI chatbot just told a customer their order shipped three hours ago. The problem? That order is still sitting in a warehouse, waiting for a carrier pickup. The customer is confused, your support team is fielding an angry follow-up, and the AI looks like it made the whole thing up. But the model didn’t hallucinate. It retrieved perfectly real information from your knowledge base. That information just happened to be six hours old.

This is the dirty secret of most RAG implementations in production today. The retrieval part works beautifully. The vector search is fast. The prompt engineering is tight. The LLM generates fluent, well-structured responses. But the data sitting in the vector database — the context the AI retrieves and reasons over — was last refreshed by a batch job that ran at 2 AM. And it is now 3 PM. The world has moved on. Your AI has not.

If you have been building RAG systems and wondering why your AI occasionally serves up confident-sounding nonsense, the answer probably is not a better model or fancier prompt templates. It is a data freshness problem. And the fix comes from an unlikely corner of the data engineering world: Change Data Capture.

What Is RAG and Why It Dominates AI Architecture

Before we dig into the freshness problem, let’s make sure we are on the same page about what RAG actually is and why it has become the dominant pattern for building AI applications.

Retrieval-Augmented Generation (RAG) is an architecture pattern where a large language model retrieves relevant context from an external knowledge base before generating a response. Instead of relying entirely on what the model memorized during training, you give it fresh, specific information to reason over at query time. The model’s job shifts from “recall everything” to “reason over what I just retrieved.”

The basic flow looks like this:

1. A user asks a question
2. The system converts that question into an embedding (a numerical vector)
3. That embedding is used to search a vector database for semantically similar documents
4. The top results are injected into the LLM’s prompt as context
5. The LLM generates a response grounded in that retrieved context

RAG won the architecture wars for several compelling reasons:

• It reduces hallucinations. When the model has specific, relevant documents to reference, it is far less likely to make things up. The retrieved context acts as a factual anchor.
• It grounds AI in your actual data. Instead of generic training knowledge, the model works with your product catalog, your support tickets, your internal policies — whatever you put in the knowledge base.
• It is dramatically cheaper than fine-tuning. Fine-tuning a large model on your data costs tens of thousands of dollars and needs to be repeated as your data changes. RAG just needs a vector database and an embedding pipeline.
• Your data stays private. With RAG, sensitive data lives in your own infrastructure. You never need to send proprietary information to a model provider for training.
• It adapts to new information without retraining. Update the knowledge base, and the AI’s answers improve immediately — at least in theory. And that “in theory” part is exactly where things get interesting.

For a deeper look at how real-time data powers AI applications, check out our AI/ML pipelines solutions page.

The Freshness Gap in RAG Pipelines

Here is where most RAG tutorials leave you hanging. They show a clean, satisfying workflow: take your documents, chunk them into pieces, generate embeddings, load them into a vector database, and query away. Job done. Ship it.

But that workflow describes a one-time data load. It is a snapshot. And in the real world, data does not sit still.

Product prices update hourly during flash sales. Support tickets get resolved, escalated, and reassigned throughout the day. Customer records change as people update their profiles, change addresses, or modify subscriptions. Inventory fluctuates as orders come in and shipments go out. Policy documents get revised. Employee directories shift as people join, leave, or change roles.

The typical approach to keeping a RAG knowledge base current looks something like this:

1. A cron job runs every few hours (optimistically every hour, realistically every 6-24 hours)
2. It queries the source database for records modified since the last run
3. It re-chunks and re-embeds the changed documents
4. It upserts the new embeddings into the vector database
5. It goes back to sleep until the next scheduled run

The gap between those ingestion runs is what we call the freshness gap. And it is the root cause of an entire class of RAG failures that teams often misdiagnose as model problems or retrieval quality issues.

What the Freshness Gap Looks Like in Practice

Consider a concrete scenario. A customer messages your AI support assistant at 2:30 PM: “Where is my order?” The order was marked as “shipped” in your database at 11:45 AM. But the last batch ingestion ran at 6:00 AM. The vector database still contains the old record showing “processing.” The AI retrieves this stale context and confidently tells the customer their order is being processed and should ship soon.

The customer, who already received a shipping confirmation email at noon, now thinks your AI is broken. They escalate to a human agent. Trust in the AI assistant erodes. And the irony is, your RAG pipeline did its job perfectly — it just had stale data to work with.

