Agnostic
An AI agent that assists the Investor Relations team (and contributing departments) by automatically gathering, validating, and consolidating specific quantitative and qualitative data points required for periodic regulatory filings (e.g., 10-K, 10-Q, 20-F, Proxy Statement sections, Sustainability Reports).
This use case solution follows these general steps at a high level:
The agent is a versatile data aggregator that uses the best connection method for each source system:
Direct System Integration: It uses secure APIs and database connectors to pull structured data directly from enterprise systems like SAP, Oracle, Salesforce, and Workday.
Intelligent Document Processing: It can extract specific, targeted data points from designated PDFs or spreadsheets stored in secure locations like SharePoint or network drives.
The agent can be configured to perform several types of validation:
Reconciliation: Tying numbers back to a control total (e.g., ensuring the sum of segment revenues equals the total revenue reported in the ERP).
Variance Analysis: Flagging any data point that has changed by more than a predefined percentage from the prior period or budget, highlighting it for human investigation.
Completeness Checks: Ensuring that all required data points for a specific table or disclosure have been successfully retrieved.
Yes. Instead of just dumping raw data into a folder, the agent can be customized and configured to populate your team’s existing, pre-formatted templates in Microsoft Word or Excel.
When the agent encounters an issue, it generates a clear, actionable exception report. This report details:
Missing Data: Which required data points could not be found.
Failed Validations: Which data points were found but failed a check (e.g., a variance was too large).
Source/Access Issues: Any systems the agent was unable to connect to.
The customization and configuration typically take 2-3 weeks. It involves:
Digitize your filing checklist, identifying every required data point.
Map each data point to its source system and specific location (e.g., report name, table, cell).
Define the validation rules and variance thresholds.
Configure the output templates.
Adding a new data requirement is a simple customization and configuration change. Your team can add the new metric to the data checklist, map it to its source, and define its validation rules. With minor customization, the agent will then include this new data point in its next run.
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