AROHA is an AI-powered research and trend intelligence system that transforms live web information into structured signals, emerging trends, actionable opportunities, creative directions, and visual concepts.
Instead of simply collecting information, AROHA helps users move from:
What is happening? → Why does it matter? → Where is the opportunity? → What could we create next?
---
AROHA turns live web information into structured intelligence through a multi-stage research and creative workflow.
Research a topic using live web sources and collect relevant information from multiple sources. AROHA gathers current evidence that can be analyzed for meaningful patterns and emerging developments.
Analyze research findings to extract meaningful signals, recurring themes, concepts, styles, and supporting evidence that may indicate changes or emerging patterns.
Connect related signals to identify emerging patterns and trends. AROHA analyzes the relationships between signals to surface trends that may be relevant to the researched topic.
Transform identified trends into actionable opportunity spaces. AROHA evaluates opportunities based on their relevance and potential, helping users identify where meaningful possibilities may exist.
Turn selected opportunities into creative directions and concepts. This layer helps move from analytical insights toward practical ideas that can be explored, developed, or visualized.
Transform creative directions into detailed visual concepts and prompts. AROHA can optionally send these prompts to an AI image-generation system powered by FLUX to create visual representations of the ideas.
---
AROHA follows a multi-stage intelligence pipeline that transforms live web research into actionable insights and creative outputs.
USER QUERY
│
▼
🔎 WEB RESEARCH
Tavily
│
▼
📡 SIGNALS
Signal Extraction
│
▼
📈 TRENDS
Pattern Detection
│
▼
💡 OPPORTUNITIES
Opportunity Scoring
│
▼
🎨 CREATIVE
Creative Directions
│
▼
🖼️ VISUAL
Visual Prompting
│
▼
✨ AI IMAGES
FLUX / Hugging Face
The pipeline is designed to progressively transform information into higher-level intelligence:
Research → Signals → Trends → Opportunities → Creative Directions → Visual Prompts → AI Images
Each stage builds on the output of the previous stage, allowing AROHA to move beyond information retrieval toward trend discovery, opportunity identification, and creative exploration.
---
Traditional research systems often focus on finding and collecting information.
AROHA is designed to go beyond information retrieval by transforming research evidence into progressively higher-value intelligence.
Information
↓
Evidence
↓
Signals
↓
Patterns
↓
Emerging Trends
↓
Opportunities
↓
Creative Possibilities
The goal is to help users move from:
“What is happening?”
to:
“Why does it matter?”
and ultimately:
“What could we do with it?”
By connecting research, trend intelligence, opportunity discovery, creative exploration, and visual generation in a single workflow, AROHA helps turn raw information into actionable possibilities.
---
AROHA is organized as a layered AI pipeline where each layer performs a specific stage of the research-to-intelligence workflow.
┌────────────────────────────────────────────────────┐
│ AROHA UI │
│ Streamlit App │
└────────────────────────┬───────────────────────────┘
│
▼
┌────────────────────────────────────────────────────┐
│ RESEARCH LAYER │
│ │
│ Tavily Web Research │
│ Live Sources → Research Evidence │
└────────────────────────┬───────────────────────────┘
│
▼
┌────────────────────────────────────────────────────┐
│ INTELLIGENCE LAYER │
│ │
│ Signal Extraction → Trend Detection │
│ → Opportunity Scoring │
│ │
│ Groq LLM │
└────────────────────────┬───────────────────────────┘
│
▼
┌────────────────────────────────────────────────────┐
│ CREATIVE LAYER │
│ │
│ Opportunity → Creative Directions │
│ → Visual Concepts │
└────────────────────────┬───────────────────────────┘
│
▼
┌────────────────────────────────────────────────────┐
│ VISUALIZATION LAYER │
│ │
│ Visual Prompt Generation → Trend Visuals │
│ │
│ Optional FLUX Generation │
│ Hugging Face │
└────────────────────────────────────────────────────┘
User Query
↓
Streamlit Interface
↓
Tavily Web Research
↓
Research Evidence
↓
Signal Extraction
↓
Trend Detection
↓
Opportunity Scoring
↓
Creative Direction Generation
↓
Visual Prompt Generation
↓
Optional AI Image Generation
| Layer | Responsibility |
|---|---|
| UI Layer | Provides the interactive Streamlit interface |
| Research Layer | Collects live web information using Tavily |
| Intelligence Layer | Extracts signals, detects trends, and scores opportunities using LLM-powered analysis |
| Creative Layer | Converts opportunities into creative directions and concepts |
| Visualization Layer | Generates visual prompts and optionally produces AI-generated images |
This layered architecture allows AROHA to progressively transform live information into structured intelligence and creative outputs.