This problem compounds as you scale. A single stale record is an annoyance. Hundreds of stale records across thousands of daily queries become a systemic trust issue. Users learn that the AI cannot be relied upon for time-sensitive questions, and they route around it entirely, which defeats the purpose of building the thing in the first place.

The Batch Ingestion Blindspot: Deletes

There is another problem with batch-based RAG ingestion that is even more insidious: deletes. When a record is removed from your source database — a product is discontinued, a support article is deprecated, a customer account is closed — batch ingestion jobs often fail to propagate that deletion to the vector database. The query that ran on the source only looked for records with a modified_at timestamp after the last run. Deleted records do not show up in that query. They are just gone.

The result is ghost data: embeddings in your vector database that reference documents, products, or records that no longer exist. Your AI will happily retrieve these phantom entries and generate responses based on them. A customer asks about a product that was recalled last week, and the AI describes it glowingly because the old product page embedding is still sitting in the vector store.

Handling deletes correctly in a batch pipeline requires maintaining a separate reconciliation process that compares the full set of record IDs in your source against the full set in your vector database, identifies the orphans, and removes them. Most teams skip this step because it is complex and expensive to run at scale.

How CDC Closes the Freshness Gap

This is where Change Data Capture enters the picture and changes the game entirely.

Change Data Capture (CDC) reads the database transaction log — that detailed, append-only journal that every database maintains for crash recovery and replication — and streams every change as it happens. Every insert, every update, every delete. In real time. Not on a schedule. Not in batches. Continuously.

Instead of a cron job asking the database “what changed since last time?”, CDC taps into the stream of changes as they are committed to the database. The difference is fundamental.

How CDC Reads Transaction Logs (Not Polling)

Traditional batch ingestion polls the source database by running queries against live tables. This puts load on the database, can miss changes that happen between polls, and gives you data that is only as fresh as your last query. It is the equivalent of constantly asking someone “did anything change?” every few minutes.

Log-based CDC takes a completely different approach. It reads the database’s own transaction log — the Write-Ahead Log (WAL) in PostgreSQL, the binlog in MySQL, the oplog in MongoDB. This log already exists. The database writes to it for its own internal purposes. CDC just reads it, like quietly reading over someone’s shoulder as they write.

This matters for several reasons:

• Sub-second latency. Changes are captured within milliseconds of being committed. The gap between “data changed in the source” and “change is available downstream” shrinks from hours to fractions of a second.
• Zero polling overhead. No queries hit your production database. The CDC process reads from the log, which the database was going to write anyway. The impact on source database performance is minimal.
• Complete change capture. Every insert, update, and delete is captured. Nothing falls through the cracks. There is no window between poll intervals where changes can be missed.
• Before and after values. CDC captures both the old and new state of a record on updates. This is incredibly valuable for understanding what changed, not just that something changed.
• Explicit delete events. When a record is deleted, CDC captures a delete event with the full record content. No more ghost data. No more reconciliation jobs.

For RAG pipelines specifically, this means your vector database can be updated within seconds of a change in the source — not hours. That customer asking “where is my order?” at 2:30 PM gets the correct “shipped” status because the CDC pipeline pushed that update to the vector database at 11:45 AM, right when it happened.

If you want a deeper technical understanding of how CDC works under the hood, our guide on what is Change Data Capture covers the mechanics in detail.

Architecture: Building a CDC-Powered Real-Time RAG Pipeline

Let’s walk through the full architecture of a CDC-powered RAG pipeline, stage by stage. The flow moves through five key components, each with a specific role.

Stage 1: Source Database

Everything starts with your operational databases — the systems of record that contain the data your AI needs to reason over. These might be:

• PostgreSQL storing customer orders, product catalog, and account information
• MySQL holding support tickets, knowledge base articles, and CRM data
• MongoDB containing user profiles, content documents, and activity logs
• SQL Server or Oracle running enterprise ERP, HR, or financial systems
• DynamoDB powering high-throughput application state

The key insight is that these databases already have the data your RAG pipeline needs. You do not need to maintain a separate “AI-ready” copy. CDC reaches into the source of truth and streams changes as they happen. Streamkap supports CDC from all of these sources through its 50+ pre-built connectors.

Stage 2: CDC Layer

The CDC layer connects to each source database’s transaction log and produces a continuous stream of change events. Each event includes:

• The operation type (insert, update, or delete)
• The full record (or the before/after states for updates)
• Metadata like the table name, primary key, and transaction timestamp

This is where a managed CDC platform like Streamkap eliminates a tremendous amount of operational complexity. Instead of deploying and managing Debezium clusters, Kafka Connect workers, and Kafka brokers yourself, you configure a connector through a UI and the platform handles the rest — including automatic schema evolution, self-healing pipelines, and sub-250ms end-to-end latency.

