Venture Case Study
Brand Scan: from opportunity to working product
Brand Scan turns an emerging strategy question—how AI systems describe and recommend a brand—into an operating product. Origin Studios defined, designed, and built a Romanian-localized workflow for brand onboarding, prompt generation, competitor context, multi-platform scanning, source analysis, Google AI visibility, scoring, recommendations, and follow-through.
Written by Vlad Covaci · Reviewed by Radu Benga
Last reviewed
Product overview
What Brand Scan is
A brand analysis and digital-presence audit tool for growing companies.
Industry: AI search and brand visibility software
Venture type: Origin Studios venture
Intended audience
Who it was built for
Companies that need a clearer view of brand and digital visibility.
Starting context
Companies can inspect conventional search rankings and analytics, but AI answers introduce another discovery surface: a brand may be mentioned, omitted, described inconsistently, or supported by different sources across platforms. Manual checks are difficult to repeat and compare.
Problem and opportunity
Brand signals and digital performance are distributed across channels, while companies increasingly need to understand how AI systems represent them.
Create a measurable workflow that repeatedly asks relevant questions across AI platforms, records brand and competitor visibility, identifies cited sources, and turns the findings into prioritized actions for Romanian companies.
Initial hypothesis
If brand onboarding, question generation, competitive context, multi-platform scans, sources, and recommendations were connected in one product, companies could treat AI visibility as an observable improvement process rather than occasional manual prompting.
Relevant constraints
- The product had to turn a technically variable set of AI answers into an understandable business workflow.
- Prompts, competitors, language, sources, and recommendations needed Romanian market context.
- Platform results had to remain traceable enough for users to inspect what contributed to a score.
- The initial build needed to move quickly while leaving room for user-led product changes.
Origin Studios’ role
- Opportunity definition and product strategy
- AI-search and GEO workflow design
- Software architecture, implementation, and testing
- Romanian localization and market framing
- Launch, user feedback, and product iteration
Strategy and decision process
- 01
Decision 1
Define the user decision before the score: where is the brand visible, against whom, supported by which sources, and what should change next?
- 02
Decision 2
Use onboarding information to create relevant prompts and competitors rather than one generic prompt set.
- 03
Decision 3
Connect scanning to prioritized recommendations and a task workflow so measurement can lead to action.
- 04
Decision 4
Localize sources, language, examples, and recommendations for the Romanian market.
Product scope
- Brand onboarding and context collection
- Question or prompt generation
- Competitor identification
- Scanning across AI platforms
- Google AI visibility in the platform view
- Mention, position, sentiment, source, and trend views
- GEO scoring, prioritized recommendations, and task tracking
What was built
- A working Romanian AI-visibility monitoring platform
- A dashboard for global score, mentions, position, sentiment, platforms, and sources
- Signal, decision, and execution areas linking scans to prioritized actions
- Competitor comparison, gap analysis, recommendation, and follow-through workflows
AI and technical implementation
- The platform coordinates brand context, generated questions, competitors, and repeated scans across supported AI surfaces.
- Results are structured into mentions, position, sentiment, sources, trends, platform visibility, and GEO sub-scores.
- Google AI appears within the implemented visibility-by-platform view alongside ChatGPT, Gemini, Claude, and Perplexity.
- Recommendations and integrated task tracking connect observed signals to implementation work.
Validation and launch approach
- Released a working product rather than validating the concept only through a report or manual service.
- Used initial user feedback to change the product after the first build.
- Made a public report and live dashboard path available so the workflow could be understood before account commitment.
Go-to-market work
- Positioned Brand Scan specifically for Romanian brands and Romanian sources.
- Explained the product through the practical question of whether a brand appears in AI answers.
- Connected free scanning, an example report, recommendations, and optional implementation support.
Results and outcomes
Results and outcomes
- Built in two weeks
- Tracks how LLMs represent brands
- Localized for the Romanian market
Evidence register
Evidence behind the case study
| Claim or outcome | Evidence type | Evidence source | Date or period |
|---|---|---|---|
| Brand Scan is a working Romanian AI-visibility product | Live product implementation | Brand Scan public product page ↗ | Not separately recorded |
| The workflow includes brand monitoring across AI platforms | Public feature and dashboard record | Brand Scan public product page ↗ | Not separately recorded |
| Google AI visibility is represented in the platform view | Public product interface | Brand Scan public product page ↗ | Not separately recorded |
| The product is localized for the Romanian market | Public Romanian-language implementation | Brand Scan public product page ↗ | Not separately recorded |
| Built in two weeks | First-party build record | Origin Studios internal venture records | Not separately recorded |
| The product changed after initial user feedback | First-party product and feedback record | Origin Studios internal venture records | Not separately recorded |
Internal sources are first-party Origin Studios records. Public links and interface images show the implemented product; they do not independently audit internal performance records.