Public Projects

clever-whatsapp

by Meghna Rajawat

WhatsApp-Based AI-Governed Investment & Financial Distribution Platform (Mutual Funds | Broking | Lending | Insurance | Payments) 1. System Overview The proposed system is a WhatsApp-first, AI-governed financial orchestration platform designed to enable distribution and execution of multiple financial products through a secure conversational interface. The platform integrates: Mutual Fund Transaction Rails (NSE NMF II, BSE StAR MF) Broking APIs (Angel One SmartAPI and future integrations) Lending Infrastructure (FinBox, Decentro) Insurance APIs (Digit, Acko) KYC & Identity APIs (Aadhaar eKYC, CIBIL, Account Aggregator, CKYC) Payment & Mandate Infrastructure (UPI, Razorpay, NACH) WhatsApp Business API as the primary customer interaction layer At the core of the system is an AI-driven orchestration engine that governs intent recognition, workflow routing, escalation management, and compliance-safe interaction handling. The system is modular, phase-driven, compliant-ready, and designed for long-term scalability. 2. Architectural Model The system will follow a layered, modular architecture: WhatsApp Interaction Layer AI & Conversation Intelligence Layer Workflow Orchestration Layer Financial Domain Modules Integration Adapter Layer Data & Infrastructure Layer Admin & Operations Control Layer All third-party providers will be connected through adapter abstractions to ensure: Provider isolation Flexibility for future migration Rail redundancy Failover capability The AI layer will operate independently from execution rails, ensuring separation between decision logic and transaction execution. 3. AI & Intelligence Layer Architecture The AI layer acts as the decision-making engine of the platform. It governs: Intent classification Entity extraction Multi-turn conversation management Workflow routing Escalation detection Guardrail enforcement Continuous enrichment The AI system will never directly execute financial transactions. All executions pass through backend validation and integration layers. 3.1 Intent Classification Engine The AI will detect and classify user intents across domains: Investment (SIP, Lump sum, Redemption, Portfolio) Broking (Order placement, Portfolio view) Lending (Loan application, Eligibility check) Insurance (Quote request, Policy issuance) Informational queries Escalation requests Outputs will be structured and confidence-scored to ensure deterministic routing. 3.2 Entity Extraction & Structured Output The AI will extract required financial parameters such as: Investment amount Scheme name Tenure Loan type Risk preference Policy type All extracted data will undergo validation before workflow execution. 3.3 Conversation State Management The system will support: Multi-turn dialogue Reference resolution Partial workflow continuation Session-based memory tracking This enables natural, uninterrupted WhatsApp-based financial journeys. 3.4 Workflow Orchestration Engine The AI determines: Required pre-checks (KYC, Mandate, Eligibility) Correct domain module activation Appropriate API invocation Error handling and fallback responses Execution remains backend-controlled to ensure transaction safety. 3.5 Guardrails & Compliance Controls Given the regulated financial domain, strict safeguards will be implemented: Tool-first data sourcing (no hallucinated financial data) Deterministic transaction confirmations Structured response formatting No speculative financial advice Ambiguity detection with clarification prompts All AI decisions will be auditable. 3.6 Human Escalation Intelligence Escalation triggers include: Explicit RM request Sentiment-based distress detection Repeated workflow failure High-value transaction flags The AI pauses automation and routes the interaction to the assigned Relationship Manager. 3.7 Continuous Enrichment Framework The AI system will support long-term expansion via: Modular intent registry Config-driven workflow templates Version-controlled prompt management Analytics-based refinement cycles Domain expansion without architectural rebuild 4. Phase-Wise Implementation Scope Phase 1 – Revenue Foundation Objective Establish the AI-governed core execution backbone for mutual funds and broking via WhatsApp. 