Helping Strainprint Advance Medical Marijuana Analytics

Strainprint Technologies is the leader in demand-side cannabis data and analytics. Our partnership started in 2016 and since then we are helping them advance medical marijuana data and analytics.


Why Strainprint Chose Agilno?

Strainprint wanted to build a sophisticated analytics platform that gives producers, clinics and researchers the information they need to make informed decisions about what to grow, what to prescribe and what trends are on the horizon.

They were looking for an external development partner, with extensive experience in building SaaS solutions, analytical and decision support systems, performing data integrations and engineering. They chose the team at Agilno to outsource their entire development process and build the required solution.


Development Processes

Agilno team was responsible for defining entire development process, recommending software development methodology, hiring team members and evaluating tools and technologies to use. We used Jira as our project management tool, Bitbucket for code versioning, OpsGenie as incident management and xRay for QA management.

We used Scrum as our methodology for developing software in iterations. This allowed us to ship software more frequently, get feedback early, and make changes as fast as possible.



To support sales, marketing, finance and other departments we integrated with multiple required services to make sure Strainprint team can track and improve their operations and products.



To support the development process and have faster code delivery cycle the Agilno team deployed a robust DevOps methodology to have a truly Agile development environment. We used BitBucket to manage code versioning and BitBucket pipeline to run unit and integration tests after every commit.

Jenkins was used as primary tool for continuous integration and continuous deployment multiple times in a day. We defined code quality standards including creation of mandatory unit and integration tests (we currently have 85% test coverage) and utilized SonarCube to ensure code quality.


Data Engineering

To ensure maximum uptime, having collective view of company wide data and quick rendering of analytics visualizations we created a data warehouse, defined and built ETL process to pull data from multiple internal and external sources into a common warehouse.

We used Stitch to move the data to the warehouse. Data Form, Luigi and Easy Morph to transform the data and schedule transformations and we used Python, D3.js and Qlik to create powerful visualizations.


Watch Strainprint Analytics Video



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