CONTENT INTELLIGENCE

Content intelligence is Vertesia's foundational platform capability that prepares enterprise content for AI. It automatically transforms raw documents, images, audio, and video into clean, structured, AI-ready material at the point of intake. It covers the full lifecycle: how content is prepared, enriched, indexed, governed, and retrieved. It works beneath RAG, agents, and every AI-powered workflow on the platform.

Document intelligence focuses on extracting data from individual documents: reading a contract, pulling fields from a form, classifying a single file. It treats each document as its own task.

Content intelligence goes further. It treats your entire content ecosystem (documents, images, audio, video, and data across all your systems) as a single, connected knowledge base that AI can access, understand, and act on. Where document intelligence processes one file at a time, content intelligence manages the full lifecycle at scale: intake, enrichment, indexing, retrieval, governance, and insight generation across your whole content estate.

Think of it this way: document intelligence reads a document. Content intelligence makes every document in your organization work for AI, including the ones created years before you deployed AI.

Vertesia delivers both. Semantic DocPrep handles document-level preparation. The broader content intelligence platform handles everything that happens before and after, from intake to retrieval to organizational insights.

AI models can read text, but they struggle with structure. When a PDF is converted to plain text, it loses the layout, tables, headings, and relationships that carry most of its meaning. That leads to hallucinations, missed context, and poor AI performance. Vertesia preserves that structure through Semantic Document Preparation before the content ever reaches a model.

Semantic Document Preparation (Semantic DocPrep) is Vertesia's patent-pending document preparation technology. It preserves tables, clauses, section hierarchies, and attachments so AI agents can read and reason over content accurately, not just display it for humans.

Vertesia supports PDFs, Microsoft Word, PowerPoint, Excel, scanned forms, images, audio files, and video. Audio and video are converted to timestamped transcripts with speaker labels, making them fully searchable and usable by AI agents.

Vertesia builds three types of indexes for every piece of content: a vector (semantic) index, a structured field index, and a full-text index. When an AI agent asks a question, it searches all three at once. This hybrid retrieval approach returns precise, relevant answers, not a long list of loosely related documents to manually sort through.

Agentic RAG (Retrieval-Augmented Generation) grounds AI responses in your actual documents rather than general training data. Vertesia combines semantic, full-text, and structured search in one query, with permission enforcement built in at retrieval time.

Yes. Vertesia can process and enrich historical content that predates your AI deployment. Existing documents can be run through the content intelligence pipeline to extract metadata, generate summaries, create embeddings, and add semantic structure without migrating or replacing your current systems.

Absolutely. Permissions, lineage, redaction, and retention policies are enforced at intake and at every retrieval query. Content is governed throughout its full lifecycle on the Vertesia platform, meeting the requirements of regulated industries including financial services, healthcare, and legal.