Data systems and AI that do a job
Organizations sit on data that almost works. Names spelled four ways. Schools that changed districts. Records in a spreadsheet, a database, and someone's inbox. The work is joining it, trusting it, and making it answer questions.
What we build
- Ingestion and cleaning
- Pulling data out of files, feeds, APIs, and websites, then normalizing it into something consistent. Including the sources that arrive as a PDF every quarter.
- Record matching and entity resolution
- Deciding that these records describe the same person, school, or organization. This is harder than it sounds, and it decides whether everything downstream works.
- Warehouses and reporting
- A place your data lands, organized so a question takes a minute rather than a project. Dashboards for the numbers people check weekly.
- Search and ranking
- Search that returns the right result for the way your users phrase things, with filters and ordering that match how they decide.
- AI features
- Classification, extraction, summarization, and retrieval against your own content. We build features with a defined input, a defined output, and a way to check whether they're right. We don't ship a chat box and call it a strategy.
Where we've done this
ScholarComp matches school and student records across thousands of competition sources that agree on nothing, then rates results across competitions that were never designed to be compared. Read how the matching and rating systems work.
Tell us what you need.
Send a paragraph about your organization and the problem in front of you. We'll tell you whether we're the right shop, what we'd build, and what it costs. If we're not the right shop, we'll say so and point you somewhere better.