How to build a Spreadsheet AI Assistant

TL;DR

• What it does: Answers spreadsheet questions in seconds, with cell-level citations.

• Who it’s for: Business and ops teams that need quick numbers without formulas or BI tickets.

• Time to value: ~20–30 minutes to configure; seconds per question thereafter.

• Output: Clean text or tables (markdown/CSV/PDF) citing file › sheet › cell for verification.

Common Pain Points of Manually Analyzing Spreadsheets

• Hunting across tabs, filters, and pivot tables to find a single number.

• Inconsistent definitions (MRR, bookings, ARR) across teams.

• Manual copy/paste into emails or slides with no source trail.

• BI/analyst bottlenecks for ad-hoc questions.

What the Agent Delivers

• Plain-language Q&A over one or many spreadsheets.

• Cell-level citations (e.g., Budget.xlsx › Sheet: Budget 2025 › C14).

• Structured outputs: totals, tables, and short narratives ready to share.

• Context fusion with internal docs so metrics are interpreted correctly.

• Optional exports to PDF/CSV and notifications to Slack/Email.

Step-by-Step Build (StackAI Nodes)

1) Ask a Business Question (Input)

What it does: Captures a plain-language question (e.g., “What is 2025 MRR by month in the budget file?”).

Goal: Define the query the agent must answer.

Tips: Add helper text with examples and preferred file names.

2) Enrich Answers with Internal Documentation (Documents / Knowledge Base) – optional

What it does: Loads metric definitions, assumptions, and naming conventions.

Goal: Keep terms consistent (e.g., MRR vs. ARR, gross vs. net).

Settings: Chunk ~2,000–2,500 tokens, overlap 300–500; advanced extraction ON for PDFs.

3) LLM: Interpret Data and Build the Answer

What it does: Scans sheets, computes the requested values, and auto-cites the exact cells used.

Goal: Return a concise answer + table with citations.

Model: GPT-5 (primary) or Claude 3 Opus (long context).

Instructions

You are an AI assistant specialized in financial data analysis.

You will receive structured data from spreadsheets and internal documentation to answer business questions.

Your task is to:

Interpret the provided table data.

Enrich responses with financial context from StackAI documents.

Provide a clear, cited answer—mentioning sheet names and cell references where applicable

Prompt

<AnalyzeQuestion>

{in-0}

</AnalyzeQuestion>

<FinancialExplanations>

{knowledgebase-0}

</FinancialExplanations>

<Answer>

Provide a clear and concise answer with citations (Sheet Name, Cell Reference).

</Answer>

Temperature: 0.1 for deterministic, repeatable answers.

4) Display a Clear, Cited Response (Output)

What it does: Shows the answer text and table with inline citations; allow Download PDF and Copy.

Enhancements: Add “Ask a follow-up” to chain questions without re-uploading files.

5) Export Interface: Advanced Form

What it does: Publishes a simple form so non-technical users can ask questions and attach files securely—without opening the builder.

Security: SSO + role permissions; file storage scoped to your tenant.