Analyze call recording of customer support agents and provide detailed report on various quality parameters
Automated Call Quality Evaluation for Operational Efficiency in Healthcare Appointment Management
Validate your response or bid with the tender schedule and reports on any shortcomings, improvements or missing items
Agentic AI – Autonomous agents that can plan, reason, and execute tasks for validating tender content.
Autogen – Framework for building multi-agent conversations and intelligent workflows.
Langchain – Enables chaining of LLMs and tools, allowing complex tender validation workflows and prompt engineering.
Groq – High-speed, low-latency LLM inference engine for processing large tender documents and generating feedback.
Streamlit – Lightweight Python-based framework for building interactive dashboards and apps with real-time feedback and data visualization.
Proposal & Bid Managers
Validate tender submissions to ensure compliance and maximize the chances of winning contracts, without manually reviewing line-by-line.
SMEs & Startups
Level the playing field by getting automated feedback on proposal strength, helping smaller teams create competitive bids.
Consulting Firms
Automate the bid review process across clients, allowing consultants to focus on strategic improvements rather than administrative checks.
Government Contractors & Vendors
Ensure accuracy and completeness in large, regulated tenders — especially when managing responses across multiple teams.
Business Development Teams
Rapidly validate new submissions and maintain a library of optimized responses for future use.
📋 Compliance Assurance
Automatically checks your tender responses against the original schedule and compliance matrix, ensuring every requirement is addressed.
📌 Gap Analysis & Improvement Suggestions
Highlights missing sections, vague responses, or areas that can be strengthened with more detail or clarity.
🧠 AI-Powered Insights
Uses Agentic AI and Autogen agents to simulate evaluator feedback, flag inconsistencies, and recommend improvements for higher scoring responses.
🚀 Fast Iteration & Feedback
With Streamlit’s real-time interface, users get instant feedback and can iterate faster.
🔗 Modular & Expandable Architecture
Built using Langchain and Groq, the system can be extended to handle RFPs, RFIs, and custom evaluation matrices with minimal overhead.
YOLO v8 – For object detection in fashion images
Llama 3.2 90B Instruct – Large Language Model for deep language understanding
Mixtral (Groq mixtral-8x7b-32768) – High-performance LLM for fast reasoning
Sentence Transformer (paraphrase-MiniLM-L6-v2) – For semantic embeddings
multi-qa-MiniLM-L6-cos-v – For embedding and question-answer similarity
Image Captioning – Generates descriptions for fashion images
OCR (easyocr) – Extracts text from fashion-related images
PostgreSQL – Relational database for structured data
Chroma DB – Vector store used for semantic search
Faiss – Facebook AI Similarity Search for fast vector similarity
Crew.ai – Agent-based framework used for chatbot and web scraping
LLMs + Scrapers (Crew.ai Inbuilt) – For ingesting and querying fashion web data
Flask – Lightweight Python backend framework
Next.js – React-based web framework for building the portal
Stylists & Influencers
Discover emerging styles, analyze outfit popularity across regions, and create fashion-forward content driven by data.
E-commerce Platforms
Power smart product recommendations, trend-based sorting, and visual search using embedded fashion intelligence.
Design & Product Teams
Leverage AI to research and validate design concepts, gather global inspiration, and reduce the guesswork in product planning.
Fashion Market Researchers
Automate competitive analysis and consumer sentiment tracking by aggregating visuals and descriptions from across the web.
📈 Real-Time Trend Forecasting
Harnesses computer vision, LLMs, and embeddings to extract insights from fashion images, blogs, social media, and e-commerce platforms — delivering up-to-date style forecasts and emerging trends.
🧠 Intelligent Data Extraction & Integration
Uses OCR, image captioning, object detection (YOLO v8), and Crew.ai scraping workflows to pull structured and unstructured data from multiple sources into a unified knowledge base.
💬 AI Fashion Chatbot
A Crew.ai-powered virtual assistant trained on fashion domain knowledge, trend data, and user behavior — enabling personalized guidance for shoppers, stylists, or business analysts.
📊 Visual & Textual Intelligence
Combines image analysis with LLM-based NLP (Llama 3.2, Mixtral, MiniLM) to provide deep understanding of both visual style cues and descriptive context.
📦 Scalable & Modular Architecture
Built with Next.js, Flask, and Faiss vector DB, it’s ready for production use, scalable across teams, and easily deployable on Azure for enhanced performance and security.
