How do you build an AI chatbot that uses my company's data?

Summary

• Retrieval-augmented generation (RAG) connects an LLM to your enterprise data at query time, reducing hallucinations and keeping chatbot answers accurate without costly retraining.
• Data preparation-including strategic chunking, metadata tagging, and vector embedding storage-is the foundation of chatbot quality and retrieval accuracy.
• Agent Bricks on the Databricks Platform provides a unified control plane to build, govern, and continuously improve enterprise AI chatbots with built-in security, evaluation, and support for any model or framework.

How to build an AI chatbot that uses your company's data

Your company's most valuable knowledge lives in internal documents, databases, CRM records, and support tickets. A general-purpose chatbot cannot access any of it. When employees or customers ask questions requiring proprietary context, a standard large language model guesses, often producing inaccurate or fabricated answers.
According to McKinsey Global Institute, employees spend nearly 20% of their workweek searching for and gathering internal information. A chatbot grounded in your own data can reclaim that time by surfacing answers instantly. Building one requires connecting an LLM to your enterprise data, preparing that data for retrieval, and establishing evaluation loops that keep answers accurate. Organizations scaling AI agents across their organization are already seeing these benefits firsthand.

What is RAG and why does it matter for enterprise chatbots?

Retrieval-augmented generation (RAG) retrieves the most relevant information in real time from trusted sources, then uses that content to generate accurate, context-aware responses. Instead of embedding all company knowledge into the model's weights, the system pulls fresh data at query time.
A typical RAG pipeline includes three stages:

1. Ingestion: Internal documents are chunked, embedded as vectors, and stored in a vector database.
2. Retrieval: The system searches the vector database for the most relevant chunks when a user asks a question.
3. Generation: The LLM receives those chunks as context and produces a grounded answer.

RAG reduces hallucinations, keeps the AI current, and avoids the cost of constant retraining.

How to prepare your data for a RAG chatbot

Data quality determines chatbot quality. Focus on building a high-quality knowledge base and optimizing your retrieval strategy.

• Inventory sources: Catalog databases, PDFs, wikis, support tickets, and API endpoints containing the knowledge your chatbot needs.
• Clean and normalize: Remove duplicates, outdated content, and formatting artifacts.
• Chunk strategically: Break documents into segments that each represent a complete thought. Use overlap between chunks to preserve context across boundaries.
• Attach metadata: Tag chunks with source, date, department, and topic. Metadata improves retrieval accuracy, filtering, and source citation.

Building and storing embeddings in a vector database

An embedding model converts each chunk into a numerical vector that captures its meaning. These vectors are stored in a vector database optimized for similarity search.

Decision	Considerations
Embedding model	Balance dimensionality, accuracy, and latency. Open-source models like Sentence Transformers work well for many use cases.
Indexing strategy	Approximate nearest neighbor (ANN) indexes trade slight accuracy loss for major speed gains at scale.
Sync frequency	Decide whether embeddings update in batch or near-real time based on how often source data changes.
Hybrid search	Combining vector similarity with keyword search often improves retrieval for domain-specific terminology.

Keeping your chatbot accurate and preventing hallucinations

Without systematic evaluation, incorrect responses go undetected until they cause real damage.

• Ground every response: Only generate answers from retrieved enterprise data, not the model's general training knowledge.
• Evaluate continuously: Build domain-specific benchmarks and run automated LLM judges against them to catch drift early. Tools like the OfficeQA benchmark demonstrate how end-to-end grounded reasoning can be measured.
• Incorporate human feedback: Route low-confidence or flagged responses to subject-matter experts. Feed corrections back into the system.
• Set confidence thresholds: When retrieval scores are low, have the chatbot say "I don't know" rather than guess.

Security and access control for enterprise chatbots

Enterprise chatbots often touch sensitive data. Security must be designed in from the start. Building trusted AI agents requires governance capabilities that scale with confidence.

