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
How it works
A user asks a finance question in Slack, which a Python service routes to a custom GPT orchestrator. The orchestrator assigns either a forecast or actuals sub-agent to gather and consolidate the relevant data, calculate variances, and draft driver explanations; the primary agent then validates and formats the result before returning it to Slack.
Why the architecture is not shown
This solution was developed within a professional environment, so production screenshots and detailed implementation artifacts are intentionally omitted to protect confidential systems, data, and workflows.
