Practice digital marketing through connected workplace simulations.
KRYNSTAR is a free learning project built around the work of a digital marketing team: investigating a brief, reconciling data, making decisions under constraints, producing deliverables, and explaining the results. It is designed for people who need practical experience without paying for a course or an AI subscription.
Current status: Private development. KRYNSTAR is not yet open for public use or download. This page describes the project and its direction; it is not a claim that a public release is ready.
- Research and measurement: inspect exports, define metrics, and resolve conflicting numbers.
- Growth planning: choose audiences, channels, budgets, and priorities.
- Campaign operations: interpret paid-media performance and make trade-offs.
- Search and content: connect search intent, pages, messaging, and editorial decisions.
- Lifecycle and CRM: segment audiences, plan communications, and work through handoffs.
- Analysis and reporting: evaluate experiments, forecast outcomes, and present recommendations.
The work is connected inside a simulated company, so a decision in one assignment can affect what comes next. Learners receive evidence-based feedback and can revisit a scenario to try a different approach.
- Receive a company brief with a goal, constraints, and an imperfect stakeholder request.
- Investigate source files, performance data, and previous decisions.
- Submit job-relevant work, from a measurement plan to an executive review.
- See the simulated consequences, feedback, and evidence behind the assessment.
- Reset or start another run to practice again.
The private development version includes two connected US-market scenario families: a direct-to-consumer ecommerce engagement and a B2B SaaS pipeline engagement. More scenarios and markets are planned, but the priority is depth and reliable assessment before volume.
KRYNSTAR uses fictional companies and synthetic, scenario-specific datasets shaped like workplace exports. They are not live customer records, live advertising accounts, or guaranteed current market prices. The goal is to practice transferable workflows and judgment safely, with visible assumptions and traceable scoring.
A completed simulation can show the work a learner performed. It is not a professional qualification, a promise of employment, or proof that simulated results will match a real campaign.
The first release is being designed for desktop browsers. Core practice will not require a paid AI service. Optional bring-your-own AI assistance may be added later, with an AI-free path remaining available.
There is no public sign-up, release package, or launch date to announce yet. This public repository contains reviewed project information only; the application source and development infrastructure are not published here.
- Author: KSAGlory
- Community: discord.gg/ksahub
No application or source-code license is offered through this repository. Release terms will be stated when a public version is available.
Copyright © 2026 KSAGlory. All rights reserved.