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.
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Skillfully - The future of skills based hiring
Realistic Workplace Simulations that Show Applicant Skills in Action
Skillfully transforms hiring through AI-powered skill simulations that show you how candidates actually perform before you hire them. Our platform helps companies cut through AI-generated resumes and rehearsed interviews by validating real capabilities in action. Through dynamic job specific simulations and skill-based assessments, companies like Bloomberg and McKinsey have cut screening time by 50% while dramatically improving hire quality.
Documentation and coding for the generation of combinations
This project comprises documentation and VBA programming code in respect of a combinations algorithm that I have written.
The algorithm is in the Excel file named Combinations.xlsm and the documentation that explains how to use interface and execution of the algorithm is in the pdf file called Combinations.pdf
You will need to enable macros in Excel in order to run the algorithm.
Documentation of two types of permutation algorithms
This project provides documentation on two different approaches to permutations. Permutations can be generated by building them or by transposing characters between different character positions. My project addresses the efficiency between CPU speed and memory usage for generating unique permutations from input source texts that have repeated characters.
The challenge is to create an efficient algorithm that can have the capacity to permutate sources texts that are hundreds and thousands...