https://dream.weekday.works/redirectToHiringCopilotExtension/

VectorShift: No-Code Generative AI Automations Platform. Worked on developing an end-to-end RAG automation workflow. Worked on features like search, chatbots, knowledge bases, pipelines that lets users automate various tasks/applications via no-code pipelines powered by AI models
Developed deployment options for search and chatbots feature that lets users query through different knowledge stores leveraging AI.

Co-Founder @ VectorShift(YC S23): The No-Code AI Platform | Ex-McKinsey | Statistics @ Harvard

Albert Mao

Engineering @ VectorShift (YC S23) | GFG Solving for India Regionalist | Full Stack Software Developer | Open Source Contributor

Anchit Sinha

Co-Founder & CEO at VectorShift (YC S23) | Ex-Blackstone | Stat/CS at Harvard

Alex Leonardi

vectorshift.ai

Ethan Kim

VectorShift is an AI automations platform. Teams leverage AI through VectorShift’s no-code or SDK interfaces to search through knowledge bases, generate documents, and deploy chatbots and assistants.

Discover VectorShift - the ultimate solution for creating AI-driven platforms. Intuitive design, powerful features, and seamless data integration for tech innovators.

A personal injury law firm used to have 15 paralegals doing routine research tasks They now leverage AI search through VectorShift to increase productivity and reduce non-billable hours The search engine: 1. Finds similar cases and searches through case documents 2. Identify inconsistencies between documents 3. Identify case risks

A YC edtech company had 50+ course assistants answering student questions on an online forum They built an AI assistant with VectorShift that answers >80% of the questions The assistant was built in <1 hour and: + Answers questions immediately instead of waiting for a human assistant to respond + References and links to lecture notes and transcripts + Livesyncs with new lectures as they are added to Google Drive

VectorShift

A real estate consulting firm spent 1000s of hours answering basic client questions The average response time was 6.3 hours via email They built an AI assistant with VectorShift that now responds instantly The assistant took less than 1 week to build and: + References their proprietary research articles + Pulls live transaction data from their database + Is SOC 2 type 2 compliant with no training by LLM providers on their data

A YC company was quoted $43k for an employee HR copilot They built it in 1 hour instead of hiring additional headcount With VectorShift , employees now receive answers to questions about: 1. Onboarding and offboarding procedures 2. Benefits and HR policies 3. Number of remaining PTO / sick days

A YC series C company spent 10+ hours a week searching through 1000s of customer contracts An SDR built an AI search engine with VectorShift and automated the entire process The tool: - Live-syncs to both Google Drive and Dropbox - Answers search queries based on retrieved contracts - Finds and chats with specific contracts

A recruiting agency receives 15k+ resumes per month Recruiters used to spend 3 hours daily 1) inputing candidate data into a database and 2) searching through resumes to send to clients We automated this entire process on VectorShift in <20 minutes: - Applicants upload their resumes onto a form - Claude 3.5 Sonnet extracts data from the resumes and inputs into a Google sheet - Recruiters use a natural language search tool to find candidates

During YC, people laughed at VectorShift . But here’s an unpopular belief: if you want to build a successful startup, work on DUMB ideas. VectorShift is a no-code platform to help anyone build AI workflows. Essentially, a “horizontal” solution to build custom AI tools. However, in our batch, “vertical” AI solutions were all the rage. Our batchmates (and a lot of VCs) told us: “Enterprises are already using vertical AI agents to replace entire teams” “Every unicorn SaaS company will have a vertical AI unicorn equivalent” In fact, YC just made a video titled: Vertical AI Agents Could be 10x bigger than SaaS. However, Harj, our YC group partner defended our approach in the video (clip below): “Enterprises are a little unsure about exactly what agents they need and one approach I've seen is letting Enterprises spin up custom agents. It's something I've seen from one of my companies called VectorShift that I funded about a year ago.” We couldn’t agree more (and thanks for the shoutout Harj!) When I look back at recent startups that have succeeded, I’m reminded of the story of Scale AI. When Scale was at YC S16, their batch mates laughed at their product entitled “Human API.” People said that human-labeled training data wasn’t a “technical” solution. That it was just “Filipinos as a service.” However, those people didn’t realize that the buyers of training data don’t care that your solution is not “technical.” They just want the data. Today, Scale is worth $14B and growing. Similarly, what people don’t understand is that most human workflows in a company are custom and buyers of LLM software just want them automated. Custom workflows need custom solutions. We think that most vertical SaaS solutions will be replaced by custom AI workflows that you can build on your own, because it is faster, cheaper, and more performant.