The CDC layer needs to be reliable above all else. If it drops a change event, your vector database drifts out of sync with reality. Streamkap’s platform is built specifically for this kind of mission-critical, real-time data delivery.

Stage 3: Transformation and Embedding Generation

This is where the raw CDC events are transformed into something a vector database can actually use. This stage handles several responsibilities:

Chunking: The change event’s content needs to be broken into appropriately sized chunks for embedding. A product record might become a single chunk. A long support article might be split into several. The chunking strategy depends on your use case (we will dig deeper into this later).

Embedding generation: Each chunk is sent to an embedding model (OpenAI’s text-embedding-3-small, Cohere’s embed-v3, or a locally hosted model) to produce a vector representation. This vector is what the vector database uses for similarity search.

Metadata enrichment: Alongside the embedding, you attach metadata from the original record — the source table, primary key, record type, timestamp, and any other fields your RAG application needs for filtering or display.

Delete handling: When the CDC event is a delete, the transformation step emits a delete command targeted at the vector database, keyed on the source record’s primary key.

Streamkap’s managed Apache Flink transformations can handle this entire stage. You can write transformation logic in SQL, Python, or TypeScript that runs on every CDC event as it passes through the pipeline. This means the embedding API call, the chunking logic, and the metadata enrichment all happen in-stream, without you deploying or managing any additional infrastructure.

Stage 4: Vector Database

The transformed and embedded data lands in your vector database, which serves as the knowledge base for your RAG application. Each entry in the vector database contains:

• The embedding vector (for similarity search)
• The original text chunk (to be included in the LLM prompt)
• Metadata (source record ID, table, timestamp, record type)

When new CDC events arrive, the corresponding entries in the vector database are created, updated, or deleted in near real-time. This is the magic moment: your knowledge base is now a living reflection of your source databases, not a stale snapshot.

Stage 5: RAG Application

Finally, your RAG application queries the vector database at request time. A user asks a question, the application generates a query embedding, performs a similarity search, retrieves the most relevant chunks, injects them into the LLM prompt, and the model generates a grounded response.

Because the vector database is continuously updated via CDC, the retrieved context is always current. The order that shipped five minutes ago? It shows as shipped. The product that was just discontinued? Its embedding has been removed. The support article that was updated with a new troubleshooting step? The latest version is what gets retrieved.

The entire pipeline runs continuously, with no batch windows, no scheduled jobs, and no freshness gaps. Data flows from source database to vector database to AI response in a seamless, always-on stream.

Choosing Your Vector Database

The vector database is the heart of your RAG pipeline’s knowledge layer. There are several strong options available, each with different strengths depending on your scale, operational preferences, and deployment model.

Pinecone

Pinecone is a fully managed vector database built specifically for production AI applications. You do not manage any infrastructure — it is a pure API. Pinecone excels at large-scale, low-latency similarity search and offers features like namespace isolation, metadata filtering, and hybrid search (combining vector and keyword search). It is a strong choice for teams that want to focus entirely on application logic and not worry about database operations.

Weaviate

Weaviate is an open-source vector database that offers both self-hosted and managed cloud options. It supports multiple vectorization modules (including built-in integrations with OpenAI and Cohere), GraphQL-based querying, and hybrid search. Weaviate’s flexibility makes it a good fit for teams that want more control over their deployment or need to run the database in their own infrastructure for compliance reasons.

Milvus / Zilliz

Milvus is a high-performance, open-source vector database designed for massive scale. It can handle billions of vectors with low query latency. Zilliz is the fully managed cloud version. If your RAG pipeline processes large volumes of data and needs to serve high-throughput search queries, Milvus is worth serious consideration.

Qdrant

Qdrant is a Rust-based vector database known for its speed and efficiency. It supports rich filtering, payload indexing, and both gRPC and REST APIs. Qdrant offers both open-source self-hosted deployment and a managed cloud service. Its efficient memory usage makes it a good choice for cost-sensitive deployments that still need strong performance.

pgvector

pgvector is a PostgreSQL extension that adds vector similarity search to an existing PostgreSQL database. If you are already running PostgreSQL and your vector search needs are moderate, pgvector lets you keep everything in one database. It is the simplest path to getting started with RAG, though it may not match the performance of purpose-built vector databases at scale.

