Query normalized entities, filings, holdings, funds, securities, people, offerings, and adviser documents.
For data and engineering teams
Automate monitored datasets with provenance.
Replace one-off filing pipelines with documented records, stable joins, explicit dates, and delivery that fits your stack.
What it makes possible
Move from public records to a usable research result.
Use native identifiers and documented relationship keys instead of name-only joins.
Receive incremental changes or full snapshots through API and managed data feeds.
Carry source URLs, report periods, accepted dates, amendment states, and coverage metadata downstream.
A source-aware workflow
Keep the evidence with the answer.
AUMSearch connects distinct public records without pretending they are interchangeable. Each step preserves the identifiers, dates, filing versions, and source links needed to review the result.
Choose the interface
Use the API for bounded queries, MCP for controlled research, or files for warehouse delivery.
Model dates and keys
Keep filing, report, effective, accepted, and publication dates distinct in downstream tables.
Reconcile each refresh
Use manifests, cursors, source status, and stable IDs to check every load.
The context a reviewer needs.
- REST resources with cursor pagination and documented filters
- Full and incremental files through SFTP, Amazon S3, or Azure Blob Storage
- Coverage, freshness, source, and amendment metadata
- Web and MCP access for review; coverage gaps must stay distinct from a confirmed zero
Data behind the workflow
Start with the records that answer the question.
Review every dataset, field family, source, time basis, and supported research path.
Explore the dataData-source methodologySee official sources, native keys, provenance fields, and publication controls.
Explore the dataSource and review. The linked datasets identify their official sources. See the methodology and editorial policy for review and correction standards.
Continue the research