A faster, more accurate way to explore innovation data—now available in Cypris.

For innovation teams, speed and accuracy aren’t optional—they’re critical. You need to quickly find all relevant documents, slice and dice datasets however you want, and trust that the results are complete and representative. With this in mind, we’ve upgraded how semantic search works inside Cypris.

Today, we’re launching an upgraded search infrastructure that gives users access to full, exact result sets—unlocking more powerful analysis, faster iteration, and deterministic filtering and charting.

Unlike traditional semantic or vector search engines—which make it difficult to count, filter, or chart large sets of matched documents—our new approach prioritizes transparency and performance while preserving semantic relevance.

Why we moved away from vector search

Our original implementation relied on semantic and vector search to capture the “meaning” behind user queries. But as our platform evolved, it became clear that these systems weren’t well-suited for our core use cases.

Users needed:

• Deterministic filtering (e.g., "how many results match this atom?")
  
  
• Transparent, complete result sets to power charts and dashboards
  
  
• Fast, repeatable queries that don’t change subtly over time
  
  

Modern vector search systems don’t easily support this level of transparency. They return approximate matches and abstract similarity scores, often making it hard to understand why a document was returned—or whether it’s the full picture.

So we made a decision: move away from vector search and lean into what traditional search engines do best.

A return to boolean and lexical search—with a twist

We rebuilt our search infrastructure on top of Elasticsearch’s powerful boolean and lexical search capabilities. This shift brings major advantages:

• Faster query speeds that dramatically improve iteration time
  
  
• Deterministic filtering and counts, so every chart is grounded in the full dataset
  
  
• Predictable, explainable results that users can trust
  
  

But we didn’t stop there.

To preserve the benefits of semantic understanding, we’ve rethought where that intelligence should live—not at query time, but at data ingestion.

Capturing semantic meaning at ingest time

Instead of computing document-query similarity during search, we enrich documents at the time of ingestion. Here’s how:

• Synonym expansion: We find related words and concepts not explicitly mentioned in the document and add them as fields, enabling semantic-style recall via lexical search.
  
  
• Stemming: Both queries and documents are reduced to their root forms, allowing consistent matches (e.g., “running” and “run”).
  
  

The result? You get the same functionality—semantically relevant results—without the opacity or latency tradeoffs of vector search.

What’s next: Reranking for even better relevance

We’re not done. Coming soon to Cypris is a reranking layer that boosts the most relevant results to the top of the list using lightweight vector techniques.

Here’s how it works:

1. A standard lexical search retrieves the full result set.
   
   
2. We take the top N results and rerank them using vector similarity, powered by Elasticsearch’s new hybrid scoring capabilities.
   
   
3. You get faster queries with even better relevance—without compromising on counts or transparency.
   
   

This layered approach gives us the best of both worlds: precise filtering and fast queries, plus smarter ordering of results where it matters most.

We’re excited to bring this upgrade to our users, and we’re already seeing teams iterate faster and uncover insights more confidently. This is a foundational shift—and just the beginning of what’s to come.

Want a walkthrough of what’s changed? Reach out to our team.

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