Imagine you're building an AI-powered assistant that helps users troubleshoot issues by answering questions like, “Why did my payment fail?” or “How do I set up multi-factor authentication?” To respond accurately, the assistant needs to pull from dozens of internal data sources — support tickets, help docs, product changelogs, and system logs — and deliver relevant, trustworthy answers in real time.

This is where retrieval-augmented generation (RAG) shines. By combining the reasoning power of large language models (LLMs) with your organization's private data, RAG delivers grounded, domain-specific responses that generic AI tools simply can’t match.

But without clean, up-to-date data flowing into the system, even the smartest LLMs will generate outdated or incomplete answers. Building RAG applications at scale depends on automated, unified data pipelines that make all your structured, unstructured, and semi-structured data available for retrieval — reliably and continuously.

That’s where Fivetran comes in.

With 700+ fully managed connectors and ELT pipelines that centralize structured, unstructured, and semi-structured data in data lakes and cloud data warehouses, Fivetran automates the data movement that fuels RAG. With Fivetran, the ingestion, transformation, and sync process is fully managed, ensuring data is fresh, compliant, and ready for retrieval by the LLM so the AI features stay accurate and useful as customer data evolves.

Plus, the Fivetran Quickstart Models for RAG, in combination with our support for vector database destinations, accelerate setup by transforming raw source data into retrieval-optimized chunks, embeddings, and metadata — giving AI applications the context they need without weeks of manual engineering. The result? Developers spend less time wrangling pipelines and more time building smarter applications.

3 ways to use RAG + Fivetran to turn data into intelligence

Once your RAG foundation is in place — with unified, continuously updated data pipelines and retrieval-ready context — the real opportunity lies in how you put it to work.

RAG isn’t a single-use tool; it’s a flexible framework that can power a wide range of AI features across your business. From customer-facing experiences like onboarding and support to behind-the-scenes copilots that boost internal efficiency, RAG applications can be tailored to deliver real-time intelligence wherever it’s needed most.

With Fivetran automating the data integration behind the scenes, you can reliably fuel these applications with clean, trusted, and always-fresh data — without burdening engineering teams. Below are three high-impact ways companies are combining RAG + Fivetran to turn static data into intelligent, scalable product capabilities and deliver next-generation AI features:

1. Document retrieval and synthesis – Quickly retrieve, make sense of, and serve up text-heavy information from across multiple sources.

• AI-powered support assistants
  
  • When a user reports a bug or asks a question, retrieve related help articles, changelog entries, or known issue tickets specific to their plan, product version, or usage history.
  • Reduce support team workload by pre-populating chatbot responses with accurate, dynamic content pulled from a knowledge base that evolves as the product does.
  • Auto-surface customer-specific past interactions, open tickets, or error logs from systems like Zendesk or Salesforce Service Cloud.

• Internal knowledge discovery
  
  • Let team members ask, “What’s our latest SOC 2 policy?” or “Who owns the integration with Stripe?” and get exact answers without switching tabs.
  • Automatically pull relevant policies, project docs, or historical decisions from systems or shared drives.
  • Reduce time lost searching through email threads or out-of-date wikis.

• Policy and risk copilots
  
  • Let compliance teams ask, “What are our current data retention policies in EMEA?” or “Have we updated our SOC 2 controls in the last 90 days?”
  • Automatically retrieve and summarize relevant policies, documentation, and change logs across tools like Confluence, Google Drive, or OneTrust.
  • Alert teams when related policies or configurations appear outdated, incomplete, or inconsistent across systems.

• Audit readiness assistants
  
  • Help teams prepare for audits by compiling relevant artifacts — like access logs, certifications, or past audit responses — on request.
  • Enable quick answers to auditor questions like, “When did we last perform a penetration test?” or “Where are our vendor risk assessments stored?”
  • Auto-generate summaries of compliance status by standard (e.g., SOC 2, ISO 27001, HIPAA), pulling data from fragmented sources into a clean report.