4.1 WhatsApp Orchestration Layer Webhook ingestion Intent routing Conversation state management Escalation to human interaction 4.2 User Onboarding & Identity Aadhaar eKYC integration KYC status tracking User lifecycle management Identity state validation 4.3 Mutual Fund Execution (Primary Rail: NSE NMF II) Scheme discovery SIP creation Lumpsum execution Redemption handling Order status synchronization Reconciliation framework 4.4 Broking Integration (Angel One SmartAPI) Portfolio retrieval Order execution Token lifecycle management Status synchronization 4.5 Payment & Mandate Infrastructure Razorpay integration UPI mandate setup Webhook verification Transaction-payment reconciliation 4.6 Relationship Manager (RM) Escalation Layer RM-client mapping Ticketing framework Escalation matrix Callback scheduling Manual override capability 4.7 Admin & Operations Dashboard – Phase 1 Includes: User management KYC monitoring MF transaction logs Broking activity logs RM assignment management Immutable audit logs Operational override tools Phase 1 Outcome Live-ready AI-governed mutual fund and broking distribution system with operational oversight. Phase 2 – Risk & Lending Expansion Objective Introduce credit intelligence, lending orchestration, and insurance distribution. 5.1 Account Aggregator Integration Consent lifecycle management Financial data pull orchestration Consent artifact storage 5.2 Credit Bureau Integration (CIBIL) Credit score retrieval Bureau normalization Pull logging & audit tracking 5.3 Lending Infrastructure (FinBox) Loan application orchestration Offer tracking Document handling Status synchronization 5.4 Insurance Distribution (Digit / Acko) Quote generation Policy issuance Renewal tracking Document delivery 5.5 Admin Dashboard – Phase 2 Enhancements Bureau pull logs Consent tracking panel Loan monitoring Insurance issuance dashboard Risk analytics Phase 2 Outcome Multi-product AI-enabled financial distribution engine with credit and insurance capabilities. Phase 3 – Redundancy & Ecosystem Scaling Objective Increase resilience and diversify rails. 6.1 Multi-Rail Mutual Fund Infrastructure BSE StAR MF integration Rail abstraction Automated failover logic 6.2 Thematic Investments (Smallcase) Basket discovery Rebalancing orchestration 6.3 Credit Marketplace Integration (Paisabazaar) Pre-qualification engine Offer comparison 6.4 Dual Bureau Strategy (Experian) Secondary bureau integration Comparative analytics Phase 3 Outcome Resilient, diversified ecosystem-ready financial orchestration platform. Phase 4 – Embedded Finance Expansion Objective Transition into enterprise-grade embedded finance infrastructure. Includes: Co-lending (M2P) NACH mandate management DigiLocker integration CKYC registry integration Payment gateway redundancy Compliance reporting exports Phase 4 Outcome Fully embedded, AI-governed financial infrastructure platform. 5. Cross-Phase System Characteristics Across all phases, the platform will maintain: Adapter-based third-party integration State-driven workflow management Idempotent financial transaction handling Field-level encryption Immutable audit logging Centralized observability Modular domain isolation Progressive Admin Dashboard evolution AI decision trace logging Compliance-safe automation 6. Monitoring & Evaluation Framework The system will track: Intent accuracy Workflow success rate Escalation frequency API failure rates Transaction reconciliation accuracy AI latency Operational exception metrics Continuous optimization cycles will be implemented. 7. Final System Positioning Upon full implementation, the platform will function as: A WhatsApp-native financial execution environment An AI-governed financial orchestration backbone A consent-driven risk and credit engine A multi-rail resilient transaction system An embedded finance distribution infrastructure A centralized, audit-controlled financial operations platform

