Stanavi

Stanavi is a self-reported photography studio search service built as a hackathon project by one developer (T. Sato). The product allows users to filter studios by purpose, area, and price range, with AI assisting in collecting and structuring data from official sources. It does not appear to have any revenue, customers or traction beyond the author's own description.

The single most important open question is: What is the actual commercial viability of a search-as-a-service model for photography studios, especially given that the core value proposition appears to be minimal — matching user needs with existing studio listings?

Confidence Level: Low. This analysis is based entirely on self-reported evidence from one author, with no third-party verification or data on adoption, revenue, or customer behavior.

The description states that Stanavi is a photography studio search service. Users input three filters:

• Purpose
• Area
• Maximum price

It then recommends matching studios based on its own "search logic".

The system uses AI to collect structured data from official sources, which is then reviewed by a human before being published.

Key technical components (as declared by the author):

• Google Apps Script
• Google Sheets
• HTML, CSS, JavaScript
• AI tools like GPT-5.6 and Codex for development support

The product does not appear to have an integrated booking or payment system — users are directed to official studio websites for further action.

Inference: The service is a curated search engine for studio listings, not a marketplace or platform with transactional capabilities.

The author states that Stanavi was created to reduce the time spent searching for photography studios, targeting production teams, influencers, photographers, and other creators.

It positions itself as a tool that:

• Aggregates studio data from scattered sources
• Standardizes information presentation
• Recommends suitable options based on user-defined criteria

Claim: The product aims to help users spend less time searching and more time creating.

There is no evidence of prior positioning or evolution in messaging beyond the initial hackathon submission. No branding, marketing, or customer feedback data are provided.

The author states that Stanavi targets television production teams, influencers, photographers, and other creators who need to find photography studios.

There is no evidence of a defined Ideal Customer Profile (ICP) beyond this broad segment. No specific buyer personas, usage patterns or customer segmentation are described.

Inference: The target market appears to be niche — production-focused content creators with a need for studio logistics.

The description does not state any business model or pricing structure.

It is unclear whether Stanavi intends to monetize through:

• Advertising on studio listings
• Commission from bookings (if any)
• Subscription fees
• Data licensing

No pricing, revenue streams, or monetization strategy are mentioned.

The system was built using:

• Google Apps Script
• Google Sheets
• HTML, CSS, JavaScript
• AI tools (GPT-5.6, Codex)

AI is used in the data collection workflow, not in decision-making or recommendation logic.

Key technical details:

• Structured prompts are generated automatically based on fixed rules
• AI returns data in JSON format
• Human review and approval are required before publication
• No AI makes final decisions about what to publish

There is no evidence of scalability, API access, or integration with other platforms.

The description states that Stanavi was built as a hackathon project by one developer (T. Sato).

There is no evidence of:

• Users
• Revenue
• Customers
• Adoption metrics
• Product iterations or improvements beyond the initial version
• Any form of traction, growth or user feedback

Inference: The product exists in a very early stage — likely a prototype or proof-of-concept.

The description does not provide any information about competitors, market size, or competitive landscape.

No mention is made of existing platforms for finding photography studios (e.g., Google Maps, Yelp, specialized booking sites).

Absence of evidence: No indication of how Stanavi differentiates from or competes with other tools in this space.

• No commercial traction or revenue: The product is described as a hackathon project with no evidence of monetization.
• Single-person development team: A team size of one raises concerns about scalability and long-term maintenance.
• Human-in-the-loop data collection: While this ensures accuracy, it is not scalable and may become a bottleneck.
• No pricing or business model: Unclear how the product will generate value or revenue.
• Unverified AI claims: The author states AI is used for data collection but does not describe its performance or impact.

1. What is the actual process of collecting and validating studio data? How many studios are currently in the database?
2. Has there been any user testing or feedback from target customers?
3. What is the long-term vision for monetization — are you planning to charge studios, users, or both?
4. How do you plan to scale beyond the initial area?
5. What are the key assumptions about user behavior and willingness to use this tool?
6. Have you considered how to compete with existing search tools like Google Maps or specialized booking platforms?