A YC ed-tech company used to manually input data from 15k+ student forms 20+ full time staff used to spend >50% of their day doing this We built a workflow in <2 hours that automated the entire process: - Staff now uploads the forms to VectorShift - GPT4 mini and advanced OCR extracts student information (even from messy handwriting) - VectorShift adds data directly into their ERP system of record

Enterprise AI solutions are 99% showmanship – cool internal demos without real ROI. That is the cold hard truth. <10% of enterprises have GenAI solutions in production. Most enterprises have: 1. Appointed a head of AI 2. Identified use cases across business units 3. Built proof of concepts So why are enterprises stuck before moving to production? - Uncertainty around AI investments: Companies are concerned about the implications of investing in AI solutions that may not deliver the expected results (or worse, result in negative reputational risks). - Too many choices: Firms are flooded with options and are not sure what to choose. Should I use an out of the box solution? Should I build the solution in-house? Should I outsource to a vendor? - Complex decision making structures: I have seen many enterprises form multiple committees that complicate decision-making processes, leading to delays and indecision. We built VectorShift to help enterprises move from proof of concept to production with confidence. Feel free to reach out - happy to share best practices: albert@vectorshift.ai

Last year, we got into YC and raised $3m. But when we launched VectorShift , we only got 3 signups. No one cared. People often think that raising money and getting into a prestigious accelerator are the hallmarks of success as a founder. It is far from it. There are millions of tactical steps in between. This past year: I did sales I did customer support I did marketing I did interviewing I did product QA I did client deployments I did legal and compliance I did bookkeeping And more Many people think being a founder is about raising money, setting the high-level strategy, and giving the team instructions from an ivory tower. The reality is doing whatever it takes for the customer to have a better experience and to solve their pain points. 10+ launches later (and many more failed ones), we now have thousands of paying customers and tens of thousands of users. There are still millions of more steps left, but we are excited to continue on this journey with our customers.

Added support for LangChain v0.2 in the LangChain Python repository, LangChain-JS JavaScript repository, and LangChain-IBM, which contains packages for IBM integrations with LangChain. Introduced support for NVIDIA AI endpoints in LangChain v0.2 by implementing an in-memory database using RunnableWithMessageHistory, which manages chat message history for another Runnable. Implemented RunnableSequence, which allows two Runnables to be chained together, replacing LLMChain used in v0.1. Added a callback handler for streaming responses and an AgentExecutor with a React agent executor, which manages state defined by a list of messages. Utilized {pull} from LangSmith to extract prompts directly from the LangChain hub.

Engineering @ LangChain

Alex Kira

Helping organizations deploy their LLM apps from prototype to production.

Charles Bernoskie

Building LangChain

Julia Schottenstein

Software and ML

Eugene Yurtsev

Sequoia Capital

We're on a mission to make it easy to build the LLM apps of tomorrow, today. We build products that enable developers to go from an idea to working code in an afternoon and in the hands of users in days or weeks. We’re humbled to support over 50k companies who choose to build with LangChain. And we built LangSmith to support all stages of the AI engineering lifecycle, to get applications into production faster.