A girl shaking her ass

frosty-coach

by Tanna Bhavik

https://coach.kingdomofchess.com

hardy-account

by Maulik Patel

I have account agency for insurance. Pls cretae a website for me. With Admin login in python

snowy-website

by Keval Bhatt

Create a visually rich, modern demo website for a premium interior design studio. The goal of this website is to feel luxurious, immersive, and highly visual while keeping text minimal. The design should rely heavily on imagery, spatial transitions, and smooth scrolling experiences that allow visitors to visually explore the brand rather than read large amounts of content. Brand Name: Aurelia Interiors (you can adapt if needed). Overall Design Direction: The website should feel elegant, calm, and premium. Use light, sophisticated tones such as ivory, warm beige, soft taupe, light marble textures, and subtle gold accents. Avoid dark or heavy colors. The entire experience should feel high-end and refined, similar to premium architecture or luxury real estate websites. Navigation (Header): A clean floating header that stays visible as the user scrolls. It should include: Home About Us Services Projects Contact Us The header should be minimal, elegant, and slightly transparent so it blends with the visual background. Core Website Concept: The scrolling experience should simulate moving through a modern luxury house. As the user scrolls down, the perspective subtly transitions through different interior spaces of the house, creating a 3D immersive feeling. Each section of the house represents one of the services offered by the interior design studio. Example flow: Hero Section: A stunning modern luxury home interior with soft lighting and elegant furniture. Subtle animated camera movement or parallax effect to create a cinematic introduction. Display the brand name and a short tagline such as: "Designing Spaces That Feel Like Home." Scrolling Experience: As the user scrolls, the camera transitions through different rooms of the house. Each room highlights a service. Living Room Section: Represents Residential Interior Design Minimal text with elegant overlay labels and subtle animations. Kitchen Section: Represents Space Planning & Functional Design Bedroom Section: Represents Luxury Bedroom Concepts & Custom Furniture Dining Area: Represents Decor Styling & Material Selection Bathroom / Lounge Area: Represents Turnkey Interior Solutions Each section should emphasize imagery, layout ideas, furniture, textures, and decor rather than long explanations. Visual Experience: Use parallax scrolling, depth, shadows, and layered visuals to create a subtle 3D spatial feel as users move through the house. The experience should feel like exploring a beautifully designed home. Projects Section: Display a clean gallery of interior design projects with hover effects and smooth transitions. About Us Section: Short, elegant brand introduction emphasizing craftsmanship, design philosophy, and attention to detail. Contact Section: Minimal and stylish contact form with a luxurious feel. Animations & Effects: Smooth scroll transitions Soft fade-ins for text Parallax depth effects Subtle hover animations on images Typography: Elegant modern fonts that reflect luxury and design sensibility. Keep text minimal and spacious. Overall Experience: The website should feel like exploring a luxury home rather than browsing a typical website. The visuals, layout, and scrolling interaction should invite users to move through the space and imagine how their own interiors could look.

mammoth-organizations

by Priya Mandaviya

Build a SaaS product called "AI Marketing Agent Studio". Goal: A platform where organizations can create marketing campaigns using AI agents. Teams collaborate, generate marketing content, and track campaign performance. Core Features: 1. Multi-Tenant Organization Management - Organizations can create workspaces - Invite team members - Assign roles (Admin, Marketer, Viewer) - Users can switch between organizations - Each organization has separate projects and campaigns 2. AI Marketing Agents Create different AI agents for marketing tasks: - Blog Post Generator - Social Media Caption Generator - Email Campaign Generator - Ad Copy Generator Workflow: User creates campaign → selects AI agent → enters prompt → AI generates content → user edits and saves.

Want to build a web application to get all the jobs present on all the job posting sites to display them on a single dashboard, and a chatbot for help

