TL;DR: Firebolt built a support chatbot using retrieval-augmented generation (RAG) and its native vector search. The system retrieves relevant content using vector similarity and feeds it to a language model for accurate, low-latency answers.

The chatbot returns precise answers to product-specific questions and applies access controls for internal vs. external content. The same approach is applicable across domains requiring secure, fast retrieval of proprietary data.

Building a Domain-Specific Support Chatbot with Firebolt

As AI continues to evolve, the ability to deliver fast, context-aware assistance is becoming essential for support and knowledge-driven applications. This post outlines how we built the Firebolt Support Chatbot using retrieval-augmented generation (RAG) and Firebolt’s native vector capabilities. It introduces our architectural decisions and performance considerations, laying the groundwork for a deeper implementation walk-through in the follow-up blog.

We demonstrated this chatbot during Firebolt Forward, and you can watch the full demo recording below. 

WATCH DEMO: 11 - Customer Support Chatbot Demo - Product

The demo highlights how domain-specific AI can deliver sub-second answers to technical product questions using Firebolt. We encourage you to explore the full set of Firebolt Forward sessions to learn more about our advancements in AI infrastructure, vector search, and next-generation data warehousing.

Why Firebolt for AI Applications

Firebolt offers unique advantages for AI-driven workloads:

• Low-latency vector search: Firebolt's engine delivers sub-second vector similarity search, essential for low-latency AI interactions.
• Unified data management: Unlike specialized vector databases such as Pinecone, Firebolt can store both embeddings and structured data in one system.
• High scalability: Firebolt handles large-scale vector and analytical workloads simultaneously without compromising performance.

These characteristics make Firebolt well-suited for powering real-time, production-grade AI systems.

Why We Built the Firebolt Support Bot

We developed the Firebolt support chatbot to give users accurate, immediate answers—without needing to sift through documentation or wait on support tickets. Traditional LLMs are not optimized for domain-specific knowledge like Firebolt’s SQL syntax or index behavior. That’s where RAG comes in.

Using Firebolt as a vector database, we built a system that can:

• Retrieve semantically relevant documentation chunks.
• Provide precise, Firebolt-specific responses.
• Ensure secure access controls by differentiating between internal and customer-facing content.

The chatbot is also extensible: changing prompts enables features like document summarization or text-to-SQL generation.

This architecture is not limited to support chatbots. You can apply the same pattern in domains where speed, accuracy, and secure retrieval of proprietary data are essential.

For example:

• Healthcare: Enable clinical teams to retrieve patient records and clinical trial data in seconds, reducing time to diagnosis and enabling AI-assisted treatment recommendations. Use RAG-based systems to support claims validation and compliance workflows by accessing internal documentation and structured datasets.
• Marketing & Advertising: Power chatbots that surface low-latency campaign insights or perform real-time segmentation across historical performance data. Leverage Firebolt’s vector similarity search to retrieve the most relevant performance drivers, helping marketers optimize bids and creative strategies.
• Finance: Build assistants to support fraud detection, transaction monitoring, or investment analysis. Use Firebolt to retrieve semantically similar past cases, compliance rules, or financial metrics across large volumes of structured and semi-structured data.

Each use case follows the same architecture: chunk and embed relevant data, store it in Firebolt, retrieve the top-matching context using vector search, and feed that to the LLM for precise, domain-specific outputs.—such as legal research, healthcare knowledge bases, or e-commerce support—using your own data and documents.

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Demonstrating Domain-Specific Accuracy

We asked the chatbot three questions to illustrate how RAG improves answer precision:

1. How are primary indexes implemented in Firebolt?
   The chatbot describes sparse indexing and integration with the Firebolt File Format, showing product-specific depth.
2. What are Firebolt’s aggregating indexes?
   It explains how they work with Firebolt’s storage format and handle non-update-friendly aggregates.
3. What are the advantages of Firebolt?
   The response includes sub-second performance, high concurrency, and AI integration—all uniquely relevant to Firebolt.

In each case, the chatbot surfaces correct and contextually accurate answers, showing the value of combining RAG with a purpose-built vector engine.

How RAG Works (Overview)

Retrieval-augmented generation bridges the gap between generic language models and specialized knowledge. The process includes:

1. Ingestion: Domain documents are chunked and embedded using a model like nomic-embed-text.
2. Storage: Embeddings are stored in Firebolt for high-speed vector retrieval.
3. Querying: User queries are embedded, compared via cosine similarity, and the top-k chunks are passed to the LLM.

Firebolt’s ability to run fast similarity search against embedded documents enables domain-specific precision at scale.

What’s Next

In the technical deep-dive blog, we provide an in-depth breakdown of the implementation, including:

• Our document processing pipeline
• Chunking strategies and embedding model selection
• Schema design for vector storage in Firebolt
• Query logic for cosine similarity search
• LLM prompt construction and performance benchmarks

We also open-source the full implementation, enabling developers to build their own Firebolt-powered RAG chatbot.

Firebolt combines the scalability of a modern data warehouse with the vector-native features needed for AI. It serves as both a high-performance vector database and a robust SQL engine, offering a unified solution for building AI applications.

If you're exploring how to bring GenAI into your product with speed and accuracy, Firebolt offers the performance foundation and architectural simplicity to make it possible.