Another year, another leap in LLM app development! 🪄 Our new 2024 LangChain State of AI report uncovers key trends in how developers are building LLM apps. In 2024, we’ve seen a shift from retrieval workflows to multi-step, agentic applications. 🤖 Open-source models are also gaining traction as devs seek more customizable infrastructure. Here's where the AI ecosystem is headed, based on data from LangSmith.👇 Read the full report: https://lnkd.in/g5yd92QW

LangChain

🌱 GreenMe: AI Eco-Assistant Meet your personal AI-powered sustainability guide that analyzes your lifestyle to reduce carbon footprint. Built with LangChain and Cohere LLM, GreenMe delivers smart, tailored recommendations through an intuitive interface. Ready to make a positive environmental impact? Check out the project 🌍 https://lnkd.in/gDha6fej

❓What do Replit, Uber, LinkedIn, Elastic and AppFolio all have in common? They all built, launched, and talked about LangGraph agents in production in 2024 As did Unify, Rexera, OpenRecovery, Komodo Health, Airtop, Tradestack & Athena Intelligence As the year draws to a close, we highlighted all their stories and more. Building agents is still not easy - but with LangGraph it's possible Read more here: https://lnkd.in/gKQC5Y9z

🏆Top 5 LangGraph Agents in Production 2024 #1: Replit While "agents" are the buzzword of the moment, agentic apps built with LangGraph have been in production throughout 2024. Among those who shared insights publicly, we're doing a countdown of our 5 favorite. Winner: Replit Replit released their agent this fall to pretty widespread adoption. When we've talked with them about the agent they've emphasized human-in-the-loop and multi-agent setup - both things we think will be key for agents in the future. We've chatted with them a few times about Replit agent: Fireside: https://lnkd.in/eSbM4feZ Blog: https://lnkd.in/gEHuJBmp

Get started with our new Introduction to LangSmith course over the holiday break! 🎄 Dive in and learn the essentials of LangSmith — our platform purpose-built for LLM application development, whether you're building with LangChain or not. In this course, you’ll learn how to: 🔄 Quickly iterate and refine prompts with LangSmith’s prompt engineering tools 📐 Evaluate and improve your app’s performance using best practices for testing LLM applications 📈 Monitor and debug real user issues in production with LangSmith’s tracing and observability Enroll for free ➡️ academy.langchain.com Request a LangChain Academy training for your team ➡️ https://lnkd.in/gmUC6D2V

🤖How to add persistent memory using PostgreSQL to LangGraph ReAct agent This tutorial adds PostgreSQL persistent memory to the LangGraph ReAct agent (using Tavily and Anthropic) Check it out for a simple example of how memory works https://lnkd.in/gTEGrp3m

🏆Top 5 LangGraph Agents in Production 2024 #2: Elastic While "agents" are the buzzword of the moment, agentic apps built with LangGraph have been in production throughout 2024. Among those who shared insights publicly, we're doing a countdown of our 5 favorite. Runner up: Elastic Elastic was one of the first companies in my mind to launch an AI "agent". We covered their AI assistant in late January of 2024 - right at the start of the year. Back then it was using LangChain, but as they added features they moved onto LangGraph. Excited to see the continued development of AI assistant! Initial case study: https://lnkd.in/gZTunt-d Follow up details: https://lnkd.in/gTRDnTa5

🏆Top 5 LangGraph Agents in Production 2024 #3: LinkedIn While "agents" are the buzzword of the moment, agentic apps built with LangGraph have been in production throughout 2024. Among those who shared insights publicly, we're doing a countdown of our 5 favorite. Next up, number 3: LinkedIn One of the big use cases for LLMs is in making data more accessible to everyone. LinkedIn recently rolled out SQL Bot, an AI-powered assistant internally This internal tool transforms natural language questions into SQL: it finds the right tables, writes queries, fixes errors, and enables employees across functions to independently access the data insights they need under the appropriate permissions. Behind the scenes, SQL Bot is a multi-agent system built on top of LangChain and LangGraph. https://lnkd.in/gB_9X4D4

↔️Integuru An AI agent that generates integration code by reverse-engineering platforms' internal APIs You use create_har.py to generate a file containing all browser network requests The agent outputs Python code to perform the desired action. https://lnkd.in/eVpRdRu2