# Document Chat Assistant — Product Requirements (Prompt for Tool Evaluation) ## Goal Build a production-ready **Chat with Documents assistant** that allows users to upload a large collection of files and ask natural language questions to retrieve accurate information from them. The system should understand document content, identify relevant information, and generate clear answers based on the available documents. This document describes the **expected capabilities and behaviors** of the system so that it can be tested using existing production-ready tools in the market. --- # Core Capabilities ## 1. Document Understanding The system should be able to process different types of documents and understand the content inside them. Examples of supported content include: - Text paragraphs - Structured tables - Lists and bullet points - Headings and sections - Multi-page documents - Spreadsheet-style data The assistant should treat the documents as a knowledge base and use them to answer questions. --- ## 2. Question Answering Users should be able to ask questions in natural language such as: - Product related questions - Information lookup - Comparison questions - Summary requests - Clarification questions - Follow‑up questions based on previous answers The system should respond with **clear and accurate answers based only on the available documents**. --- ## 3. Table Understanding Many documents contain structured data in tables. The assistant should be able to: - Understand table structure - Read rows and columns - Extract values from tables - Compare numbers - Identify patterns or differences in rows If a question is related to numerical or tabular information, the system should return answers based on that data. --- ## 4. Multi‑Document Reasoning Some questions may require information from **multiple documents**. The assistant should be able to: - Combine information from different sources - Compare data between files - Provide consolidated answers - Mention the relevant sources used for the answer --- ## 5. Document Discovery Users may also want to discover documents themselves. The assistant should support queries such as: - Finding documents related to a topic - Identifying which document contains certain information - Listing available documents - Suggesting relevant files for further reading --- ## 6. Conversation Context The assistant should support **multi‑turn conversations**. Example behavior: User question → assistant answers User follow‑up → assistant understands context and continues the conversation. The system should remember recent conversation context so follow‑up questions make sense. --- ## 7. Answer Quality Generated answers should: - Be concise and easy to read - Be factually grounded in the documents - Avoid hallucinating information not present in documents - Clearly present structured information when necessary Responses may include: - Text explanations - Bullet points - Short summaries - Tables when relevant --- ## 8. Source Awareness Whenever possible, the assistant should indicate **which document or section the information came from**. This helps users verify answers and explore the original source. --- ## 9. Large Knowledge Base Support The system should be able to operate with a **large document repository**, potentially containing thousands of files. Expected capabilities include: - Fast search across many documents - Accurate retrieval of relevant information - Stable performance as the dataset grows --- ## 10. User Experience Expectations The assistant interface should allow users to: - Ask questions easily - View clear answers - See referenced sources - Continue conversation naturally The experience should feel similar to interacting with a knowledgeable assistant that has read all the uploaded documents. --- # Evaluation Objective This requirement document is intended to test and evaluate **existing AI document assistant tools available in the market**. The goal is to observe: - How accurately the tool retrieves information - How well it understands structured data - Whether it handles multi‑document queries - How natural and helpful the generated responses are The system should behave like a **reliable knowledge assistant for document collections**.