---
| Category | Technology | Purpose |
|---|---|---|
| Programming Language | Python | Core application and AI pipeline |
| User Interface | Streamlit | Interactive research and intelligence dashboard |
| Backend API | Flask | Backend API and application services |
| LLM | Groq | LLM-powered analysis and content generation |
| Web Research | Tavily | Live web search and research evidence collection |
| AI Intelligence | LLM-based Analysis | Signal extraction, trend detection, opportunity generation |
| Image Generation | FLUX | AI-generated visual concepts |
| Image Inference | Hugging Face Spaces | Hosted image-generation inference |
| Image Client | Gradio Client | Communication with the image-generation service |
| Configuration | python-dotenv | Environment variable and API key management |
| Version Control | Git & GitHub | Source-code management and collaboration |
Python LLMs Groq Tavily Agentic AI Generative AI
Web Research Signal Extraction Trend Intelligence Opportunity Scoring
Creative Generation Visual Prompting FLUX Hugging Face Image Generation
Streamlit Flask Gradio Client python-dotenv
---
## 📁 Project Structure
AROHA/
│
├── app.py
├── api.py
├── requirements.txt
├── .gitignore
│
├── assets/
│ ├── aroha\_research\_background.png
│ └── aroha\_trend\_background.png
│
├── src/
│ │
│ ├── analysis/
│ │ ├── creative\_generator.py
│ │ ├── opportunity\_scorer.py
│ │ ├── signal\_analyzer.py
│ │ └── trend\_detector.py
│ │
│ ├── llm/
│ │ └── client.py
│ │
│ ├── research/
│ │ ├── source\_image.py
│ │ └── web\_search.py
│ │
│ ├── visualization/
│ │ ├── image\_generator.py
│ │ ├── trend\_visual\_generator.py
│ │ └── visual\_prompt\_generator.py
AROHA follows an end-to-end workflow that transforms a user's research question into structured intelligence and creative outputs.
The user enters a research topic or query through the Streamlit interface.
AROHA sends the query to Tavily to retrieve relevant and current information from the web.
User Query
↓
Tavily Web Research
↓
Relevant Sources
↓
Research Evidence
The collected research is analyzed to identify meaningful signals such as:
- Recurring themes
- Emerging concepts
- Patterns
- Styles
- Consumer or market changes
- Supporting evidence
These signals provide the foundation for identifying broader trends.
AROHA analyzes relationships between signals to identify emerging patterns and trends.
Instead of treating every piece of information independently, related signals are connected to determine whether they represent a broader movement.
Research Evidence
↓
Signals
↓
Related Patterns
↓
Emerging Trends
Detected trends are transformed into potential opportunity spaces.
AROHA evaluates these opportunities based on their relevance and potential, helping users identify which areas may deserve further exploration.
Emerging Trend
↓
Opportunity Ideas
↓
Opportunity Scoring
↓
High-Potential Opportunities
Users can explore an opportunity and transform it into creative directions and concepts.
This stage moves AROHA from analytical intelligence toward practical creative exploration.
Opportunity
↓
Creative Direction
↓
Concept
Creative directions can be transformed into detailed visual prompts.
These prompts can optionally be sent to the image-generation system powered by FLUX through a Hugging Face Space.
Creative Direction
↓
Visual Prompt
↓
FLUX Image Generation
↓
AI-Generated Visual
USER QUERY
│
▼
🔎 WEB RESEARCH
│
▼
📡 SIGNALS
│
▼
📈 TRENDS
│
▼
💡 OPPORTUNITIES
│
▼
🎨 CREATIVE
│
▼
🖼️ VISUAL
│
▼
✨ AI IMAGE
The result is a complete journey from live research to intelligence, opportunity discovery, creative exploration, and visual generation.
