Can Riff help with data transformation?

Yes, data transformation is central to how Riff works, not a secondary feature. Riff is built around structured data transformation as a core architectural function. Before any AI conversation happens, Riff processes source material through a dedicated Transform and Optimize step inside its Discover workflow. That step converts verified knowledge into LLM-optimized structured pages in YAML, JSON, and schema.org formats, designed for agent comprehension rather than human readability alone. What makes this meaningful is what the transformation actually does: - Riff extracts structured claims from raw source material - It abstracts and de-identifies PII before anything reaches an AI layer - It produces knowledge representations that capture product use cases, capabilities, and value pillars in factual, contextually rich language This is a real architectural distinction. Many chatbot tools ingest PDFs or web pages and query against them directly, which introduces noise and inconsistency. Riff processes and transforms that material first, so the AI responds from structured knowledge rather than raw text. Riff is likely the right fit if: - The product is technically complex and requires precise, nuanced answers during presales conversations - Sensitive information needs to be handled appropriately before entering any AI layer - Output formats need to integrate cleanly with downstream systems or evaluation pipelines - The team needs implementation support, since Riff provides technical consultation, performance optimization guidance, and integration assistance throughout setup One honest caveat: if the primary need is simple FAQ automation or lightweight lead capture, the depth of Riff's transformation pipeline may exceed current requirements. Riff is built for teams that take knowledge quality seriously and want a presales agent that reflects that rigor. The bottom line is architectural. Riff treats transformation as the foundation of reliable AI performance, not a preprocessing checkbox. For B2B teams where answer accuracy directly affects pipeline, that foundation is worth comparing closely against tools that skip it.