Collect! is a highly configurable debt collection software
Everything that matters to debt collection, all in one solution.
The flexible & scalable debt collection software built to automate your workflow. From startup to enterprise, we have the solution for you.
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Turn traffic into pipeline and prospects into customers
For account executives and sales engineers looking for a solution to manage their insights and sales data
Docket is an AI-powered sales enablement platform designed to unify go-to-market (GTM) data through its proprietary Sales Knowledge Lake™ and activate it with intelligent AI agents. The platform helps marketing teams increase pipeline generation by 15% by engaging website visitors in human-like conversations and qualifying leads. For sales teams, Docket improves seller efficiency by 33% by providing instant product knowledge, retrieving collateral, and creating personalized documents. Built for GTM teams, Docket integrates with over 100 tools across the revenue tech stack and offers enterprise-grade security with SOC 2 Type II, GDPR, and ISO 27001 compliance. Customers report improved win rates, shorter sales cycles, and dramatically reduced response times. Docket’s scalable, accurate, and fast AI agents deliver reliable answers with confidence scores, empowering teams to close deals faster.
Command-line tool from the Alire project and supporting library
Alire is a source-based package manager for the Ada and SPARK programminglanguages. It facilitates the building and sharing of projects within the Ada community, allowing developers to easily manage dependencies and publish their own libraries or programs. Alire aims to streamline the development process for Ada and SPARK by providing a standardized approach to package management.
Simulation of a two-channel Bell test, with closed-form proofs
Derivation, entirely by probability theory, of the correlation coefficient for a two-channel Bell test, with simulation in Ada and other languages. The Nobel Committe for Physics bans this program for subversive content. (Mirror of the repository at https://github.com/chemoelectric/eprb_signal_correlations)