For SaaS builders, software companies, ISVs and ISOs who want to embed payments into their tech stack
NMI Payments is an embedded payments solution that lets SaaS platforms, Software companies and ISVs integrate, brand, and manage payment acceptance directly within their software—without becoming a PayFac or building complex infrastructure. As a full-stack processor, acquirer, and technology partner, NMI handles onboarding, compliance, and risk so you can stay focused on growth. The modular, white-label platform supports omnichannel payments, from online, mobile and in-app to in-store and unattended. Choose from full-code, low-code, or no-code integration paths and launch in weeks, not months. Built-in risk tools, flexible monetization, and customizable branding help you scale faster while keeping full control of your experience. With NMI’s developer-first tools, sandbox testing, and modern APIs, you can embed payments quickly and confidently.
Learn More
Contractor Foreman is the most affordable all-in-one construction management software for contractors and is trusted by contractors in more than 75 countries.
For Residential, Commercial and Public Works Contractors
Starting at $49/m for the WHOLE company, Contractor Foreman is the most affordable all-in-one construction management system for contractors. Our customers in 75+ countries and industry awards back it up. And it's all backed by a 100 day guarantee.
Automated leaf area estimation from scanned leaf images
Black Spot is a free stand alone software and method to estimate leaf area from images of leaves captured using standard flatbed scanners. This easy to use software allows the user to batch process a large number of samples from multiple species with minimal user input.
Taxonomy assignment of metazoans using a python based pipeline
The aim of this project is to create an automated pipeline for taxonomic assignment of DNA sequences obtained from environmental samples.
We develop a series of python scripts to process the raw sequence data obtained from benthic environmental samples and to taxonomical assignment of these sequences and finally to integrate all data in a relational database.