An internal Slack copilot that lets any team member ask data questions in natural language and get sourced, traceable answers, without bothering the ops person and without learning a new tool.
Sales asks “which deals are stuck?” Finance asks “what's our AR aging?” Project managers ask “what's due this week and who owns it?” Today, every one of those questions is a Slack thread, a screenshot, and a wait.
The ops person who knows the right view is the bottleneck. The team makes decisions on stale data, or no data. And when a number gets challenged, nobody can show how it was calculated, which is when finance meetings turn awkward.
The bot plans the query first, fetches only the records it needs, returns a structured answer with a deep link back to the source view, and writes the whole interaction to an audit log. Read-only by design, every query is a SELECT-equivalent.
A private channel where any team member types a question in natural language and gets a structured answer; text, an inline table or chart when the data shape calls for one, and a direct link back to the source so every number is traceable.
Connects to one Airtable workspace (multiple bases and tables) and a Google Sheets folder. Auto-discovers Airtable schema on first run and infers Sheet structure from column headers, no training, no manual mapping.
Every response includes a deep link back to the exact Airtable view or Sheet range used to compute the answer. If a number looks wrong, you can click through and verify the underlying records in seconds.
Every query is logged: the original question, the data sources hit, the answer returned, and the response time. A lightweight admin page lets the ops lead review answers, mark correct or incorrect, flag unanswered questions, and watch daily LLM cost.
“Which deals have been stuck in the pipeline for more than 14 days?”
The bot filters Airtable deals by stage-change timestamp and returns deal name, current stage, days stuck, and owner, with a direct link to the filtered view.
“What is the AR aging breakdown by client?”
Groups outstanding invoices by client and aging bucket (0–30, 31–60, 60+), returns a clean inline table, and links to the source range in Sheets.
“How many retainer hours has Acme used this month?”
Joins time-tracking with the client table, filters by current calendar month, sums logged hours, and compares against the contracted retainer if that field exists.
“Which projects are due this week and who owns them?”
Filters the project status table by due date inside the current ISO week, returns name + owner, and flags overdue items from the previous week separately.
“Who has logged time on Bituin in the last 30 days?”
Filters the time tracking table by project and date range, deduplicates by team member, and returns hours logged per person with a link to the underlying records.
“Who has been on more than five client calls this month?”
Substring-matches calendar event titles against known client names, applies a confidence threshold, and notes which matches were exact vs fuzzy. Low-confidence rows are surfaced separately.
“How much have we billed Acme this quarter?”
If a number is challenged, the audit log shows exactly which records were summed, the date range used, and the field interpretation, so disagreements (calendar vs fiscal quarter, missing record, wrong field) get resolved in minutes.
“(opens the admin page, not Slack)”
Reviews the past 24 hours of queries, marks answers correct or wrong, flags unanswered questions for prompt review, and watches daily token spend in dollars, no separate Anthropic dashboard needed.
Technical specification document and a working live connection to one Airtable base, with schema auto-discovery confirmed end-to-end.
The bot answering five canonical question types accurately inside Slack, with source links and per-query logging active.
Admin page live with full audit log, daily LLM cost display, and full project handoff with documentation your team owns.