Chroma

Chroma is a lightweight, open-source embedding database designed for ease of use. It is excellent for prototyping, development, and smaller-scale RAG applications. Chroma’s simple API and minimal setup make it ideal for getting a proof-of-concept running quickly before scaling to a more robust solution.

The important thing to understand is that the CDC layer is entirely agnostic to your vector database choice. Streamkap streams changes from your source databases, the transformation layer handles embedding generation, and the output can be routed to any of these vector stores through Kafka output or custom destination configurations. You can even write to multiple vector databases simultaneously if your architecture calls for it.

Chunking Strategies for CDC-Powered RAG

When data changes arrive via CDC, you need a strategy for converting those changes into chunks that get embedded and stored in your vector database. The chunking strategy directly impacts retrieval quality — chunk too big and you dilute the semantic signal; chunk too small and you lose important context.

CDC-powered pipelines have a significant advantage over batch pipelines here: because you know exactly what changed (thanks to the before/after values in CDC events), you can be surgical about what gets re-chunked and re-embedded, instead of blindly reprocessing entire document collections.

Document-Level Chunking

The simplest approach: treat each database record as a single chunk. One product record becomes one embedding. One support ticket becomes one embedding. One knowledge base article becomes one embedding.

When it works well: Records are relatively short and self-contained. A product catalog entry with name, description, price, and specs fits comfortably in a single embedding. Customer order records are naturally bounded.

When it breaks down: Records are long or contain multiple distinct topics. A lengthy knowledge base article covering five different troubleshooting scenarios should probably be split so that vector search can retrieve the specific scenario that matches the user’s question.

CDC advantage: When a product’s price changes, you re-embed that single product record. One CDC event triggers one embedding API call and one vector database upsert. Fast, cheap, and precise.

Field-Level Chunking

A more granular approach: embed specific fields or combinations of fields from each record, rather than the whole thing. For a product record, you might create separate embeddings for the product description, the technical specifications, and the customer reviews.

When it works well: Records have distinct, independently searchable sections. A support article with separate “problem description,” “root cause,” and “resolution” sections benefits from field-level chunking because a user’s question might match only one of those sections.

When it breaks down: Fields are short and meaningless in isolation. Embedding a single “status” field or a product SKU produces a vector that is not semantically useful.

CDC advantage: CDC’s before/after values let you detect exactly which fields changed. If only the price field updated on a product record, you only need to re-embed chunks that include the price — not the entire product description or specs. This dramatically reduces embedding API costs and vector database write volume.

Hybrid Approaches

In practice, most production RAG pipelines use a hybrid strategy:

• Short, self-contained records (orders, transactions, customer profiles) get document-level chunking
• Long, multi-topic content (articles, documentation, policies) gets split using overlapping fixed-size chunks or semantic boundaries
• Structured fields with discrete meaning (status, category, metadata) are stored as filterable metadata rather than embedded text

The key is that CDC enables incremental re-embedding. In a batch pipeline, you might re-embed your entire knowledge base nightly “just to be safe.” With CDC, you only process what actually changed, and you process it immediately. This is not just faster — it is dramatically cheaper in terms of embedding API costs, especially at scale.

Handling Deletes and Updates: The Hardest Part of Real-Time RAG

If there is one thing that separates production-grade real-time RAG pipelines from toy demos, it is proper handling of deletes and updates. Get this wrong, and your AI will serve up ghost data — confidently generating responses based on products that no longer exist, policies that have been revoked, or customer records that have been purged.

The Ghost Data Problem

Imagine this: your company discontinues a product due to a safety recall. The product is removed from your database. But the embedding for that product’s description, specifications, and glowing reviews still sits in your vector database. A customer asks your AI assistant about the product. The AI retrieves the old embedding, finds a strong semantic match, and enthusiastically describes the recalled product as if it is still available.

This is not a hallucination. The model is faithfully reporting what it found in the knowledge base. The problem is that the knowledge base contains data that should no longer exist. Ghost data is arguably worse than hallucination because it is harder to detect — the response looks well-grounded and confident because it is grounded in an actual document. It just happens to be a document that should have been deleted.

How CDC Solves Delete Propagation

CDC captures delete events explicitly. When a record is removed from the source database, the transaction log records that deletion, and the CDC pipeline emits a delete event containing the record’s primary key (and often the full record content).