🏆Top 5 LangGraph Agents in Production 2024 #4: AppFolio While "agents" are the buzzword of the moment, agentic apps built with LangGraph have been in production throughout 2024. Among those who shared insights publicly, we're doing a countdown of our 5 favorite. Continuing with number four: AppFolio AppFolio's AI-powered copilot Realm-X has saved property managers over 10 hours per week. Realm-X provides a conversational interface that helps users understand the state of their business, get help, and execute actions in bulk – whether it’s querying information, sending messages, or scheduling actions related to residents, vendors, units, bills, or work orders and many more They shared their journey in building this with us last month. They needed a controllable agent architecture to make this a reality and so chose LangGraph https://lnkd.in/gv2DaVt6

I led a team of seven members, working closely with a USA-based CTO to design and implement both high-level architecture and low-level system designs. This collaborative effort focused on creating scalable, ensuring that the design was flexible enough to integrate additional tools seamlessly—similar to how an operating system allows various applications to run on it.

As part of the development process, I successfully set up and automated over 31 tools and pipelines, including API routes, YAML configurations, and deployment automation. Each of these tools was hosted in its own custom Docker environment, ensuring consistency and isolation across various stages of development. This automation not only significantly improved deployment times but also minimized human error throughout the entire development lifecycle.

For the frontend, I utilized EJS, JavaScript, and Tailwind CSS, ensuring responsive and efficient user interfaces. The combination of these technologies allowed us to deliver a smooth and dynamic user experience, optimized for performance.

I implemented multi-stage Docker workflows to host and manage various microservices and to streamline the deployment process. This setup helped maintain clean, reproducible environments for development, testing, and production.

As we were operating on bare metal, all our systems were hosted on custom HPCs. To scale the application and manage the jobs submitted by various developers and scientists across different clusters, we set up the SLURM Workload Manager. This allowed us to efficiently handle job submissions, allocate resources, and ensure optimal job execution across the entire infrastructure.

Finally, I established a robust CI/CD pipeline using GitHub Actions, which automated the testing, building, and deployment processes. This approach ensured rapid feedback loops and consistent delivery of quality code, streamlining the release cycle and minimizing downtime.

Founder/CTO of Aganitha | Transforming BioPharma R&D with advances in computing | building cross functional teams

Ramarao Kanneganti

Founder - Aganitha | Tech Business Exec | Transforming BioPharma R&D with Comp. Bio/Chem and AI | Translational Omics | Generative modeling for de…

Prasad Chodavarapu

AI/ML | Digital Transformation | Business Intelligence | Data (Big/small) Management

Srinivas Tumuluri

Full Stack Data Scientist at Aganitha Cognitive Solutions

Jigar Patel

At Aganitha, we partner with global bio-pharma to complement the traditional lab based approaches with in-silico approaches to research and development. We develop and deploy AI, data and cloud powered solutions that efficiently tackle the complexity, scale and richness of modern biopharma R&D domains. We adopt multi-disciplinary approaches blending and translating the latest knowledge in Biology, Chemistry, Computing and BioPharma Business.

Aganitha develops deploy AI, data, and cloud-powered solutions.