Signals are the first layer of intelligence in AROHA.
Instead of treating web research as a collection of unrelated articles, AROHA analyzes the gathered evidence to identify meaningful patterns, recurring themes, emerging concepts, and supporting observations.
The signal analysis stage processes research evidence and extracts information that may indicate a broader change or emerging movement.
Research Sources
↓
Research Evidence
↓
LLM Analysis
↓
Signal Extraction
↓
Structured Signals
Each signal represents an observation that can contribute to understanding a larger trend.
The signal analysis layer can identify patterns such as:
- Recurring Themes — topics appearing across multiple sources
- Emerging Concepts — new ideas or behaviors gaining attention
- Styles & Aesthetics — visual, cultural, or design patterns
- Market Signals — changes in products, services, or consumer behavior
- Technology Signals — emerging technologies and applications
- Supporting Evidence — research information that strengthens a signal
A key purpose of signal intelligence is to connect individual observations.
For example:
Source A → New Consumer Behavior
Source B → Similar Product Direction
Source C → Growing Online Discussion
Source D → New Market Activity
│
▼
Related Signals
│
▼
Emerging Pattern
When multiple related signals appear across the research, they can provide stronger evidence for identifying an emerging trend.
Rather than passing raw research directly to the next stage, AROHA converts the analysis into structured intelligence that can be used by downstream components.
Raw Web Information
↓
Evidence
↓
Signals
↓
Related Signals
↓
Emerging Patterns
This creates a foundation for the Trend Intelligence layer, where multiple signals are analyzed together to identify broader emerging trends.
Research tells AROHA what is being discussed. Signals help AROHA understand what may be changing.
Trend Intelligence is the core analytical layer of AROHA that transforms individual signals into broader emerging patterns and trends.
Instead of simply reporting what is mentioned across web sources, AROHA analyzes relationships between signals to determine whether they collectively indicate a meaningful trend.
Multiple related signals can be connected to reveal a larger pattern.
Signal A ──┐
Signal B ──┼──→ Related Pattern ──→ Emerging Trend
Signal C ──┤
Signal D ──┘
This allows AROHA to move beyond isolated observations and identify connections across the research evidence.
The trend intelligence workflow can be represented as:
Research Evidence
↓
Signals
↓
Signal Relationships
↓
Pattern Identification
↓
Trend Detection
↓
Structured Trend Insights
The LLM-powered analysis evaluates the extracted signals and looks for meaningful relationships, recurring themes, and indications of broader change.
AROHA considers factors such as:
- Signal Relationships — how strongly multiple signals connect
- Recurrence — whether similar patterns appear across sources
- Emergence — whether the pattern suggests something that is developing or gaining attention
- Relevance — how closely the trend relates to the original research query
- Supporting Evidence — research observations that help validate the trend
The trend detection layer transforms signal-level information into higher-level insights that can be explored further.
Signals
↓
Emerging Pattern
↓
Trend
↓
Why It Matters
↓
Potential Opportunity
This creates a bridge between research intelligence and opportunity discovery.
Identified trends are passed to the opportunity intelligence layer, where AROHA explores what could potentially be built, created, or pursued around those trends.
📡 Signals
↓
📈 Emerging Trends
↓
💡 Opportunity Spaces
↓
🎨 Creative Directions
Signals show what is changing. Trends reveal the broader pattern behind that change.
Opportunity Intelligence is the layer that transforms identified trends into actionable opportunity spaces.
While trend intelligence focuses on understanding what is changing, opportunity intelligence explores what could be done with that change.
AROHA uses detected trends as a foundation for generating potential opportunities.
📈 Emerging Trend
↓
Trend Interpretation
↓
💡 Opportunity Generation
↓
Opportunity Evaluation
↓
High-Potential Opportunities
For each relevant trend, AROHA explores possible opportunity spaces based on the available research evidence.
These opportunities can represent areas such as:
- New product or service concepts
- Emerging market spaces
- Unmet user needs
- Business possibilities
- Creative applications
- Technology-driven opportunities
- New directions worth exploring
The goal is not to make a final business decision, but to provide evidence-informed possibilities for further exploration.