The pattern for handling deletes in your vector database is straightforward:

1. Store source metadata with every embedding. When you insert an embedding into the vector database, include the source table name and source record primary key as metadata fields.
2. On CDC delete events, remove matching embeddings. When the transformation layer receives a delete event, it issues a delete command to the vector database targeting all embeddings whose source record ID matches the deleted record.
3. For field-level chunking, remove all related chunks. If a single source record produced multiple embeddings (due to field-level or fixed-size chunking), the delete must remove all of them. Using a consistent record ID in the metadata makes this possible.

This pattern ensures that your vector database is an accurate, up-to-date reflection of your source data at all times. No ghost data. No reconciliation jobs. No periodic “full refresh” runs to clean up orphaned embeddings.

Handling Updates Correctly

Updates are slightly more nuanced. When a record is updated, the corresponding embeddings need to be replaced, not just appended. The correct sequence is:

1. CDC captures the update event with before and after values
2. The transformation layer generates new embeddings from the updated content
3. The old embeddings (identified by source record ID in metadata) are deleted from the vector database
4. The new embeddings are inserted

Some vector databases support an “upsert” operation that combines steps 3 and 4, which simplifies the logic. But be careful with field-level chunking: if the number of chunks changes after an update (say, an article that was previously three chunks is now five chunks after an edit), a simple upsert by chunk ID will not clean up the old chunks that no longer have corresponding new ones. The safest approach is always “delete all old chunks for this record ID, then insert all new chunks.”

Real-World Use Cases for Real-Time RAG

The value of keeping RAG context fresh is not hypothetical. Here are concrete scenarios where the freshness gap causes real problems — and where CDC-powered RAG pipelines deliver measurable improvements.

Customer Support AI

Support AI assistants are one of the most common RAG applications, and also one of the most sensitive to data freshness. Customers ask about order status, billing issues, account changes, and service disruptions. All of this information changes constantly.

With batch-ingested RAG, a support AI might tell a customer their refund is “being processed” when it was actually completed two hours ago. Or it might suggest a troubleshooting step that was removed from the knowledge base after it was found to cause more problems than it solved.

With CDC-powered RAG, the support AI retrieves the current order status, the latest troubleshooting steps, and the actual state of the customer’s account — as of seconds ago, not hours ago. This is the difference between an AI assistant that builds trust and one that erodes it.

E-Commerce Product Assistant

Product catalogs are among the most volatile datasets in any business. Prices change for promotions and competitive adjustments. Inventory levels fluctuate as orders come in and restocks arrive. Products are added, discontinued, or modified. Seasonal items rotate in and out.

A product assistant powered by stale RAG context might recommend an out-of-stock item, quote yesterday’s price, or describe a product version that has been superseded. CDC ensures the assistant always knows the current price, current availability, and current product details.

Internal Knowledge Base and HR Assistant

Large organizations maintain internal knowledge bases covering policies, procedures, benefits, organizational structure, and more. These documents are updated frequently — new policies are introduced, old ones are revised, org charts shift with every hire and departure.

An internal AI assistant that retrieves outdated policy information can cause real problems. An employee might be told they have a benefit that was eliminated in the last policy update, or follow a procedure that has been superseded. CDC-powered RAG keeps the internal knowledge base in lockstep with the source of truth.

Financial Services

Financial applications have some of the strictest requirements for data accuracy and freshness. Account balances, transaction histories, compliance documents, and risk assessments change frequently and have direct financial and regulatory consequences.

A financial AI assistant that retrieves stale account information is not just inconvenient — it could lead to incorrect financial decisions or compliance violations. Real-time RAG powered by CDC ensures the AI always works with current financial data.

Real-World Example: InHire

InHire, a recruitment technology company, uses Streamkap to power real-time AI capabilities in their platform. By streaming candidate and job data changes via CDC, InHire ensures their AI matching algorithms always work with the latest information — new job postings, updated candidate profiles, and changing requirements are reflected immediately. This is real-time RAG in action, applied to a domain where the difference between current and stale data directly impacts hiring outcomes.

For more on how Streamkap supports AI/ML workloads, visit our AI/ML engineers page.

Batch vs Streaming RAG: A Direct Comparison

To make the trade-offs concrete, here is a side-by-side comparison of batch-ingested RAG versus CDC-powered streaming RAG.