Aganitha

🦠Learning from and improving upon high-throughput screens for protein fitness with Generative AI: Application to BBB-crossing AAV design At Aganitha, we are applying deep tech to learn from & build on the datasets generated by deep science. Today, we are happy to share a preprint describing how we used #GenAI to improve design of blood-brain-barrier ( #BBB ) penetrating #AAV viral vectors for gene therapies in brain disorders. 📊 High-throughput directed evolution (DE) for protein fitness optimization is only able to explore a minuscule portion of the large design space. E.g., exploring the best 7-mer insert into #AAV capsid sequence by testing just 100 million nucleotide sequences gives a coverage of just 0.04% of the possible design space. 🔬AAV optimization fits into a broader class of protein fitness optimization problems. Optimizing enzymes for catalytic activity as well as expression, stability, solubility & other developability related properties is a common use case for protein fitness optimization methods. 💡Previous attempts at application of #AI to #AAV variant generation trained predictive models on carefully selected experimental datasets & performed large screens of randomly generated sequences or sequences generated through saturation mutagenesis at select positions. 🚀 We designed a more direct approach: Autograd based maximisation of protein fitness ( #AutoMaxProFit ). 🔍 Using data from directed evolution, we fine-tuned a #Transformer based protein language model to predict fitness scores, performed gradient ascent in the embedding space & decoded optimal sequence. Loss function for the optimizer combined predicted fitness score along with a penalty for straying outside the embeddings sub-space covered by the training dataset. 🧬 Molecular dynamics ( #MD ) simulations showed 4x improvement in binding to LY6A receptors that were previously shown to be targets of #BBB penetrant variants in mice models used by DE experiments. For penetrants, the N-terminal of the LY6A receptor engages extensively with trimeric #AAV9 capsid protein. Conversely, for non-penetrants, the trimeric AAV9 interacts only with loop VIII from one of the three chains in the trimer. 📄 Preprint is at https://lnkd.in/gaGZcN9Z 🌐 To know more about Aganitha's work on gene therapies, visit https://lnkd.in/g6sGAuUb

🧬 Disease Biomarker and Target Insights Platform & Services (DBTIPS™) Like in “Water, water everywhere and not a drop to drink”, it is not incorrect to say “Data, data everywhere and not a drug in sight” given the estimated median time of 25 years from genetic association discovery to drug approval and 6000+ diseases with known molecular basis but no approved treatments. Tremendous advances in deep science are generating a high volume of multi-scale, multi-modal data on health and disease. The key bottleneck now is in the expertise needed to distill molecular data into insights needed for accurate target identification, assessment and validation. We at Aganitha.ai are doing our bit to bridge this gap with a combination of platforms and services. Today, we announce the availability of Aganitha Disease Biomarker and Target Insights Platform & Services (DBTIPS™) with the following components. 🔬Disease dossiers AI generated disease dossiers collating latest literature & data on: - Disease pathways - Genomic associations - Diagnostic biomarkers - Targets in trials - Animal and organoid models with built-in LLM support enabling contextual conversations, interrogating and analysing of knowledge & data presented in the dossiers. 🎯 Target & biomarker dossiers AI generated target & biomarker dossiers collating latest literature & data on: - Target/biomarker roles in disease pathways as evidenced by human, model organism or organoid studies - Genomic evidence linking target/biomarker to disease phenotypes - Market intelligence covering prior clinical studies & opinion from KOLs - Structural and functional data enabling target tractability assessments - Safety analyses using real-world data, methods from evolutionary biology, structural biology & genomics, and perturbation study results. Like in disease dossiers, our target and biomarker dossiers come with built-in LLM support for contextual interrogation and analysis of knowledge & data presented in the dossiers 🛠️ Comprehensive service offerings & solutions for: - Hypothesis generation & validation for indication expansion & drug repurposing - Biomarker identification for patient stratification & smart cohort definition in trial design - Analysis and interpretation of data from genomics, transcriptomics, proteomics and organoid research, with robust and scalable omics pipelining on HPC/Cloud - AI & MD powered structural biology services for target elucidation and pocket/epitope selection - Gen AI powered therapeutic design and optimization for small molecules and biologics To know more and request a demo, visit https://lnkd.in/gSy3BrJm #DrugDiscovery #Biomarkers #PrecisionMedicine #AI #Biopharma

Meet Manasvi Sharma in the poster session at Rare Genetic Diseases Research Summit (REDRESS) to know how Aganitha.ai is combining Deep science and Deep tech for viral vector engineering in advanced cell and gene therapies. Learn about AI powered optimization of capsids for tropism, analysis of host-vector fusion events and troubleshooting of quality issues in capsid synthesis. #ACGT #GenAI #REDRESS2024