Generated opportunities can be evaluated and scored to help prioritize the most promising directions.
Opportunity
↓
Relevance
+
Potential
+
Trend Alignment
↓
Opportunity Score
Scoring helps distinguish potentially valuable opportunities from ideas that have weaker connections to the identified trends.
The opportunity layer helps users move from a large set of possibilities toward areas that deserve deeper investigation.
Emerging Trends
↓
Multiple Opportunities
↓
Opportunity Evaluation
↓
Prioritized Opportunities
↓
Creative Exploration
This creates a direct connection between trend intelligence and creative decision-making.
Once an opportunity is selected, AROHA can take it into the Creative Studio, where the opportunity is transformed into creative directions, concepts, and visual possibilities.
📈 Trend
↓
💡 Opportunity
↓
🎨 Creative Direction
↓
🖼️ Visual Concept
Trends reveal where change is happening. Opportunities explore what could be created from that change.
Creative Studio is the layer where AROHA transforms identified opportunities into creative directions and concepts.
The purpose of this stage is to bridge the gap between analytical intelligence and creative exploration.
Once an opportunity has been identified, AROHA explores possible ways it could be translated into a creative concept.
💡 Opportunity
↓
Context & Insight
↓
🎨 Creative Direction
↓
Concept Development
↓
🖼️ Visual Possibility
AROHA uses LLM-powered generation to develop creative directions based on the selected opportunity and the intelligence produced by previous stages.
Creative directions can explore areas such as:
- Product and service concepts
- Brand and campaign ideas
- Visual themes
- Design directions
- User experiences
- Content concepts
- Technology-enabled experiences
- New creative applications
The generated directions are grounded in the research, signals, trends, and opportunities discovered earlier in the pipeline.
A key principle of AROHA is that creative ideas should not exist independently from research.
Research
↓
Signals
↓
Trends
↓
Opportunities
↓
Creative Directions
This creates a traceable path from real-world information to creative exploration.
Creative directions can be further transformed into detailed visual concepts and prompts.
Creative Direction
↓
Visual Concept
↓
Detailed Visual Prompt
↓
AI Image Generation
This allows users to move from an abstract opportunity to a more tangible representation of what the idea could look like.
Traditional research workflows often end with a report or a list of findings.
AROHA continues the workflow by asking:
“What could we create from this insight?”
Creative Studio turns research-driven intelligence into ideas that can be explored, communicated, and visualized.
AROHA doesn't stop at discovering opportunities — it helps turn them into creative possibilities. ---
Visual Intelligence is the final creative layer of AROHA that transforms creative directions into detailed visual concepts and AI-generated imagery.
It connects AROHA's research and intelligence pipeline with generative visual AI.
The visualization workflow follows:
💡 Opportunity
↓
🎨 Creative Direction
↓
🖼️ Visual Concept
↓
✍️ Visual Prompt
↓
✨ AI Image Generation
AROHA converts creative directions into detailed visual prompts designed to communicate the intended concept clearly to an image-generation model.
The prompts can describe elements such as:
- Subject and composition
- Visual style
- Environment and setting
- Materials and textures
- Lighting
- Color and mood
- Camera perspective
- Artistic direction
- Overall visual atmosphere
This creates a structured bridge between creative reasoning and image generation.
AROHA can optionally send generated visual prompts to an external image-generation system powered by FLUX.
The image-generation workflow uses a Hugging Face Space for hosted inference and communicates with the service through the Gradio Client.
Creative Direction
↓
Visual Prompt Generator
↓
Detailed Visual Prompt
↓
Gradio Client
↓
Hugging Face Space
↓
FLUX
↓
✨ Generated Image
The complete creative flow connects AROHA's intelligence layers with visual generation:
Research
↓
Signals
↓
Trends
↓
Opportunities
↓
Creative Directions
↓
Visual Prompts
↓
AI-Generated Visuals
This allows AROHA to move from real-world research → intelligence → opportunity → creative concept → visual representation.
Visualizing an idea can make an emerging opportunity easier to understand, communicate, and explore.
