#3826 App to "Mine Ideas" 24/7/365

#3826 App to "Mine Ideas" 24/7/365

#3826 I want to develop a software application to "mine ideas" 24/7/365. With generative-AI assistance, the app should select and industry or user group, identify opportunities to providing innovative solutions, suggest improvements, and describe the implementation of each suggested improvement. The app should evaluate the results of each step, ranking each suggestion by feasibility, practicality, complexity, cost and risk.

This is a fantastic and ambitious project! You're essentially describing an automated strategic innovation pipeline. Building this requires connecting several distinct components: a data ingestion engine, a multi-step AI reasoning core, and a real-time dashboard.

I've put together a detailed project proposal and system design document. This file outlines the complete architecture, the technology stack I'd recommend (using Node.js, Firestore, and the Gemini API), and the specific AI prompts you'd need to build the "idea mining" loop.

Project Proposal: 24/7 AI Idea Miner Application

1. Executive Summary

This document outlines a system design for a "24/7 AI Idea Miner," an application designed to continuously identify, develop, and evaluate new business opportunities. The system will leverage a real-time data ingestion pipeline, a multi-step generative AI core powered by the Google Gemini API, and a real-time database and frontend. Users will be able to define target industries or user groups and receive a constant stream of ranked, actionable ideas, complete with implementation outlines and feasibility analysis.

2. Core Concept & Workflow

The application's core is an automated, continuous loop:

1. Watch: The user defines a "Watch Target" (e.g., "Sustainable packaging for e-commerce," "Fintech solutions for Gen Z," "Improving urban mobility").

2. Ingest: The system constantly scans the web (news, social media, reports) for new data related to this target.

3. Identify: A grounded AI prompt sifts through this data to find a new "Opportunity" or "Pain Point."

4. Brainstorm: The identified opportunity is fed to a creative AI prompt to generate an "Innovative Solution" and an "Implementation Plan."

5. Evaluate: The solution is fed to a third, structured-output AI prompt to rank it based on the user's criteria (feasibility, cost, risk, etc.).

6. Store & Display: The complete "Idea Packet" (Opportunity + Solution + Evaluation) is saved to a database and appears in real-time on the user's dashboard.

3. System Architecture

We will need four main components:

1. Backend Server (Node.js / Express):

   • Manages user-defined "Watch Targets."

   • Runs the 24/7 scheduler (e.g., node-cron) that triggers the pipeline.

   • Handles all API calls to data sources (Google Search) and the Gemini API.

   • Secures all data and handles authentication.

2. AI Core (Gemini API):

   • This is not a single prompt, but a chain of prompts. We will use the gemini-2.5-flash-preview-09-2025 model for its speed, advanced reasoning, and structured output capabilities.

   • Prompt 1: Opportunity Finder (Grounded): Uses the tools: [{"google_search": {}}] property to find real-time information.

   • Prompt 2: Solution Generator (Creative): A standard text prompt for creative brainstorming.

   • Prompt 3: Evaluator (Structured JSON): Uses responseMimeType: "application/json" to get a consistent, machine-readable ranking.

3. Database (Firestore):

   • A NoSQL, real-time database is perfect for this.

   • We can store the WatchTargets and the resulting GeneratedIdeas in separate collections.

   • Its real-time nature (onSnapshot) means the frontend will update instantly as new ideas are mined.

4. Frontend (React or Angular):

   • A dynamic dashboard where users can manage their Watch Targets.

   • Displays the feed of new ideas in a "card" format.

   • Allows users to filter and sort ideas by their evaluation scores (e.g., "Show me only 'High Feasibility' ideas").

4. Detailed AI Pipeline & API Calls

This is the heart of the application. The backend scheduler would run this entire function (e.g., every hour) for each WatchTarget.

Step 1: Opportunity Finder (Grounded)

The system first needs to find a relevant, recent opportunity. We'll use the Gemini API with Google Search grounding.

System Instruction:

"You are an expert market analyst and trend-spotter. Your job is to analyze the latest news and discussions related to the user's target industry and identify a single, specific new opportunity or customer pain point."

User Query:

"Find the latest trends, user complaints, and market gaps for: [WatchTarget Name]"

API Call (Pseudo-code):

const userQuery = "Find the latest trends, user complaints, and market gaps for: Sustainable packaging for e-commerce";
const apiKey = ""; // Handled by the environment
const apiUrl = `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-preview-09-2025:generateContent?key=${apiKey}`;

const payload = {
  contents: [{ parts: [{ text: userQuery }] }],
  tools: [{ "google_search": {} }],
  systemInstruction: {
    parts: [{ text: "You are an expert market analyst..." }]
  },
};

// ... fetch call ...
const result = await response.json();
const opportunity = result.candidates[0].content.parts[0].text;
// opportunity = "A recent trend shows consumers are frustrated with the amount of non-recyclable plastic air pillows in boxes."