The pattern is clear: batch ingestion is simpler to start with, but CDC-powered streaming RAG is what you need when data freshness directly impacts user trust and business outcomes. And with a managed platform like Streamkap handling the CDC infrastructure, the operational complexity gap narrows significantly.

Getting Started with Real-Time RAG

Ready to close the freshness gap in your RAG pipeline? Here is a practical roadmap for building a CDC-powered real-time RAG system.

Step 1: Identify Your Source Databases

Start by mapping the databases that contain the data your RAG application needs. Which tables power the knowledge base your AI retrieves from? Common sources include:

• Product catalogs and inventory systems
• Customer records and order management databases
• Support ticket systems and knowledge base platforms
• Content management systems and document repositories
• CRM and ERP databases

Streamkap connects to PostgreSQL, MySQL, MongoDB, DynamoDB, SQL Server, Oracle, and more. Check the full list of available connectors.

Step 2: Set Up CDC From Those Sources

Configure CDC to stream changes from each source database. With Streamkap, this means creating a source connector through the platform UI, specifying which tables to capture, and the pipeline begins streaming changes immediately.

Key things to configure:

• Table selection: Choose the specific tables that feed your RAG knowledge base
• Column filtering: Exclude columns that are not relevant to your RAG context (reduces data volume and embedding costs)
• Schema evolution: Enable automatic schema evolution so your pipeline does not break when source schemas change (Streamkap handles this automatically)

Step 3: Build Your Embedding Pipeline

The transformation layer converts raw CDC events into embeddings. Using Streamkap’s managed Flink transformations, you can write this logic in SQL, Python, or TypeScript:

1. Receive the CDC event with the operation type and record content
2. Apply your chunking strategy (document-level, field-level, or hybrid)
3. Call an embedding API (OpenAI, Cohere, or a self-hosted model) to generate vectors
4. Format the output with the embedding vector, text chunk, and metadata (including source record ID for delete handling)

The managed Flink environment means you do not need to provision, configure, or maintain a Flink cluster. You write the transformation logic, and Streamkap handles the execution.

Step 4: Route to Your Vector Database

Connect the output of your embedding pipeline to your chosen vector database. This can be done through:

• Direct API integration from the Flink transformation (calling the vector database’s API on each processed event)
• Kafka output from Streamkap, consumed by a lightweight service that writes to the vector database
• Custom destination connectors for specific vector database targets

Regardless of approach, make sure your routing handles all three CDC operation types: inserts create new embeddings, updates replace existing ones, and deletes remove stale ones.

Step 5: Query From Your RAG Application

With the pipeline running, your RAG application queries the vector database as usual. The difference is that the data it retrieves is now seconds old, not hours old. Your existing RAG application code does not need to change — the improvement happens entirely in the data layer.

Step 6: Monitor and Iterate

Once your real-time RAG pipeline is live, monitor these key metrics:

• End-to-end latency: Time from source database change to vector database update. With Streamkap, expect sub-250ms.
• Embedding pipeline throughput: Events processed per second. Make sure your embedding API can handle the volume.
• Retrieval accuracy: Are the AI’s responses more accurate and current? Track user feedback and correctness metrics.
• Cost: Monitor embedding API costs. CDC’s incremental approach should be significantly cheaper than full re-embedding on batch runs.

The Bottom Line: RAG Is Only as Good as Its Data

The AI industry has spent enormous energy on improving models, refining retrieval algorithms, and optimizing prompt engineering. All of that matters. But the most impactful improvement you can make to a RAG pipeline is often the simplest conceptually: keep the data fresh.

CDC is not a new technology. It has been powering real-time data integration, database replication, and streaming analytics for years. What is new is the recognition that the same technology that keeps your data warehouse in sync can keep your AI’s knowledge base in sync too.

The architecture is straightforward: CDC streams changes from your source databases, a transformation layer generates embeddings, and a vector database stays continuously updated. No batch windows. No freshness gaps. No ghost data from missed deletes.

Streamkap makes this architecture accessible without requiring you to become a Kafka and Flink expert. With sub-second latency, 50+ connectors, managed Flink transformations, automatic schema evolution, and self-healing pipelines, you can build a production-grade real-time RAG pipeline in hours, not months.

Plans start at $600/mo for Starter and $1,800/mo for Scale, with transparent pricing that includes all infrastructure costs.

Your AI deserves to work with data that is seconds old, not hours old. Your users certainly deserve it.

Start your free trial and see how fast your RAG pipeline can be.