Instead of ending with a text-based recommendation, AROHA can provide a visual representation of what a potential concept might look like.
AROHA turns research-driven ideas into visual possibilities.
Follow the steps below to set up AROHA locally.
git clone https://github.com/Thekeshavjoshi/aroha-trend-intelligence.git
cd aroha-trend-intelligenceCreate an isolated Python environment for the project:
python -m venv venvActivate the environment:
Windows:
venv\Scripts\activatemacOS / Linux:
source venv/bin/activateInstall the required Python packages:
pip install -r requirements.txtAROHA requires API credentials for its external AI and research services.
Create a .env file in the project root:
GROQ_API_KEY=your_groq_api_key
TAVILY_API_KEY=your_tavily_api_key
Add any additional configuration required by your local image-generation setup.
Important: Never commit API keys or other secrets to GitHub. Keep
.envin your.gitignore.
Start the Streamlit application:
streamlit run app.pyOnce the application starts, open the local Streamlit URL shown in your terminal.
Clone Repository
↓
Create Virtual Environment
↓
Install Dependencies
↓
Configure API Keys
↓
Run Streamlit
↓
🌸 Explore AROHA
AROHA uses environment variables to securely manage API credentials and external service configuration.
Create a .env file in the project root directory:
GROQ_API_KEY=your_groq_api_key
TAVILY_API_KEY=your_tavily_api_key| Variable | Purpose |
|---|---|
GROQ_API_KEY |
Provides access to the Groq LLM service used for AI-powered analysis and generation |
TAVILY_API_KEY |
Provides access to Tavily for live web research |
API keys should never be hardcoded into source files or committed to GitHub.
Make sure your .gitignore contains:
.env
This keeps sensitive credentials outside the public repository.
Never share your API keys publicly or commit them to version control.
After completing the installation and configuring the required environment variables, start the AROHA application using Streamlit.
streamlit run app.pyStreamlit will start the application locally and provide a URL in the terminal.
Open the displayed local URL in your browser to access the AROHA interface.
Once AROHA is running, the typical workflow is:
Enter Research Topic
↓
🔎 Start Web Research
↓
📡 Analyze Signals
↓
📈 Detect Trends
↓
💡 Explore Opportunities
↓
🎨 Generate Creative Directions
↓
🖼️ Generate Visual Prompts
↓
✨ Optional AI Image Generation
AROHA processes the research progressively, allowing users to move from live information to structured intelligence and creative exploration.
During development, Streamlit automatically reloads the application when relevant source files are changed.
To stop the application, press:
Ctrl + C
in the terminal running the Streamlit server.
AROHA is designed to progressively transform a research query into structured intelligence and creative outputs.
A user can enter a topic such as:
Emerging trends in AI-powered consumer products
AROHA then processes the query through its intelligence pipeline.
User Query
↓
🔎 Live Web Research
↓
📡 Signal Extraction
↓
📈 Trend Detection
↓
💡 Opportunity Generation
↓
🎨 Creative Directions
↓
🖼️ Visual Prompt Generation
↓
✨ Optional AI Image Generation
The research stage collects relevant information from live web sources.
The signal analysis layer then identifies meaningful observations, recurring themes, emerging concepts, and supporting evidence.
Web Sources
↓
Research Evidence
↓
Meaningful Signals
Related signals are analyzed together to identify broader patterns.
Signal A ──┐
Signal B ──┼──→ Emerging Trend
Signal C ──┤
Signal D ──┘
The resulting trends provide a higher-level view of what may be changing within the researched topic.
Detected trends are used to generate potential opportunity spaces.
Emerging Trend
↓
Opportunity Ideas
↓
Opportunity Evaluation
↓
Prioritized Opportunities
These opportunities can then be explored further through the creative layer.
A selected opportunity can be transformed into creative directions and concepts.
Opportunity
↓
Creative Direction
↓
Concept
Creative directions can be converted into detailed visual prompts and optionally passed to the FLUX-based image-generation pipeline.
Creative Concept
↓
Visual Prompt
↓
FLUX
↓
AI-Generated Visual
The complete workflow allows AROHA to move from:
Live Research → Evidence → Signals → Trends → Opportunities → Creative Concepts → Visual Possibilities
This progression helps transform raw web information into structured intelligence and actionable creative exploration.
