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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:
A standard lexical search retrieves the full result set.
We take the top N results and rerank them using vector similarity, powered by Elasticsearch’s new hybrid scoring capabilities.
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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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:
A standard lexical search retrieves the full result set.
We take the top N results and rerank them using vector similarity, powered by Elasticsearch’s new hybrid scoring capabilities.
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.
Keep Reading
December 27, 2022
•
XX
min read
R&D is an ever-evolving process that has recently seen a shift toward the application of computer science in research and development. By leveraging computer science, teams are able to unlock new insights from data faster than ever before. From predictive analytics to artificial intelligence, these technologies have revolutionized how R&D teams can develop products more efficiently while staying ahead of their competitors.
In this blog post, we will explore the application of computer science in research and development as well as discuss some examples, benefits, and challenges associated with its use.
Overview of Computer Science in Research and Development
Computer science is the study of algorithms and data structures that enable computers to solve problems. It involves creating algorithms that can be used by machines or programs to complete tasks efficiently and accurately. This includes developing software applications for specific purposes such as machine learning (ML), artificial intelligence (AI), natural language processing (NLP), image recognition, and robotics.
The application of computer science in research and development has become increasingly important due to its ability to help teams quickly analyze large amounts of data, automate processes, and uncover insights faster than ever before.
Benefits of Computer Science in R&D
The application of computer science in research and development provides numerous benefits.
Increased efficiency in analysis.
Improved accuracy.
Faster decision-making.
Better collaboration between team members.
Enhanced security measures.
Cost savings through automation.
Access to real-time insights into customer behavior patterns.
Improved customer experience through personalized services.
More accurate predictions based on historical trends and more reliable forecasting models.
Additionally, computer science helps organizations gain a competitive advantage by providing them with the ability to develop innovative products at a faster rate than their competitors while also reducing costs associated with product development cycles.
Challenges of Computer Science in R&D
While there are many advantages associated with the application of computer science in research and development, there are also some challenges that need to be taken into consideration. These include:
Computer science has revolutionized the way research and development (R&D) teams work. With its powerful tools, computer science enables R&D teams to achieve greater efficiency and productivity in their projects.
Increased Efficiency and Productivity
Computer science helps R&D teams become more efficient by automating mundane tasks such as data collection, analysis, and reporting. This allows them to focus on the creative aspects of their projects instead of spending time on tedious manual processes.
Additionally, computer science provides access to a wide range of software that can be used to improve workflow management and project tracking which leads to increased productivity across the board.
Improved Accuracy and Quality Control
Computer science also offers improved accuracy when it comes to data collection, analysis, and reporting due to its ability to quickly process large amounts of information with minimal errors or omissions. This makes it easier for R&D teams to identify potential problems before they arise which improves quality control throughout the entire product lifecycle from concept through commercialization stages.
Reduced Costs and Time-to-Market
Finally, utilizing computer science in R&D projects reduces costs associated with labor-intensive activities like data entry or manual testing procedures. It also speeds up production times so products are able to reach the market faster.
Key Takeaway: Investing in computer science for your R&D team is an invaluable asset that will provide long-term benefits. It can increase efficiency and productivity, improve accuracy and quality control, reduce costs, and shorten time-to-market – all of which are essential to successful innovation outcomes.
5 Trends in Computer Science Research
Artificial Intelligence: AI is revolutionizing the way we interact with computers and machines, enabling them to understand complex tasks and make decisions without human input. AI technologies are being used in a variety of industries, from healthcare to finance, to improve efficiency and accuracy while reducing costs.
Machine Learning: Machine learning is an application of artificial intelligence that allows computers to learn from data without explicit programming instructions. It can be used for predictive analytics, natural language processing, image recognition, facial recognition, and more. With machine learning technology becoming increasingly accessible through cloud computing platforms, it’s no wonder why this trend has been gaining so much traction lately!
Big Data: The term “big data” refers to large sets of structured or unstructured data that require advanced tools for analysis and storage capabilities beyond traditional databases or spreadsheets. Companies use big data analytics solutions such as Hadoop or Spark for a wide range of applications including customer segmentation, fraud detection, and market forecasting among others – all powered by computer science research breakthroughs!
Internet Of Things: IoT is the network of physical objects embedded with sensors connected via internet protocols which enable them to collect real-time information about their environment as well as communicate with other devices on the same network. From smart homes to autonomous vehicles – there are endless possibilities when it comes to leveraging this technology in our everyday lives!
Cyber Security: As digital systems become increasingly interconnected across multiple networks worldwide, cyber security becomes even more important than ever! Computer scientists have been working hard at developing new methods for protecting sensitive information against malicious attacks such as malware and ransomware threats which can cause serious damage if left unchecked!
Conclusion
The application of computer science in research and development enables teams to access data sources more easily, analyze large datasets faster, and develop new products or services with greater efficiency. While there are challenges such as data security concerns and the need for specialized skill sets, the benefits far outweigh any potential drawbacks.
Are you an R&D or innovation team looking for a research platform that will provide rapid time to insights? Look no further than Cypris! Our platform centralizes all of your data sources into one easy-to-use interface, making it easier and faster to get the answers you need.
Sign up now and start getting results in record time!
Application of Computer Science in Research and Development
Blogs
December 23, 2022
•
XX
min read
How Cypris Sources, Validates, & Interprets Data to Drive Innovation
