Our system is designed to process this diverse information and create a single, reliable knowledge source that our AI can use. In this ‘Deep tech story’ we dive deeper into the tech that makes this possible.

First up: Data ingestion

1. Reads multiple formats We can process everything from Word documents to PDFs and scanned images.
2. Understands complex documents Using advanced techniques (like Vision-Language Models and OCR), we capture important details like tables, footnotes, and even handwritten notes and graphs.
3. Smart organisation We break down content into meaningful sections while keeping track of page numbers and headings (contextual chunking).
4. Connects the dots Our system maps out the structure within documents and understands how different documents relate to each other across your organisation (hierarchical maps).

The result is a complete and accurate digital library of your company's knowledge, ready for analysis and audits.

To extract relevant data, AI needs the right context. This is often achieved through a technique called Retrieval-Augmented Generation (RAG), which feeds relevant information to the AI before it generates a response.

However, finding the most relevant information isn't a novel task. Different questions require different retrieval approaches. A single retrieval method can't guarantee accuracy, completeness, relevance, and reliability all at once. That's why we uses a combination of four specialised retrieval engines, automatically choosing the best one for each query.

How these engines work together:

• Precision vector search: Ideal for quickly finding precise facts or clauses. It maps information based on meaning for fast lookups.
• RAPTOR for recall & reasoning: Builds summaries and links related topics across your entire knowledge base. Perfect for spotting connections or conflicts between different documents.
• Signal-aware reranking: Filters search results to ensure the information provided directly addresses the user's specific need or question.
• Prompt engineering for robustness: Breaks down complex questions, runs multiple checks, and uses an internal critique process to verify answers, ensuring they are reliable even if a question is phrased differently.

Example use case: Finding contract contradictions

Imagine this scenario:

1. Upload: A legal analyst uploads a 120-page supplier contract and two related policy documents into Hybridity.
2. Ingest: Our system processes the documents, extracting over 3,800 pieces of information, reading scanned signatures, and indexing everything.
3. Query: The analyst asks, "Highlight any clauses that contradict each other or our standard variation-margin template."
4. Route: Hybridity determines that the RAPTOR engine (for completeness) and Vector Search (for specific clauses) are the best tools for this task.
5. Generate: The AI receives the relevant information snippets, drafts an answer, and our critique agent double-checks any numerical details against the documents.
6. Deliver: Within 5 seconds, the analyst receives a clear, bulleted report identifying potential contradictions, complete with references to the exact lines in the documents.

All together now: A system ready for precision workflows

Hybridity's approach offers significant advantages, especially in sensitive or regulated fields:

• Audit-grade provenance: You can trace every piece of information in an AI-generated response directly back to the original source document and page number.
• Regulatory confidence: Our system is designed with the requirements of regulations like DORA, GDPR, and NIS2 in mind, ensuring retrieval and checks meet these standards.
• Operational velocity: Fast response times (typically under 5 seconds) mean your teams can get the information they need without breaking their workflow or resorting to slow manual searches.
• Future-proofing: Our modular design allows us to easily add new capabilities or adapt to changing requirements without disrupting the core system.

Using this advanced tech stack Hybridity transforms the promise of generative AI into reliable, production-ready performance for the most demanding, regulated environments.