Data systems and AI that do a job
Organizations sit on data that almost works. Names appear four ways. Records live in a spreadsheet, a database, a PDF, and someone's inbox. Reports require a person who knows where the exceptions are hidden.
What we build
- Ingestion and cleaning
- Files, feeds, APIs, websites, and recurring documents become consistent records with source history and clear handling for failures.
- Record matching
- The system determines when different records describe the same person, school, organization, or program. Confidence scoring and human review handle the cases that should not be guessed.
- Warehouses, reporting, and search
- Data lands in structures designed for questions, reporting, and retrieval. Search reflects how users describe what they need. Dashboards focus on decisions people make regularly.
- AI features
- Classification, extraction, summarization, and retrieval against the organization's own information. Each feature has a defined input, expected output, and evaluation that shows whether it works.
Where we have done this
ScholarComp resolves school and organization records across thousands of competition sources and rates performance across competitions that were never designed to work together. Read the case study.
Turn scattered records into a system people can use.
Describe your organization and the problem. We will come back with what we would build and what it costs.