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Analytics

Talk to Your Data

Ask questions about pipeline, performance and customer conversations in plain language, and get answers with the numbers and the quotes behind them.

Problem

Any question that is not already on a dashboard becomes a ticket, and the answer arrives after the decision has been made.

Solution

Counts are computed in code, not by the model. The model requests metrics and verbatims from a governed layer and writes the answer from what it gets back.

01 Solution architecture

SOURCES CRM Salesforce / HubSpot Pipeline, stages, owners Warehouse Snowflake / BigQuery / Redshift Where one exists Conversation insights Scored calls and extracted quotes With their source call Identity SSO / SCIM Who is asking, and what they may see PROCESSING Semantic layer Canonical metric definitions and joins One definition of 'pipeline', not four Query planner Question mapped to governed slices No open SQL against production Metric computation Counts calculated in code, not by LLM Insight rows vs unique calls distinct Answer synthesis Writes from returned metrics + quotes Prompt forbids invented numbers 01 02 03 04 DELIVERY Chat surface Slack or in-app, permission-scoped Answer carries its numbers and query Citation Supporting quotes and time range attached Checkable rather than trusted Refusal path Unanswerable questions declined explicitly Better than a confident wrong number

02 Estimated impact

The value is analyst capacity, and decisions that stop waiting days for a number that already exists.

Estimated annual value
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How this is calculated
  • Costed at $85/hr loaded analyst time. Adjust the share if your questions are mostly complex analysis.
  • Only self-serviceable questions count — genuine analysis still needs an analyst.
  • A model from your inputs, not a measured result at your organisation.
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