create an chating application

create a fully function AI dsa tutor with the following functionalities Your project **“AI-Powered DSA Learning & Document Analysis Platform”** combines two major systems: * **DSA Tutor (Intelligent Learning Assistant)** * **Project SISO (RAG-based Document Intelligence System)** Together they form a **multi-capability AI platform** for learning, coding practice, and document analysis. Below is a **clear, structured list of the combined functionalities**. --- # Combined Functionalities of AI Tutor + Project SiSo ## 1. Intelligent DSA Learning System Provides **concept explanations for Data Structures and Algorithms**. **Capabilities** * Step-by-step explanations * Text-based diagrams * Concept breakdown from basic → advanced * Context-aware follow-up questions **Example** User asks: *“Explain Dynamic Programming”* System provides: * Definition * Intuition * Example problems * Diagram explanation * Complexity discussion --- # 2. Coding Practice & Evaluation Engine Users can **solve coding problems and receive automated evaluation**. ### Features * Multi-language coding support * Python * Java * C++ * JavaScript * Code analysis includes: * Correctness validation * Time complexity analysis * Space complexity analysis * Optimal vs brute-force detection * Improvement suggestions **Example** User submits solution for **Two Sum** System evaluates: ``` Correctness: Passed Time Complexity: O(n²) Optimal Solution: O(n) using HashMap ``` --- # 3. MCQ Test Generation & Knowledge Assessment System can generate **adaptive quizzes for concept reinforcement.** ### Capabilities * Dynamic MCQ generation * Difficulty levels (Easy / Medium / Hard) * Instant evaluation * Explanation for correct answer * Performance feedback --- # 4. Conversational AI Tutor Acts as a **general AI assistant for learning discussions.** ### Capabilities * Free chat mode * Doubt solving * Interview preparation * Concept comparisons Example queries: * “Difference between BFS and DFS” * “Explain heap vs priority queue” --- # 5. Intelligent Intent Detection The system automatically **detects what the user wants**. ### Possible intents | User Input | System Mode | | ---------------------- | ------------- | | Explain Graphs | Learning Mode | | Solve LeetCode problem | Coding Mode | | Give MCQs on Trees | Test Mode | | General question | Chat Mode | This routing ensures **correct handler execution**. --- # 6. Multi-Document Question Answering (RAG) Project SiSo allows users to **ask questions across multiple documents.** ### Supported documents * DOCX * Study material * Notes * Resume * Job descriptions ### Process ``` Document Upload → Parsing + OCR → Chunking → Embedding generation → Weaviate indexing → Retrieval → LLM answer generation ``` --- # 7. Citation-Based Answer Generation All answers from documents include **precise citations.** Format: ``` [doc_id:page_number:chunk_index] ``` Example: ``` The transformer architecture relies on self-attention [doc1:3:2] ``` This ensures **traceability and reliability of information**. --- # 8. Resume vs Job Description Analysis The system performs **career gap analysis**. ### Features * Resume parsing * JD parsing * Skill gap identification * Improvement suggestions Example output: ``` Missing Skills: - Kubernetes - Docker - GraphQL Recommendation: Learn container orchestration and API frameworks. ``` --- # 9. Study Material Question Answering Students can upload **lecture notes or textbooks**. Capabilities: * Ask conceptual questions * Extract summaries * Generate explanations * Retrieve exact references Example: ``` Q: Explain Gradient Descent ``` Answer retrieved from uploaded lecture notes. --- # 10. High-Availability AI Architecture System ensures **reliable LLM responses**. ### Model pipeline Primary model ``` Google Gemini ``` Fallback model ``` Ollama (Local Llama 3) ``` Mechanism: ``` Gemini request → if failure → automatic fallback to Ollama ``` --- # 11. Asynchronous Processing Pipeline Heavy tasks run **as background jobs**. Architecture: ``` User Request ↓ FastAPI API ↓ Redis Queue ↓ Worker ↓ Processing ↓ Result Retrieval ``` Benefits: * Scalable * Non-blocking API * Handles large documents --- # 12. Hybrid Search Retrieval System Uses **two retrieval methods together**. | Method | Purpose | | ------------- | ---------------- | | Vector Search | semantic meaning | | BM25 | keyword matching | This hybrid search improves **retrieval accuracy**. --- # 13. Multi-Document Knowledge Indexing The system supports **grouped document search**. Metadata stored in vector database: ``` doc_id doc_group_id source_file page_number chunk_index ``` This allows: * Cross-document answers * grouped search results --- # 14. Job Status Tracking System Users can track processing progress. Endpoints: ``` POST /submit_job GET /job_status/{job_id} GET /get_result/{doc_id} ``` Example status: ``` Processing: Chunking documents Processing: Generating embeddings Completed ``` --- # 15. Interactive Frontend Workflow The UI supports **three stages**: ### 1️⃣ Input Stage * Upload documents * Enter questions ### 2️⃣ Processing Stage * Progress polling * Loading indicators ### 3️⃣ Result Stage * AI generated answers * Citation references * Analysis reports --- # Final Combined Platform Vision The system ultimately acts as a **unified AI knowledge platform** capable of: | Domain | Capability | | ------------ | --------------------- | | Learning | DSA tutor | | Coding | Practice + evaluation | | Testing | MCQ quizzes | | Conversation | AI discussion | | Documents | RAG Q&A | | Career | Resume analysis | --- ✅ **In one sentence (interview ready):** > The platform integrates an AI-driven DSA tutoring system with a Retrieval-Augmented Generation (RAG) document intelligence engine, enabling interactive algorithm learning, automated code evaluation, adaptive testing, multi-document question answering with citations, and AI-based resume-job description gap analysis within a unified scalable architecture.

okay i need to create a project , i need you to create a automation system using which we can automate article genration tasks , so basically in that we will need to enter a simple automation tool that activates every Monday and thursday , it will already have the data of the user's industry and it will choose , it will do a websearch using apis ( either duck duck go or gemini or somehting that will be used to fetch the latest news about that industry withing last 72 to 84 hour ) , and based on that it will create a article about that in simple terms and easy to understand words and it will get autmatically publihsind on my website

Create a task management system for Make sure there is drag and drop functionality for task status