AROHA can be used across research, strategy, innovation, creative exploration, and trend discovery workflows.
Discover emerging patterns, changing behaviors, and developments within a specific market or industry.
Explore potential business, product, service, and innovation opportunities emerging from identified trends.
Use trend intelligence to generate creative directions, campaign concepts, visual ideas, and new creative possibilities.
Identify emerging needs, technologies, and market signals that can inspire new product or experience concepts.
Transform large amounts of live web information into structured signals and higher-level insights that can support strategic exploration.
Use LLM-powered analysis to accelerate the process of moving from raw information → evidence → patterns → opportunities → ideas.
Research topics across different industries and domains to discover connections between emerging signals and potential opportunities.
AROHA is designed as an exploration and intelligence system—not a replacement for human judgment or final business decisions.
The following screenshots showcase the AROHA interface and its research intelligence workflow.
The AROHA home interface provides an entry point to the research and trend intelligence workflow.
AROHA analyzes research findings to extract meaningful signals, recurring themes, and supporting observations.
Related signals are analyzed to identify emerging patterns and trends within the researched topic.
AROHA is an evolving project, and several areas can be improved as the system develops further.
- 📈 Improved Trend Detection — strengthen trend identification by connecting more signals and improving trend relevance.
- 💡 Better Opportunity Generation — generate a broader and more relevant set of opportunity spaces from detected trends.
- 🎯 Opportunity Prioritization — improve opportunity scoring and ranking using stronger evaluation criteria.
- 📡 Research Optimization — reduce redundant research content while preserving important supporting evidence.
- 🧠 Intelligence Quality — improve the relevance, consistency, and depth of LLM-generated signals, trends, and opportunities.
- 🖼️ Visual Intelligence — improve the consistency and relevance of generated visual concepts and AI images.
- ⚡ Performance Optimization — optimize processing time, API usage, and LLM token consumption.
- 🌐 Broader Research Sources — explore additional research sources and data integrations.
- 🤖 Advanced Agentic Workflows — introduce more autonomous research and reasoning capabilities.
- 📊 Richer Visual Analytics — expand trend and opportunity visualization with more interactive representations.
AROHA's future direction is focused on becoming a more accurate, efficient, autonomous, and visually intelligent research-to-opportunity system.
- 🔎 Live AI-Powered Research — uses Tavily to gather current web information and transform it into structured research evidence.
- 🧠 Multi-Stage Intelligence Pipeline — moves from research and signals to trends, opportunities, creative directions, and visual concepts.
- 📡 Signal & Trend Intelligence — analyzes relationships between research signals to identify emerging patterns and trends.
- 💡 Opportunity Discovery — converts emerging trends into actionable opportunity spaces and evaluates them for prioritization.
- 🎨 Research-to-Creativity Workflow — connects analytical insights with creative direction generation.
- 🖼️ Generative Visual Intelligence — transforms creative concepts into detailed visual prompts with optional FLUX-based image generation.
- 🤖 LLM-Powered Analysis — uses Groq for intelligent analysis and generation across multiple stages of the pipeline.
- 🏗️ Modular Architecture — separates research, analysis, LLM, and visualization components for easier development and extension.
- ⚡ End-to-End AI Application — combines web research, LLM reasoning, trend intelligence, opportunity discovery, and generative AI in a single system.
- 🚀 Built for Exploration — designed to help users move from “What is happening?” to “What could we create next?”
The complete source code, project documentation, and implementation are available on GitHub.
👉 GitHub Repository: https://github.com/Thekeshavjoshi/aroha-trend-intelligence
If you find the project interesting, feel free to ⭐ the repository.
Keshav Joshi AI/ML Engineer | Generative AI | Agentic AI | LLM Applications
I build AI-powered applications focused on machine learning, generative AI, agentic systems, RAG, LLM applications, and intelligent research workflows.
- 💼 LinkedIn: Keshav Joshi
- 🐙 GitHub: Thekeshavjoshi
- 📧 Email: joshikeshav9977@gmail.com
⭐ If you found AROHA interesting, consider starring the repository.