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Case studies across various verticals
such as education, sports, retail etc

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Crowd Counting at US Open

We have partnered with PMY and deployed a crowd-counting visual interface at US Open 2021.

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IOTech

Visual Inference on Edge

We have partnered with a leading IoT edge company to offer visual inferencing solutions.

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Educompass

Building an entire EduTech platform

Lotus Labs helped realize an entrepreneur’s vision of creating an advanced scholastic planner capable of empowering educational planning. Lotus Labs’s data-driven solutions have paved the way for this product to be further implemented on a larger scale.

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CHSI Technologies

Modernizing Web Technology Infrastructure

Learn how Lotus Labs helped modernize CHSI’s web technology

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CHSI Technologies

Implementing the Most Suitable Dashboard and Reporting Tool

Lotus Labs built a custom analytics dashboard

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PMY

Ingress Egress

The application of computer vision and deep learning in an application which allows the user to read a video and count his desired object in the desired direction. This has a phenomenal impact on retail and supply-chain sectors for various use-cases.

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Automating Data Entry with LLM

Automating Data Entry with LLM: A Game Changer

In this article we talk about how data from documents can be extracted in structured format ,this helps in automating the tasks of data entry and can help in making data validation faster.

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CS Wholesale and Grocers

Reinventing Workflow of a Grocery Giant (CSWG)

A Case study about how Lotus Labs partnered with C&S Wholesale Grocers (CSWG) to develop innovative products that streamline and enhance operational efficiency

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Blogs related to some of our POC's
and latest trends in AI tech
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Why “Chat with Your Data” Usually Disappoints — and How to Make It Enterprise-Grade

Most organizations already have the data. What they lack is reliable, fast, and accountable access to it. Ticket queues, stale dashboards, schema sprawl, and ambiguous ask-to-SQL translation conspire to slow decisions and erode trust. This essay distills what we’ve learned from shipping production text-to-SQL across hundreds of tables and thousands of columns, and connects those lessons to what top papers actually found.

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Celebrating 10 Years of Innovation, Autonomy, and Impact at Lotus Labs

Take a trip down memory lane with us. Lotus Labs has completed a decade in the tech world!!. Read more about the journey in this blog

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MCP ”Connecting AI ”: How AI Learns to See, Hear, and Act in the Real World

In a world increasingly powered by artificial intelligence, a new technological concept is quietly revolutionizing how machines interact with live information. It's called a Model Context Protocol (MCP) server, and while the name might sound like something out of a sci-fi movie, the idea behind it is as intuitive and powerful as playing with LEGO bricks. This article will demystify the MCP server, a critical component in the next generation of AI that allows models to access real-time data and perform actions. Using the humble LEGO brick as our guide, we will explore why this technology is essential and walk through how a specialized "Weather Alerts and Forecast MCP Server" bridges the gap between a powerful AI and the constantly changing, real-world environment.

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Natural Language to SQL queries to Business Insights for Dummies with a demo

Unlocking data insights shouldn't require knowing complex SQL. In this beginner-friendly guide, we break down how anyone, even without technical expertise, can turn plain English questions into actionable business insights using LLM. Perfect for analysts, managers, and curious minds looking to bridge the gap between questions and data-driven answers.

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Transforming Clinical Documentation: Building an AI Medical Scribe with LLMs

Welcome to the future of clinical documentation, where real-time audio transcription meets smart summarization to deliver AI-powered medical scribing. In this blog, we walk through how you can build an AI Medical Scribe using fine-tuned models specific to medical data for speech-to-text and LLMs (Large Language Models) like GPT-4.1 for clinical summarization, patient instructions, and more.

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Turning Conversations into Conversions: How AI Can Decode the Voice of Your Customer

Understanding your customers isn’t just a marketing advantage, it’s the foundation of every successful campaign and product management. But truly getting inside their heads? That’s hard. Traditional methods like surveys and analytics certainly help, but some of the most valuable insights often come from a place we overlook: everyday conversations between your sales team and prospective clients.

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A/B Testing for LLMs: Measuring AI Impact Using Business Metrics

A/B testing is a powerful way to compare two versions of an LLM or feature by exposing them to real users and measuring outcomes like accuracy, cost, and satisfaction. It goes beyond offline evaluation to assess real-world impact. This helps businesses make data-driven decisions and ensure their AI investments deliver true value.

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How to create a RAG engine with Local LLM using Ollama

This article explains how to build a fully local RAG system using Ollama, ideal for scenarios where privacy, cost, and latency matter. It covers ingesting documents, generating vector embeddings (e.g., with all-MiniLM), storing them in Chroma, and querying them to provide context to a local LLM like Mistral or LLaMA3. The result is a fast, private, and efficient RAG engine without relying on the cloud.

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Decoding the PDF Maze: A Definitive Guide to Extracting Data from Textual and Scanned Documents with a Real-World Project

PDFs are rich in valuable data like invoices, contracts, and forms, but they’re not machine-friendly. Designed for visual consistency, they make text and table extraction challenging. This article highlights the importance of crafting a tailored PDF data extraction strategy based on your specific use case.

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How Predictive Modeling for Medical Cost Estimation Has Transformed with LLMs, AI, and Agents

Predictive modeling in health insurance has long been a crucial tool for estimating medical costs, helping insurers manage risk, set premiums, and detect fraud. Traditional predictive models relied heavily on statistical techniques and machine learning (ML) algorithms trained on historical claims data. However, the emergence of Large Language Models (LLMs), AI, and autonomous AI agents has revolutionized this process, bringing unparalleled improvements in accuracy, automation, and adaptability.

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Retrieve augment and generate (RAG): A technique to hack LLMs to your command

Retrieve-Augmented Generation (RAG) is a powerful technique for making Large Language Models (LLMs) more effective, accurate, and context-aware by enhancing their responses with external knowledge sources. It combines retrieval (finding relevant text from documents) with generation (LLM text creation), effectively enabling models to respond with updated, accurate information not contained in their original training data.

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A simple guide to understand AI agents

According to a Gartner survey and industry trends, there is no doubt that we are in the era of AI evolution. AI agents are at the forefront of recent developments. Their ability to handle complex tasks enables full-scale automation. They break down tasks like the human mind and address these sub-tasks in a step-by-step, appropriate manner. Industry leaders such as Nvidia, Google, Meta, and Microsoft are betting on their immense potential. Even tech-averse companies are catching up with the trend of using agents to improve their bottom line.

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We transform top organizations with product design & development, data science, machine learning, and artificial intelligence.

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