Our work
Bringing data together
Your critical systems were built at different times for different purposes. We design the schema that brings their data together and gives it context.
Our approach
Large organizations rarely run on a single critical system; they run on many. Some connect to other systems, and some don't connect at all. Underneath them all is the data. Some of it lives in databases. Much of it lives in documents and media with no structure at all. The structured data was shaped when its system was first implemented, before AI and enterprise search brought new requirements for how data is understood and used. Giving the data context, so search can find it and AI can use it, takes a deep understanding of the data's patterns and the purposes of the business. That understanding is captured in a schema. Turning it into the right schema is a rare skill. Getting it wrong sets off a snowball of problems across the enterprise.
Unstructured content needs its key metadata extracted, so it can be found and used in context. Our tools extract that metadata into a specialized schema designed to complement your structured data. A unified schema brings structured and unstructured content together. That's the context search and AI need.
This is where three decades of experience matter. We design one coherent data schema that respects what makes each collection of content distinct and is flexible enough to meet every demand of enterprise search and AI, while always remaining efficient.
Two examples among many
Tailored to every customer.
The same work runs through everything we do. It's where SureSoft began, in the consulting work for our first customers. Since then we've designed schemas for the private and public sectors, for technology companies, service industries, healthcare, energy and the scientific community, and many more. Each one is tailored to the needs of the customer it serves. Here are two of the larger examples.
More than 1,000 organizations, one repository.
Each organization had its own format and its own vocabulary. We built one model that took in all of it, lost none of it, and removed the redundancy between them while leaving room for what made each organization specialized.
A century and a half of records, one unified model.
An organization that old holds content of every type: for its operations, for its people, and for its purposes. Most of it had no structure at all. We designed a schema specific to each kind of content, and unified for search.
What these have in common
Neither started with a technology choice. One brought a thousand different schemas, the other none at all. In both, our work started with designing the schema: built with the content's history in mind, allowing for flexibility, and keeping all of it unified for enterprise search and AI.
Unified, not uniform.
Have data that needs to work as one?
Whatever its size or complexity, it's worth a conversation. Tell us about your systems and what you need to find. We'll tell you how we'd bring it together.