CONTENT INTELLIGENCE

Raw documents lose structure when converted to plain text. Tables collapse. Headers detach from their content. Agents read the broken version and fill the gaps with inaccurate answers. Content intelligence fixes the input before agents ever see it.

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.

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.

All documents are indexed the same way regardless of age. Institutional knowledge from years or decades ago becomes just as findable as content created today. No document gets left behind.

Yes. Documents are processed inside your environment under your governance rules. Nothing is shared with third-party AI training pipelines. Permissions are enforced at every stage: preparation, indexing, and retrieval.

Embeddings are mathematical representations of text as vectors (lists of numbers) that capture semantic meaning. Similar concepts have similar embeddings, enabling comparison and searching based on meaning rather than exact text matching.

A vector store is an index. It is not a content layer. Many platforms call themselves "AI-ready" because they store embeddings. That is not enough. Real enterprise content needs structure, permissions, history, lineage, governance, audit, and intelligence. Content Intelligence brings those pieces together so AI sees the right content, at the right time, under the right rules.

A knowledge graph is a structured database that represents information as interconnected nodes (entities) and edges (relationships), enabling complex queries and reasoning about relationships between concepts.

Vector search is a search technique that finds similar items by comparing their embeddings in vector space, enabling semantic similarity searches rather than keyword-based matching.

Semantic chunking is the process of dividing text into meaningful segments (chunks) based on semantic content and meaning rather than fixed size, ensuring related information stays together.

An AI hallucination is when an AI model generates false, inaccurate, or fabricated information that sounds plausible but isn't true or grounded in its training data.

Yes! With Vertesia, an agent can:

• Identify similar contracts your team has successfully negotiated in the past.
• Flag terms that have been problematic in previous deals.
• Suggest alternate terms based on what you have agreed to before.
• Show typical discounting rates, concessions, and other commercial terms from similar deals.

The same approach works for claims, applications, policies, case files, and any other content-heavy work. Your enterprise content becomes a source of operational intelligence, not just a storage problem.