Anurag Nagare
I’m an AI & Machine Learning engineer with expertise in deve
New to Contra
Anurag is ready for their next project!
SLM vs LLM for startups
SLMs (1B–3B parameters) are becoming a kind of cheat code for startups: they can be run with a single GPU or even a powerful CPU, data can remain on your own infrastructure, and you still get almost LLM quality for very targeted tasks like support bots, internal search, or document workflows. In numerous benchmarks, contemporary SLMs are only a few F1 points behind bigger models while being up to 10–300x cheaper per request to serve.
Huge language models (between 50 billion and 70 billion+ parameters) are still the preferred option when we talk about complex multi-step reasoning, long contexts, and highly open-ended generation. Nevertheless, the vast majority of startup scenarios do not need such models for every single request.
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I’m excited to share AuditFlow AI – AI-powered continuous auditing platform built specifically for Chartered Accountants and audit firms.
CA's practices today are drowning in manual sampling, 40–60 hour audit cycles, talent shortages, and rising client pressure for faster delivery with lower fees. Most frauds and GST/TDS errors go undetected until the assessment stage because traditional methods check only 2–5% of transactions. AuditFlow AI changes that completely: upload any ledger/Excel/CSV and in under 10 seconds it scans 100% of transactions, flags duplicates, round-figure entries, weekend fraud, high-value anomalies, and vendor loops – with plain-English AI explanations for every red flag.
Tech stack: Python, Flask, XGBoost, Isolation Forest, scikit-learn, Bootstrap 5, and trained on 5,000+ synthetic + real-world patterns
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I recently built an AI-powered Mental Health Screening & Clinical Support System designed to assist both individuals and healthcare professionals in early detection and intervention. The system combines patient-reported questionnaires (PHQ-9 & GAD-7) with advanced NLP-based text analysis to assess depression, anxiety, and crisis risk levels. Based on the results, it generates personalized recommendations, safety plans, and professional referral letters, ensuring timely access to the right resources.
This solution addresses a critical real-world problem: the growing gap in mental health care access. Many individuals experience symptoms but delay seeking professional help. Ultimately, it helps reduce the risk of untreated mental health crises and improves overall care outcomes.
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CricVision AI - Advanced Cricket Analytics Dashboard!
CricVision AI is an intelligent cricket analytics platform that leverages machine learning to provide real-time match predictions and comprehensive player insights. The dashboard predicts wicket probability, expected runs per ball, and boundary likelihood using three trained ML models with StandardScaler normalization. It features interactive match scenarios (Powerplay, Middle, and Death overs), over-by-over projections, win probability calculations, and economy rate forecasts. Users can compare players head-to-head, analyze form trends over recent innings, visualize run distribution through wagon wheels, and get AI confidence scores for all predictions - making it a complete solution for cricket enthusiasts and analysts.
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launched TextGenix Enterprise — an AI-powered intelligent document processing system! This platform enhances and transforms documents (PDF, DOCX, TXT, HTML, RTF) with context-aware vocabulary improvements, grammar validation, and industry-specific terminology (legal, medical, financial, technical). It comes with a sleek Gradio-based web interface featuring modern styling, interactive analytics dashboards, and real-time quality metrics like semantic preservation, grammar score, and AI confidence levels.
If you’re looking to build your own AI-powered text/document platform, enhance business workflows with custom NLP models, or integrate analytics-driven AI solutions into your enterprise apps I can help.
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I created WealthWise Agent, a smart personal finance planner designed to craft personalized budget plans and investment strategies. This app takes into account user inputs like salary, expenses, and financial goals, and then uses a Large Language Model (Gemini) to analyze these factors based on the 50/30/20 budgeting rule. It offers a clear step-by-step reasoning log, a detailed JSON-structured financial plan, and an interactive visualization of budget allocation, empowering users to make informed choices to reach their financial goals.
💻 Tech Stack Used:
Frontend/UI: Gradio (custom themed with CSS, Orbitron font)
AI/Logic: Google Gemini (gemini-1.5-flash) with LangChain agents
Data: yFinance API for real-time stock/ETF data, Pandas & NumPy for calculations
Visualization: Plotly Express for interactive charts
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