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.

Query normalized entities, filings, holdings, funds, securities, people, offerings, and adviser documents.

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.

Evidence included

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.

Source and review. The linked datasets identify their official sources. See the methodology and editorial policy for review and correction standards.

Continue the research

Use the same records across web, API, MCP, and data feeds.