Tool designed to streamline the process of creating formal business proposals
Sales & Marketing Teams
Quickly create customized proposals for prospects by referencing similar past proposals, case studies, or pitch decks.
Consulting Firms & Agencies
Reduce turnaround time for proposal submissions by using AI to generate baseline drafts tailored to client needs.
Internal Business Development Units
Standardize proposal language across departments and teams while reducing the dependency on manual formatting or writing.
Startups & SMEs
Empower smaller teams with enterprise-grade proposal creation tools, increasing efficiency without needing a full-time proposal writer.
Government or Grant Applications
Use the system to streamline repetitive application documentation by reusing and refining previous submission content.
📄 Proposal Automation
Automatically draft structured, professional business proposals using historical company data and AI-generated insights — eliminating repetitive work.
💬 Chat-Based Interface
A Copilot Studio-powered chat interface allows users to guide the proposal generation process intuitively, even without prior technical or writing expertise.
🧠 Knowledge-Powered Drafting
Pulls from previous documents and company knowledge stored in Microsoft SharePoint to ensure consistency, relevance, and alignment with organizational tone.
🚀 Quick Deployment & Accessibility
Lightweight deployment via Vercel (free tier) ensures the tool is accessible and scalable without additional infrastructure costs.
🛠️ Seamless Backend Integration
Built with Flask and easily integratable into existing productivity stacks or CRMs for broader workflow automation.
Filter and retrieve pictures based on attributes like sentiment and emotions, streamlining digital photo organisation.
Photography & Media Agencies
Quickly find the perfect shot for a campaign based on mood or expression — without sorting through thousands of images manually.
Internal Knowledge & Asset Management
Ideal for companies maintaining large visual libraries (e.g., employee event photos, marketing materials) that need advanced filtering for reuse.
Customer Experience Teams
Analyze customer-submitted photos for sentiment trends in campaigns or brand interactions.
Content Moderation Pipelines
Use emotion detection as an additional moderation layer in platforms dealing with large user-uploaded photo volumes.
Creative & UX Teams
Build mood boards, visual storytelling sequences, or customer journey galleries based on emotional tone.
🔍 Emotion-Based Search
Retrieve photos based on detected emotions (e.g., joy, anger, sadness), making curation faster and more intuitive than traditional keyword-based filtering.
🧠 AI-Driven Tagging & Organization
Automatically categorize images with emotion and sentiment tags using Azure AI Vision and custom ML models — no manual effort needed.
⚙️ End-to-End Integration
Built using a modern tech stack (Python, Flask, React, Docker), Picturesque integrates seamlessly into internal tools or can scale into production-level apps.
📂 Streamlined Workflows
Especially useful for media teams, photographers, and content managers — spend less time digging through folders and more time delivering results.
🔒 Secure & Private
Deployed via Azure DevOps with strong access control and containerization, ensuring data integrity and privacy compliance.
An AI Avatar which can represent the brand, answers the questions , FAQ ‘s about the brand , providing customer assistance in a more interactive way in the form of a website or a chroma extension which can guide the user on your website
Customer Support & FAQ Automation
Reduce support overhead by having Avatar handle frequently asked questions and direct users to relevant resources instantly.
Onboarding & Product Walkthroughs
Guide new users through your website or platform features step-by-step, improving engagement and adoption.
Sales Assistant for E-commerce & SaaS
Recommend products, upsell plans, or explain features based on user behavior — all while staying on brand.
Internal Knowledge Base Assistant
Use Avatar internally to assist employees in navigating documentation, tools, or company policies — accessible from any browser tab.
Brand Experience Amplifier
Deliver a consistent, friendly, and branded interaction experience that builds familiarity and loyalty over time.
💬 Human-Like Customer Engagement
Provides natural, contextual responses that feel conversational — not robotic — enhancing customer experience and brand trust.
🌐 Always-On Brand Ambassador
Whether embedded on your site or as a browser assistant, Avatar is always available to educate, assist, and convert visitors into loyal customers.
🎯 Personalized Guidance
Trained specifically on your brand’s data, Avatar can walk users through features, services, policies, or onboarding flows — just like a real assistant.
⚡ Quick Deployment & Integration
Flexible deployment as a web widget or Chrome extension, powered by scalable cloud infrastructure and modern AI APIs.
📈 Boost Conversions & Retention
Help customers find what they’re looking for faster, answer common doubts instantly, and reduce bounce rates on key pages.
Instantly answer customer support inquiries – zero waiting time, zero queue, zero customer frustrations.
OpenAI GPT-4