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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.
...This means that QAL can be backend agnostic about a subset of SQL features and data types. Of course custom SQL:s are also supported.
It is currently distributed as a Python 3 Library (pip3 install python3-qal) and Debian .deb package.
It is related the Optimal BPM project, see its Optimal Sync application for usage examples.
The text of this page is released under the Creative Commons Zero Waiver 1.0 (CC0).
WEB interface for the design, process and monitor inter-application data flows.
It’s a message queue EAI. Written in Perl,
Tested systems : Linux and HP-UX, WinXP is planned
Supported Connectors : RDBMS, , LDAP, EDI, CSV, XML, HTML etc ...