GreenPulse

GreenPulse is a fractional CFO/COO analyst for cannabis manufacturers that turns internal inventory, sales, and state benchmark data into prescriptive narratives. It delivers a Monday Morning Brief and an agentic Strategic Chat so owners see what’s at risk, what’s working, and what to do next—ramp up, hold, or reduce—without hiring a full-time analyst.

Flask PostgreSQL Google Gemini Python
Cannabis / Cannabis manufacturing
Ongoing
GreenPulse

The Challenge

Cannabis operators (e.g. Connecticut manufacturers) need exception-based, decision-ready insights from scattered data. They lack a single view of sales trends, inventory runway, customer concentration, discount impact, and state market comparison, and manual analysis is time-consuming and error-prone.

Problem Identified

Manual processes causing inefficiency and errors

Our Solution

Automated workflow with intelligent processing

The Solution

A Flask application ingests CSV data (inventory, orders, state benchmarks) into PostgreSQL and applies a strict rule: the app does all aggregations and SQL; the LLM only reasons over pre-computed evidence. A Monday Morning Brief surfaces Executive Pulse, Sales & Trends, Top 3 Anomalies, and a Portfolio & operational snapshot. Strategic Chat uses an agentic loop with nine tools (weekly summary, category metrics, sales trends, price action, inventory runway, state comparison, dispensary mix, expiration risk, discount summary) so users can ask natural-language questions and get answers grounded in the same formulas and time semantics as the brief. Optional SharePoint ingest and Redis/RQ enable automated data refresh and async brief generation.

The Impact

Owners get a single source of truth for metrics and time semantics (e.g. last completed week, YoY). Exception-based briefs and chat reduce manual number-crunching and support faster decisions on inventory, pricing, and customer concentration. The tool-extensible design allows new insight surfaces (e.g. cash runway, labor, margin) to be added as data becomes available without redesign.

Time Saved

Significant reduction in manual processing time

Efficiency Gained

Streamlined workflows and improved accuracy

Cost Reduction

Lower operational costs through automation

Technologies Used

We leveraged cutting-edge technologies to deliver a robust and scalable solution

Flask
PostgreSQL
Google Gemini
Python

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