We are experienced in providing entity resolution solutions for data matching, duplicate detection and record linking using the power of LLMs and VectorLink to harmonize text-based and unstructured data.
VectorLink is a vector database and toolset that provides the mechanisms to utilise LLMs with your data.
One such use is entity resolution for data matching, duplicate detection and record linking. Using vector embeddings, VectorLink simplifies the challenging task of entity resolution.
Traditional search methods struggle to capture the nuances of unstructured and text-based data. By transforming records into natural language text and utilizing an LLM’s embeddings, VectorLink provides the ability to reconcile records at scale.
We combine GraphQL queries and Handlebars templates to enable VectorLink to generate high-quality embeddings ensuring good semantic results. Results are stored in a vector database with an index based on Hierarchical Navigable Small World graphs – a top-performing index for vector similarity search.
Working with large datasets, often unstructured and text-based, we help clients unravel complicated record linkage problems using our skills and VectorLink. These include:
Providing a full picture with data can be a challenge. Using VectorLink’s semantic index for proximity search, we can quickly perform data matching and record linkage to provide a better understanding of the subject.
If you’ve ever had multiple phone numbers for the same contact in your phone, you know the issues surrounding duplication detection. Scale that to thousands and millions of records and it becomes a big problem. The ability to search vectors in close proximity makes it easier and faster to detect duplicates and take action
In addition to data matching and duplication detection, similarity search plays a critical role in entity resolution. By leveraging vector embeddings, we can search for records that are not exact matches but are similar in context or content.
We use vector embeddings, AI techniques, and deep learning to provide data matching for structured and unstructured data, our key features include:
Utilize vector embeddings to perform accurate and efficient semantic searches
Your indexed data gets stored as vectors with the ability to search vectors in close proximity.
Combine GraphQL and Handlebars templates to generate high-quality vector embeddings.
Define embeddings effortlessly with our intuitive approach opening it up for non-technical users.
Powered by a Hierarchical Navigable Small World graph ensures quick retrieval over large datasets with high recall.
Merge and consolidate customer data from multiple sources, improving customer profiles, segmentation, and personalized marketing
Identify and link related entities across transactions or accounts, enabling early detection of fraudulent activities in financial transactions
Reconcile patient records from different healthcare systems, ensuring accurate medical histories, care coordination, and improved patient outcomes
Consolidate and match supplier data, optimizing procurement processes and inventory management in complex supply chains
Identify connections between individuals, organizations, and events by linking data from various sources, aiding in investigations, detecting criminal networks, and supporting evidence-based decision-making
Enhance financial analysis by resolving entities across diverse data sources, facilitating accurate risk assessment, portfolio management, and compliance monitoring
If you need to leverage the potential of combining your data with generative AI to solve difficult problems, our AI consultancy services can speed up the project. Enquire today to see about working with us.
We are AI consultants who combine our vast data knowledge with VectorLink to incorporate your structured and unstructured data with LLMs to solve challenging problems quickly.