Reduce manual back-and-forth for common BvA and forecast questions.
Professional project | Sanitized case study
AI-Powered FP&A Slack Agent
An internal finance agent that gives business partners faster access to budget-versus-actual and forecast analysis while preserving supporting finance logic.
Translate a business question into calculated metrics, key drivers, and a supported explanation.
The approved public resume describes more than two hours of daily time savings.
Case study overview
This project is presented at a capability level because it was built within an employer environment. The public case study focuses on the business problem, user experience, finance logic, and measurable value.
What the agent does
- Accepts recurring budget-versus-actual and forecast questions in Slack
- Calculates the requested finance metrics
- Surfaces key drivers and supporting logic
- Returns the answer in the workflow already used by business partners
My contribution
Defined the finance use cases, translated business questions into requirements, tested responses against known finance outcomes, refined the workflow, and supported deployment for cross-functional use.
Why the architecture is not shown
Production screenshots, prompts, source systems, internal data, authentication, model details, and implementation architecture are intentionally omitted. The public page does not substitute a fictional EPM architecture for the real system.
