DSM–Neuro–Pharm Explorer

The description states that DSM–Neuro–Pharm Explorer is a transparent, rule-based educational tool designed to connect symptom patterns with neurobiology and pharmacology using a DSM-informed framework. It is presented as a browser-only application built with HTML, CSS, JavaScript, and supported by Codex and GPT-5.6 during development. The author describes it as an educational prototype without diagnostic or treatment capabilities, focusing on clinical safety, transparency, and privacy.

Key commercial due-diligence questions include: Is there any evidence of user engagement or adoption beyond the prototype? What is the long-term vision for monetization or scaling? How does the team plan to expand the symptom bank and content coverage?

The single most important open question is whether this educational tool has any traction, revenue, or customer base — all of which are absent from the self-reported description.

The description states that DSM–Neuro–Pharm Explorer is a browser-based application built with static HTML, CSS, and JavaScript. It uses explicit, human-readable rule tables to guide users through symptom selection and contextual input, connecting these inputs to educational content on neurobiology and pharmacology.

It is described as not using an artificial neural network or free-text interpretation in its current form, and it does not collect server-side data — all processing happens in the browser. The application supports a multi-stage interface with separate views for symptoms, follow-up questions, and results.

Inferred: It appears to be a proof-of-concept prototype intended for educational use, not clinical practice or commercial deployment.

The description states that the tool aims to connect symptom recognition, diagnostic frameworks (DSM), brain mechanisms, and pharmacology in one educational reference. The author emphasizes transparency, clinical responsibility, and non-diagnostic intent.

It is positioned as a modern educational resource that integrates layers of clinical knowledge into a single workflow, aiming to avoid separating these domains into different resources.

Inferred: The positioning appears to be educational and research-oriented, not commercial or product-focused. There is no indication of a monetization strategy or target for commercial adoption.

The description does not state who the intended users are beyond general educational contexts. It implies that the tool may be useful for students, trainees, or researchers in psychology, psychotherapy, and neuroscience.

It is described as being built with clinical safety in mind, suggesting a focus on those seeking to understand psychiatric symptoms and their biological underpinnings without diagnostic intent.

Inferred: The ICP likely includes individuals in training or education within mental health fields. No evidence of specific customer segments or personas is provided.

The description states that the application is deployed via GitHub Pages, requires no backend, installation, or API key, and collects no server-side data. It is described as a browser-only tool with no indication of monetization or pricing.

There is no evidence of a business model, revenue streams, or pricing structure in the self-reported description.

The description states that the prototype uses static HTML, CSS, and JavaScript with explicit rule tables for routing logic. It was built using Codex and GPT-5.6 for iterative design, debugging, and validation. The application is deployed through GitHub Pages and does not require a backend or API access.

It includes responsive layouts for desktop and mobile, syntax and browser-flow validation, and persistent safety messaging. It also supports conditional DSM chapter considerations and differential flags.

Inferred: The technical approach is minimalistic and privacy-focused, but lacks scalability or integration capabilities beyond its current prototype form.

The description states that the application is a live prototype with a public repository, demonstration video, and documented source code. It includes 45 standardized symptom options across 9 visible groups and supports seven broad dimensional routes.

It does not state any user engagement metrics, customer feedback, or adoption data beyond its own development and deployment.

Inferred: The tool is at an early prototype stage with no evidence of traction or market validation.

The description does not mention any direct competitors. It positions itself as a transparent educational resource that connects symptom patterns with neurobiology and pharmacology, but provides no information about existing tools in this space.

Inferred: No competitive landscape is evident from the self-reported description. The tool may be unique in its approach to combining DSM, neuroscience, and pharmacology in an educational context, but there is no evidence of prior similar products or market presence.

• The tool is described as a prototype with no revenue, customers, or traction.
• It does not use AI or machine learning beyond rule-based logic, which may limit future scalability or functionality.
• No evidence of monetization strategy or business model.
• The project is solo-developed (team size: 1), raising questions about long-term maintenance and expansion.
• The tool is browser-only with no backend, limiting integration or data collection capabilities.

1. What are the plans for expanding the symptom bank and DSM chapter coverage?
2. Is there any plan to monetize or commercialize this educational tool?
3. How will the team ensure content accuracy and expert review as it scales?
4. Are there any plans for integrating AI or NLP capabilities beyond the current prototype?
5. What is the long-term vision for user engagement or adoption beyond the prototype stage?