• Role-based access controls: Ensure the chatbot only surfaces data a user is authorized to see.
• Data lineage tracking: Log which source documents contributed to each response for auditability.
• Policy enforcement: Define guardrails that prevent the chatbot from disclosing restricted information.
• Encryption and network isolation: Protect data in transit and at rest within your security perimeter.

How Agent Bricks supports enterprise chatbot development

Agent Bricks is the unified control plane to build, run, and govern AI agents across any model, provider, or framework, eliminating agent sprawl through centralized management.

• Open and governed: Build with any AI model, OpenAI, Gemini, Llama, Anthropic, while maintaining granular access controls, lineage tracking, cost controls, and policy enforcement from AI models down to the underlying data.
• Contextual reasoning: Built natively into the Databricks Platform, Agent Bricks grounds agents in semantic knowledge graphs that understand your business data, producing state-of-the-art accuracy for document retrieval and processing.
• Self-improving: Agent Bricks builds benchmarks using your own data and tasks, evaluates every output against them, and leverages prompt optimization, fine-tuning, and RLHF to automatically improve performance without costly rebuilds.

FAQs

What is retrieval-augmented generation (RAG) and how does it work for building custom AI chatbots?

RAG combines information retrieval with text generation. The chatbot retrieves relevant chunks from your knowledge base at query time, then passes them to the LLM as context for generating an answer.

How do you connect a large language model to proprietary company data securely?

Connect an LLM through a retrieval layer that queries a vector database or API, with role-based access controls governing which data the model can access. Agent Bricks provides granular access controls, lineage tracking, and policy enforcement across AI models and data assets.

What are the best ways to prepare and chunk internal documents for an AI chatbot knowledge base?

Use semantic or section-based chunking to break documents into meaningful segments. Add metadata like source and date, and maintain overlap between chunks to preserve context.

How do you build a vector database to store company data embeddings for chatbot retrieval?

Chunk your data sources, transform chunks into vectors with an embedding model, and store those vectors in a vector database optimized for similarity search. Retrieval quality depends on chunking and embedding choices.

What data formats and sources can be used to ground an AI chatbot on enterprise data?

Enterprise chatbots can ingest PDFs, Word documents, HTML pages, CSV files, database records, CRM exports, support tickets, and API responses. RAG supports both structured and unstructured data.

How do you prevent an AI chatbot from hallucinating when answering company-specific questions?

Ground every response in retrieved enterprise data, set confidence thresholds, and use continuous evaluation with human feedback loops. Agent Bricks builds benchmarks from your own data and evaluates every output against them.

What security and access control measures are needed for an AI chatbot on sensitive business data?

Implement role-based access controls at the retrieval and generation layers, track data lineage, and enforce policies that restrict the chatbot to approved topics and authorized data.

How do you fine-tune a large language model on proprietary company data?

Fine-tuning trains an LLM on domain-specific datasets to internalize company terminology and patterns. RAG handles changing data, while fine-tuning suits stable, domain-specific workflows. Agent Bricks supports both approaches through prompt optimization, fine-tuning, and RLHF.

What tools and frameworks are commonly used to build a RAG-based chatbot for enterprise use cases?

Common components include an LLM, a vector database, an embedding model, an orchestration framework, and an evaluation layer. Agent Bricks unifies these into a single control plane that works with any model and any framework. You can learn more about how to build an autonomous AI assistant with Mosaic AI Agent Framework.

How do you evaluate and measure the accuracy of an AI chatbot answering questions from internal knowledge?

Use domain-specific benchmarks, automated LLM judges, and human feedback loops to measure retrieval relevance, answer correctness, and groundedness. Continuous evaluation catches quality drift before it reaches users.

Bring your company's data to life with an AI chatbot

Building an AI chatbot on proprietary data requires a governed pipeline for data preparation, retrieval, evaluation, and continuous improvement. Agent Bricks provides the unified control plane to build, run, and govern your chatbot, grounding it in semantic knowledge graphs built natively on the Databricks Platform so responses stay accurate, secure, and improving over time. Explore Agent Bricks to get started.