🚀 Covalent binder design with Gen AI & FBDD Accelerate your covalent binder discovery for challenging protein targets with Aganitha. In our latest blog, we explain: 🔑 Key design considerations for covalent binders 🔎 Why traditional structure-based drug design falls short for covalent molecules. 🪄 Generative AI and Fragment-Based Drug Design (FBDD) powered covalent binder discovery workflow 🔗 Read the full blog at https://lnkd.in/gcYWdKw2 #covalentinhibitors #FBDD #SBDD #biopharma #GenAI

🌟AI catalyzed enzyme engineering (ACE™) Happy to announce AI catalysed enzyme engineering suite of solutions addressing both therapeutic discovery as well as biocatalysis needs. 🔬 Explore vast sequence space with GenAI augmenting directed evolution 💡 Derive insights from structure modeling and molecular dynamics ⚡ Characterize & optimize properties such as thermal stability & solubility Know more at https://lnkd.in/gNjuB2Zc #Enzyme #Biocatalysis #ERT #Biopharma #GenAI #AI #MD

🔍 De Novo PROTAC Design with Gen AI & FBDD PROTACs are opening new avenues for targeting previously “undruggable” targets by harnessing the cell’s own protein degradation machinery. We're excited to share our solution for De Novo PROTAC Design with Gen AI & FBDD! Our solution brings together: 🔬 Advanced Structural Modeling: Precise modeling of the Protein of Interest (POI) and E3 ligase complex, ensuring a solid foundation for PROTAC design. ⚡High-Affinity Ligand Screening: GPU-accelerated virtual screening platform rapidly identifies high-affinity ligands for both the POI and E3 ligase. 🧪Synthesizable Linkers: Leveraging a library of laboratory tested synthesizable linkers. 🔎 Comprehensive Hit Identification: Automated PROTAC construction and evaluation process concurrently addressing binding affinity and degradability of the ternary complex with multi-parameter optimization. 🛠️ Advanced Hit-to-Lead Optimization: Deep learning-based degradability prediction to optimize PROTAC architecture, and MD simulations to refine candidates, ensuring optimal ternary complex formation and stability. 🔗 Know more from our blog post at https://lnkd.in/gWawS2UZ #Biopharma #Biotech #PROTAC #GenAI #FBDD

🧠 AI-powered solutions for Organoid research 🚀 Organoids help model complex diseases, identify new targets, and test therapeutic candidates. Organoid studies produce a torrent of imaging, omics, and physiological data. 🧬 We at Aganitha are using AI to drive faster processing and analysis of these data streams. Our solutions enable organoid model development, validation, disease modeling, drug efficacy testing, and toxicity evaluation. 🔍 If you are a researcher leveraging organoids, you can know more about what AI can do for you at https://lnkd.in/gmG__QSD #Organoids #DrugDiscovery #AI 💡

Oxford Nanopore Technologies

DNAnexus

🧬 Long-Read Sequencing (LRS), with Oxford Nanopore Technologies (ONT), is proving useful for analyzing tough to nail down quality issues in biosynthesis. 🧬 DNAnexus is enabling user-friendly access to NGS pipelines that process ONT LRS data. 🔗 More about these in our blog at https://lnkd.in/g7SY5zZk #LRS #ONT #DNAnexus #Biosynthesis #NGS #Genomics

CSIR-IGIB

Manasvi Sharma , Debleena Guin and Neha Singhal are at CSIR-IGIB today, sharing with fellow genomics researchers, our work at Aganitha.ai bridging DeepScience and DeepTech to accelerate disease research, therapeutic design and clinical affairs.