Step 2: Solution Generator (Creative)

Now, we take that specific opportunity and brainstorm a solution.

System Instruction:

"You are an innovative product designer and business strategist. Given an opportunity, your job is to brainstorm a creative, novel solution. For this solution, briefly describe it, then provide a high-level 3-step implementation plan."

User Query:

"Opportunity: [opportunity from Step 1]"

API Call (Pseudo-code):

const userQuery = `Opportunity: ${opportunity}`; // From Step 1

const payload = {
  contents: [{ parts: [{ text: userQuery }] }],
  systemInstruction: {
    parts: [{ text: "You are an innovative product designer..." }]
  },
  // Note: No 'tools' here, we want creative generation.
};

// ... fetch call ...
const result = await response.json();
const solutionAndImplementation = result.candidates[0].content.parts[0].text;
// solutionAndImplementation = 
// "Solution: 'Eco-Shred' Mailer
// Description: A 100% recycled cardboard mailer with a built-in, perforated inner layer that the user can easily tear to create their own paper-based 'shred' filler, replacing the need for plastic pillows.
//
// Implementation Plan:
// 1. Design & Prototype: Develop and patent the perforated mailer design. Source 100% post-consumer recycled cardboard suppliers.
// 2. Partnership: Partner with 3-5 mid-sized e-commerce brands for an initial pilot program to gather feedback.
// 3. Automation: Develop a high-speed manufacturing line to fold and perforate the mailers at scale."

Step 3: Evaluator (Structured JSON)

This is the final, critical step: ranking the idea. We will request a JSON object to ensure the data is consistent and can be stored and filtered in our database.

System Instruction:

"You are a pragmatic venture capitalist and risk analyst. Your job is to evaluate a business idea based on the provided criteria. You must only respond with a valid JSON object."

User Query:

"Please evaluate this idea: [solutionAndImplementation from Step 2]"

API Call (Pseudo-code):

const userQuery = `Please evaluate this idea: ${solutionAndImplementation}`; // From Step 2

const evaluationSchema = {
  type: "OBJECT",
  properties: {
    "feasibility": { "type": "STRING", "enum": ["High", "Medium", "Low"] },
    "feasibility_reasoning": { "type": "STRING" },
    "practicality": { "type": "STRING", "enum": ["High", "Medium", "Low"] },
    "practicality_reasoning": { "type": "STRING" },
    "complexity": { "type": "STRING", "enum": ["High", "Medium", "Low"] },
    "complexity_reasoning": { "type": "STRING" },
    "cost": { "type": "STRING", "enum": ["High", "Medium", "Low"] },
    "cost_reasoning": { "type": "STRING" },
    "risk": { "type": "STRING", "enum": ["High", "Medium", "Low"] },
    "risk_reasoning": { "type": "STRING" },
  },
  required: ["feasibility", "practicality", "complexity", "cost", "risk"]
};

const payload = {
  contents: [{ parts: [{ text: userQuery }] }],
  systemInstruction: {
    parts: [{ text: "You are a pragmatic venture capitalist..." }]
  },
  generationConfig: {
    responseMimeType: "application/json",
    responseSchema: evaluationSchema,
  },
};

// ... fetch call ...
const result = await response.json();
const evaluation = JSON.parse(result.candidates[0].content.parts[0].text);
/* evaluation = 
{
  "feasibility": "High",
  "feasibility_reasoning": "The technology is simple (perforation, cardboard folding) and relies on existing manufacturing processes.",
  "practicality": "Medium",
  "practicality_reasoning": "Consumer adoption is likely high, but convincing e-commerce companies to switch suppliers may be difficult.",
  "complexity": "Medium",
  "complexity_reasoning": "The design and patent process is low-complexity, but scaling a manufacturing line is a medium-complexity logistical challenge.",
  "cost": "Medium",
  "cost_reasoning": "Initial R&D and patent costs are low, but machine re-tooling and manufacturing setup will require significant capital.",
  "risk": "Low",
  "risk_reasoning": "Market risk is low as it directly addresses a known consumer complaint and aligns with corporate ESG (Environmental, Social, Governance) goals."
}
*/

5. Database Schema (Firestore)

Your Firestore database would be very straightforward.

