AI performance hinges on data quality and completeness. Yet between 80% and 90% of an organization’s data is unstructured — locked away in PDFs, images, text files, and audio formats that most pipelines overlook. Fivetran’s support for unstructured file replication brings that vast, untapped data into the fold, making multimodal, enterprise-wide data truly AI-ready.

For over a decade, Fivetran has been the backbone of modern data movement, enabling reliable, automated replication of structured and semi-structured data across 700+ prebuilt connectors. Now, we extend that same enterprise-grade automation and reliability to unstructured data, ensuring no data source is left behind — no matter the format or origin.

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Why unstructured data matters for AI, RAG, and LLM accuracy 

AI agents, retrieval-augmented generation (RAG) applications, and large language models (LLMs) rely on contextual depth to produce accurate, trustworthy responses. Structured data (tables, logs, metrics) provides clarity. But unstructured data such as contracts, call transcripts, manuals, PDFs, and other media provide nuance, intention, and meaning.

Unlocking unstructured content significantly expands the breadth and depth of enterprise knowledge accessible to AI systems. Fivetran’s support for unstructured data is a foundational capability that ensures all relevant signals can be brought to the surface, enhancing both:

• Utility: More use cases become possible when AI has access to a broader corpus of knowledge.
• Accuracy and trust: Outputs are improved when models have access to original source context.

Integrate structured and unstructured data for multimodal AI 

With this enhanced support for multimodal data sources, Fivetran is the most comprehensive multimodal data movement platform that handles:

• Structured and semi-structured data from databases, SaaS apps, APIs, and data warehouses.
• Unstructured data from file repositories like SFTP, SharePoint, Google Drive, and Box, as well as conversation transcripts and customer interactions.
• Niche and custom sources via the Fivetran Connector SDK, enabling integration for specialized use cases with the same automation and governance as standard connectors.

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This breadth of support ensures that enterprise AI initiatives are not held back by data silos, format limitations, or one-off integrations. Every dataset — no matter how obscure or unstructured — becomes part of your AI’s knowledge base.

Ingest unstructured data at scale for AI applications 

Fivetran’s fully managed pipeline architecture enables teams to operationalize unstructured data ingestion with zero manual maintenance. One key capability is automatic change detection and incremental updates, which we accomplish by storing the metadata, source URL, and location reference using a catalog.

With timely access to unstructured data, your team will be able to pursue any number of valuable AI use cases, such as:

• Internal chatbots – An LLM augmented with all of your data can become the most knowledgeable entity in your organization, allowing people to get accurate answers on the cheap without bothering colleagues.
• Enriching machine learning projects of all kinds – Generative AI can be combined with conventional machine learning, automating tasks such as labelling or transforming data, translating quantitative findings into qualitative guidance, and more.
• Engineering copilots – By augmenting an LLM with your code base, you can radically boost the productivity of your engineers by enforcing style, standards, and best practices, and obviating the need to write boilerplate code manually.
• Personalized sales and marketing content – Unstructured, qualitative data from interactions with prospects and customers offers the opportunity to tailor your sales and marketing efforts to specific audiences. 

Improve AI accuracy by integrating unstructured data with Fivetran 

As organizations build RAG applications, internal copilots, and autonomous agents, the reality is clear: data accessibility determines intelligence. Fivetran’s support for unstructured file replication removes a major blind spot.

Whether it's a product manual that improves a support chatbot, a policy handbook that strengthens an HR assistant, or a case file that enhances a legal AI advisor, the more complete your data foundation, the more capable your AI becomes.

Improve your AI with unstructured data and fast-track development with pre-built code templates for popular AI use cases.

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