Krishna Harsha Adusumilli

Last week, my team at Aganitha and I participated in an industry roundtable on "Accelerating drug discovery and development". A question that came our way was: How is generative AI powered drug design different from traditional drug design? There are of course, many differences, and a key one our team emphasized is the ability to factor-in safety and developability concerns upfront during design and not as an afterthought. There was a second key difference our team emphasized. When embarking on generative AI powered drug design exercises, or for that matter, any in silico drug design exercise, prior understanding of target structure and function becomes very important. Given that we are in the era of AlphaFold-like AI powered structure prediction models, this prerequisite does not seem too onerous but like for any step in drug discovery - nothing is as simple as it first seems. 🔗 We wrote more about this topic in a blog. Check it out at

Developed a dynamic web application that bridges the gap between investors and promising startups, enabling meaningful connections through intelligent discovery.
- Leveraged OpenAI's embeddings to semantically integrate startup data, storing it as high-dimensional vectors in Pinecone DB for fast, context-aware retrieval. To enhance user interaction, integrated Langchain parser tools for structured and efficient communication with GPT.
- For efficient data retrieval, utilized AWS S3 for scalable file storage and implemented Drizzle ORM to support deployment over an edge network, ensuring low-latency performance and faster runtime execution.
- The platform was built entirely with Next.js 13 and TypeScript, offering SEO-friendly server-side rendering with a reduced client-side JavaScript bundle.

VentureCopilot is a web-based AI-first platform for Corporate BD and CVC professionals. Using custom and fine-tuned LLMs, vector databases, and machine learning, it converts unstructured data into structured databases and enables data version management. The platform automates complex workflows, freeing investors to focus on intelligent decision-making. Integrated with multiple data sources like transcripts, documents, Linkedin, and emails it provides contextually relevant automation and operates 24/7 for increased operational efficiency. VentureCopilot is more than a tool; it's an ecosystem empowering humans to make informed buy-side decisions. It heralds the future of work by reducing manual labor, elevating productivity, and streamlining database construction.

Indian Institute of Information Technology Vadodara

Cognam Technologies Pvt. Ltd.

IIITV Finance & Consulting Club

Amazon

Microsoft Learn Student Ambassador IIITV Chapter

Engineering

Operations

Education

Information Technology

Research

Computer Science

Computational Science

Computer Engineering

PHYSICAL SCIENCES

Technologies: NextJS, TypeScript, RadixUI, Turborepo, Prisma, mySQL
Developed a high-performance meta search engine for travel affiliate websites, integrating with Skyscanner's API to retrieve real-time flight data. The entire user interface was crafted using Next.js,  leveraging Server-Side Rendering (SSR) for improved SEO and significantly reducing the client side bundle being shipped to the user resulting in faster load times and a smoother UX.

Implemented Prisma ORM for efficient and secure interaction with a MySQL database, optimizing data management and query performance. The infrastructure was built on Turborepo, a powerful monorepo tool that enhanced scalability, enabled remote caching, and significantly reduced CI/CD pipeline times.

Achieved a perfect SEO score with Lighthouse (100), ensuring top-tier visibility and performance. Notable performance metrics include a First Contentful Paint (FCP) of just 0.3 seconds and a Total Blocking Time (TBT) of 0 ms, delivering an exceptionally fast and smooth user experience.

Assistant Professor at Indian Institute of Information Technology, Vadodara

Pratik Shah

Assistant Professor at IIIT Vadodara

Naveen Kumar

Assistant Professor @ CSE_IIIT Vadodara

Pramit Mazumdar

Teacher and Researcher of Computer Science and Engineering

Gaurav Pareek

Indian Institute of Information Technology Vadodara (IIIT-V) is set up by the Ministry of Education (Earlier called the Human Resource Development (MHRD)), Government Of India under Public Private Partnership (PPP) model. The partners in this project are Government Of India, Government of Gujarat, Gujarat Energy Research and Management Institute, Gujarat State Fertilizers and Chemicals and Tata Consultancy Services. The Institute was established in the year 2013. DA-IICT Gandhinagar has been entrusted the responsibility of mentoring IIIT Vadodara. At present, IIIT-V is operating from its temporary premises at Government Engineering College (Gandhinagar) till the time its new campus is made operational at Vadodara, Gujarat. Indian Institute of Information Technology, Vadodara - is established to cater to the emerging needs and opportunities and meet economic challenges being thrown by the incessant IT revolution. It is setup as a Not for Profit Public Private Partnership (PPP) model. It is a major initiative taken by Government of Gujarat to harness the multifarious dimension of IT Industry. The Government of Gujarat has lent support to the Institute by providing 50 acres of land and Building. It is aimed at providing future candidates who shall become a precursor in the field of IT. A Governing Council consisting of distinguished people from academia, industry and government preside over the governance of the institute. The Institute aims at evolving strong research programmes in a plethora of areas where IT shall provide a common link with profound emphasis on development of technology and its application.

