Pollaris

The description states that Pollaris is a platform built with FastAPI and PostgreSQL, centered on a core abstraction called FormDefinition. This represents surveys as directed graphs where questions are nodes and conditional transitions are edges.

• The system supports branching forms through this graph structure.
• It includes:
  • A manual form builder producing FormDefinition.
  • An AI form generator producing FormDefinition.
  • A runtime engine using FormDefinition to guide respondents.
  • A validator checking FormDefinition before publishing.
  • An analytics pipeline aggregating results from FormDefinition.
  • An AI reporting system summarizing those results.

The author claims that:

• Every metric is calculated deterministically by the backend first, then interpreted by an LLM.
• The AI never performs calculations itself; it interprets aggregated data.
• A lightweight Mermaid-inspired DSL allows LLMs to describe surveys in readable text instead of large JSON graphs.
• The compiler generates question IDs, connects default transitions, and validates the resulting graph.

Inference This suggests a tightly integrated architecture where form logic is shared across generation, execution, analytics, and reporting — but no evidence exists that this has been tested or used beyond the author’s own development.

The description states:

• Pollaris helps users find their “north star” in customer feedback.
• It eliminates busywork of building surveys or digging through spreadsheets.
• It computes analytics that understand branching forms, not linear ones.
• It generates natural-language AI reports explaining what the numbers mean.

Inference These claims reflect a positioning toward simplifying survey analytics and empowering non-technical users with AI insights. However, there is no evidence of actual market positioning or product-market fit beyond the author’s own account.

The description does not clearly identify:

• Who uses Pollaris.
• What specific personas or industries it targets.
• Whether it's aimed at individual developers, teams, or enterprises.

Inference Based on the technical stack and features (e.g., AI form generation, graph-based survey logic), it may appeal to developers or product managers who want to automate survey creation and analysis. But no explicit ICP is stated.

There is no mention of:

• Revenue streams.
• Pricing models.
• Monetization strategy.
• Customer acquisition costs.
• Any commercial arrangements.

Inference The business model remains undefined in the self-reported description.

Key technical elements reported:

• Built with FastAPI, PostgreSQL, Vite.
• Core abstraction: FormDefinition as a directed graph.
• Supports branching surveys via graph traversal.
• Uses a lightweight Mermaid-inspired DSL for AI input.
• Includes validation logic to catch dead ends, missing coverage, cycles, etc.
• Analytics engine calculates metrics based on actual traversal paths.
• Separation between backend analytics and LLM interpretation.

Inference The architecture shows strong engineering discipline around shared contracts and modularity. However, no evidence of production deployment or scalability testing is provided.

The description states:

• MVP completed in one week.
• Includes authentication, workspaces, graph engine, DSL, publishing, response collection, analytics, and AI reporting.
• No mention of users, customers, or usage metrics.
• No evidence of revenue, retention, or adoption.

Inference This is a personal project submitted for a hackathon. There is no evidence of traction or product maturity beyond the author’s own development efforts.

The description does not reference:

• Competitors.
• Market size.
• Differentiation from existing tools like SurveyMonkey, Typeform, or JotForm.
• Any competitive advantage claimed.

Inference No competitive positioning or market context is evident in the self-reported account.

• Unverified claims: All features and capabilities are self-reported without external validation.
• Limited scope: The project appears to be a hackathon MVP with no evidence of real-world usage.
• No commercialization path: No pricing, monetization or customer data is mentioned.
• Single-person team: Only one developer involved, which raises questions about scalability and long-term maintenance.
• AI dependency risk: Reliance on AI for form generation implies potential unreliability if LLMs fail to produce valid outputs consistently.

1. What specific use cases are you targeting with Pollaris?
2. Have you tested the platform with real users or customers beyond your own development?
3. How do you plan to monetize this tool?
4. What is the expected timeline for moving from MVP to a scalable product?
5. Are there any known limitations in how well AI can generate valid branching forms?
6. Do you have plans to integrate with existing survey tools or platforms?