2. Copiloting and rapid prototyping – Help your organization and users alike use tools more efficiently.

• Engineering copilots
  
  • Help engineers generate boilerplate or scaffolding code that matches your team’s patterns.
  • Allow developers to ask, “How do we handle authentication in this service?” and receive code snippets pulled from internal repositories, documentation, or previous pull requests.
  • Use RAG to auto-surface best practices or internal utility functions when a developer is working on a new feature, which also reduces ramp-up time for new team members.
  • Streamline onboarding for engineers by connecting them to recent examples of similar code changes, key architecture decisions, or system design docs relevant to the service they’re working on.

• Automated onboarding copilots
  
  • Show new users how to complete setup steps based on their role, previous usage, or integrations they’ve activated.
  • Use RAG to answer setup questions with links to relevant documentation, rather than static walkthroughs.
  • Reduce churn by guiding users to intelligent recommendations based on peers’ successful paths.

3. Learning and personalization – Automatically create unique experiences on the basis of past behavior and known attributes. 

• Smart sales coaching
  
  • During a call, suggest talking points based on the prospect’s previous interactions, industry, or open opportunities from the CRM.
  • After a meeting, generate a summary with suggested next steps and relevant assets from enablement libraries.
  • Train new reps faster by allowing them to ask questions like “What’s the best way to handle a pricing objection for Enterprise clients?” and retrieve playbooks plus recent deal examples.

• Personalized marketing collateral
  
  • Automatically create one-pagers or pitch decks that include the customer’s logo, industry-specific messaging, and relevant case studies based on their region or vertical.
  • Generate email copy tailored to a prospect’s recent product usage, trial behavior, or interaction history, drawn from synced data in your CRM and marketing automation tools.
  • Let marketers ask, “Do we have a case study for a healthcare company using our analytics suite?” and retrieve both internal summaries and published assets.
  • Surface the most relevant blog posts, product features, or ROI stats based on the customer's persona — for example, pulling different proof points for a CFO versus a technical lead.

In every case, Fivetran ensures the data powering RAG is accurate, secure, and always up to date — turning a technically complex capability into a reliable, scalable product feature.

Powering AI features with RAG + Fivetran

For companies embedding AI into their products, RAG offers a powerful path forward, especially when paired with automated data integration. Imagine a SaaS platform that helps enterprises manage knowledge across their organization. With Fivetran continuously syncing the customer’s CRM data, help desk tickets, document libraries, and system logs into a vector store, the platform can offer cutting-edge AI features like natural language search, smart summarization, and contextual Q&A — all based on the customer’s private data.

Our Powered by Fivetran feature set allows you to easily collect credentials from end-users and create Fivetran data pipelines programmatically. You don’t need a team of data engineers building and maintaining fragile connectors or writing one-off scripts. You also don’t need account managers manually creating accounts and assigning permissions. In short, Fivetran plus RAG gives companies the infrastructure to offer differentiated, intelligent features without the operational burden.

Without Fivetran, building AI-powered applications often means managing a mess of manual or engineering-intensive ETL processes, fragile APIs, and inconsistent data sources. With Fivetran’s embedded data pipelines — known as Powered by Fivetran — we handle all the integration complexity for you. This allows your team to concentrate on building innovative user experiences, not back-end data infrastructure.

Here’s how it works:

• Customer onboarding made simple: To ingest data from a customer’s system (like Salesforce, Zendesk, or Google Drive), you first need access to their data source.
  
  
• Secure credential exchange: Based on the Fivetran REST API, Powered by Fivetran brokers the secure exchange of sensitive login credentials between you and your customer.
  
  
• Fully managed syncs: Once access is granted, Fivetran automatically creates and manages the pipelines, moving data securely and accurately from the customer’s system into your backend, making it ready for retrieval.

This embedded approach ensures a seamless, secure, and programmatically scalable data exchange — the perfect foundation for powering RAG features in any modern SaaS product.

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