IIIT Vadodara's 6th Convocation Ceremony We are thrilled to celebrate the accomplishments of the graduating batch of 2024! The ceremony was held on 17th December 2024 at the prestigious Malala Auditorium, Diu. Degrees Conferred: Doctor of Philosophy (PhD): 2 Master of Technology (MTech): 11 Bachelor of Technology (BTech): 305 Total Degrees Awarded: 318 The event was graced by the esteemed presence of: Shri Amit Singla, IAS (Advisor to Administrator, Dadra and Nagar Haveli, Daman & Diu) as Chief Guest Prof. Prateek Kishore (Distinguished Scientist & Director General, Armament & Combat Engineering Systems) as Guest of Honour Heartfelt congratulations to all graduates! May your future be as bright as your achievements. #IIITVadodara #Convocation2024 #GraduationDay

1st Meeting on Promoting Educational Excellence through School-Institute Synergy IIIT Vadodara (Diu Campus) hosted its first meeting to connect 25 schools in and around Diu (UT) with Higher Educational Institutes across the country. The initiative aims to empower students with vocational education and enhance employability. Highlights: Prof. Dharmendra Singh, Director, IIIT Vadodara, emphasized the theme “Sustainability through Education” while discussing: The need to revitalize school education post-COVID. Addressing information overload in the digital era. Connecting schools with Higher Educational Institutes nationwide. #EducationForAll #IIITVadodara #SchoolInstituteSynergy #SustainabilityThroughEducation

IIIT Vadodara (presently located at Gandhinagar, Gujarat and at International Campus Diu (U.T.)) invites applications from the Indian Nationals for filling up the faculty positions at Associate Professor, Assistant Professor Grade-I & II levels. The applications required in the broad area of Computer Science & Engineering, Electronics & Communication Engineering, Physics, Mathematics, Probability & Statistics, Linguistics, Social Sciences, Economics, Psychology. Last date of receipt of online application is 15 January, 2025 and hard copy of the same to Institute address by 22 January, 2025. Interested candidates may download the Qualifications, Experience, General Details, Application Form and Formats of various certificates from: https://lnkd.in/gYDv4SKK .

GLOBAL FINETECH CO., LTD.

Exciting News! GLOBAL FINETECH CO., LTD. Process at IIITV

IIIT Vadodara invites applications for PhD Program (Winter Semester 2024-25) in CSE, ECE, Science & Humanities. Open for both Full-time and Part-time candidates. ( https://lnkd.in/gaHKHHkE ) 🌟 Pursue cutting-edge research with us! Apply Now! 🔗 #PhD #IIITVadodara #Research #CSE #ECE #Humanities #PhDAdmissions #HigherEducation #WinterIntake

Oracle

Exciting News! Four talented students from IIITV have secured prestigious Job opportunities at Oracle . A proud achievement for our institute!

MathWorks

Exciting News! Eight talented students from IIITV have secured prestigious internship-to-job opportunities at MathWorks . A proud achievement for our institute!

Recruitment Alert: Applications are invited for TPO Position, IIIT Vadodara. Link to Apply For: https://lnkd.in/dSgrXXgJ

Recruitment Alert: Applications are invited for TPO Position, IIIT Vadodara.

IIIT | VectorShift(YCS23) | GenAI | Automations | LangChain | Healthcare Tech | NextJS | ReactJS | Python | RAG | TypeScript | NodeJS | Cloud Solutions

Srijan Dubey

Full Stack Developer with over 5 years of experience. Currently contributing at LangChain and working with the team based out of New York at VectorShift(YCS23). A hunger to help startups go from 1 to 100.