1. Collection: WatchTargets

   • Document ID: user_A_target_1

   • Fields:

     • userId: "user_A_id"

     • name: "Sustainable packaging for e-commerce"

     • createdAt: Timestamp

     • lastRun: Timestamp

     • isActive: true

2. Collection: GeneratedIdeas

   • Document ID: (auto-generated)

   • Fields:

     • watchTargetId: "user_A_target_1"

     • createdAt: Timestamp

     • opportunity: "A recent trend shows consumers are frustrated with..."

     • solution: "Solution: 'Eco-Shred' Mailer..."

     • implementation: "1. Design & Prototype..."

     • evaluation: {

       • feasibility: "High",

       • feasibility_reasoning: "...",

       • cost: "Medium",

       • cost_reasoning: "...",

       • (etc.)

       • }

6. Challenges & Considerations

• Cost: This is the biggest challenge. A 24/7/365 pipeline that makes multiple, complex AI calls per run, for multiple users, will become expensive. You must implement strict cost controls, rate limiting, and optimization (e.g., using gemini-2.5-flash instead of more expensive models).

• Data Quality: The quality of the "opportunity" (Step 1) dictates the quality of the entire process. Poor data in = poor ideas out. The Google Search grounding is essential here.

• "Hallucination" & Repetition: The AI may "hallucinate" its evaluation or start generating repetitive ideas. The prompt engineering (especially the system instructions for each "persona") is critical to keep the outputs novel and grounded.

• Human-in-the-Loop: This system should not be a "black box." The UI should allow users to "thumbs up/down" ideas, which can be used to fine-tune the prompts or (in a more advanced version) the model itself.

7. Next Steps

1. Build a Proof of Concept (PoC): Before building a 24/7 service, create a simple web app with one text box. The user types in a "Watch Target," clicks "Generate," and it runs this entire 3-step pipeline once.

2. Integrate Firestore: Connect the PoC to Firestore to save the results.

3. Build the Scheduler: Once the single-run pipeline works, move the logic to a backend Node.js server and trigger it with node-cron.

4. Develop the Dashboard: Build the "real-time" dashboard that reads from the Firestore collection.

This proposal gives you a complete blueprint for the application. The key is the "AI pipeline" or "chain-of-thought" where you use different, specialized AI prompts to build on each other's output, moving from broad research to creative brainstorming to structured evaluation.

Let me know if you'd like to start building the Proof of Concept for this. We could create the frontend and the backend logic for that first "single-run" version!

Would Mathematica with Wolfram LLM Kit be useful in driving this application?

That's an excellent question. Yes, Mathematica with the Wolfram LLM Kit would be extremely useful, but not as a replacement for the core components in your Canvas proposal.

It would act as a powerful enhancement, especially for Step 5 (Evaluate).

Here’s the difference:

• Current Plan: As outlined in the Canvas, your workflow has the Gemini API estimate the evaluation metrics (feasibility, cost, risk). This results in subjective, qualitative labels like "Cost: Medium" based on the model's general trained knowledge.

• With Wolfram Integration: The Wolfram LLM Kit would allow your Gemini API "Evaluator" prompt to call out to the Wolfram Knowledgebase. This is a massive, curated database of computational, factual data.

Instead of just guessing, your app could get hard numbers.

For example, in Step 5:

1. Your Gemini "Solution Generator" (Step 4) proposes: "Develop a high-speed manufacturing line for the 'Eco-Shred' Mailer."

2. Your Wolfram-enhanced "Evaluator" (Step 5) would then be able to ask Wolfram for:

   • "Average cost of industrial automation equipment for paper packaging."

   • "Market size for e-commerce shipping supplies."

   • "Current price of 1 ton of post-consumer recycled cardboard."

   • "Competitors for sustainable packaging solutions."

This would change your "Idea Packet" from having a subjective guess like "cost": "Medium" to a much more valuable, data-driven analysis like "estimated_capital_cost": "$1.5M - $3M" and "market_size": "approx. $55B".

In short, in the workflow you selected, the Gemini API is the "creative strategist." Wolfram would be the "quantitative analyst" or "fact-checker" that provides the hard, verifiable numbers to back up the idea. It would make the final output significantly more reliable and actionable.

This overview of the Wolfram Notebook Assistant and LLM Kit gives a good sense of how it bridges natural language with computational data.

From <https://gemini.google.com/app/>  Google Gemini 2.5 Pro