Amit Gawade

Project: Vendor RFQ Document Builder – Procurement Automation

• Domain: Procurement Automation | Technologies: Azure Function Apps, Azure OpenAI, Python, Azure DevOps (CI/CD)
• Built an intelligent system to automate RFQ (Request for Quotation) document generation for vendors which will reduce 50% of their workload. The solution ingests customer requirement documents, extracts key information such as product specs, quantities, delivery timelines, and terms using Azure OpenAI, and auto-generates structured Vendor RFQ templates. Deployed scalable and event-driven pipelines using Azure Function Apps, with end-to-end automation via CI/CD on Azure DevOps.

Project: Email Case Automation

• Domain: Customer Support
• Technologies: Azure Function Apps, LangGraph Multi-Agent), Azure OpenAI, Azure AI Search, Python, SAP, SFDC, CI/CD Built an intelligent automation pipeline to process incoming SFDC cases via Azure. Leveraged Azure Function Apps to receive and handle SFDC payloads, using a LangGraph multi-agent framework (powered by Azure OpenAI) to extract sentiment, intent, and case reason from customer emails. Integrated SAP to fetch and display purchase order PO and sales order SO data based on extracted context. Ensured seamless deployment and scalability via CI/CD pipelines. Highlight your accomplishments, using numbers if possible

Project: Advisor GPT SFDC Case Intelligence Chatbot

• Domain: CRM Support Automation
• Technologies: Azure AI Search, Azure OpenAI, Hugging Face LLMs, LangGraph Multi-Agent), Azure Function Apps, Azure App Services, Python, SFDC Developed Advisor GPT, an intelligent chatbot embedded in SFDC, designed to assist support agents by summarizing active cases, extracting context from the current page, and enabling conversational Q&A on open or other related cases. Powered by multi-agent LangGraph architecture, it offers actionable insights, sentiment detection, case reasoning, and suggested resolutions to help agents accelerate case closure.

Project : Smart ChatAgent (Banking and Finance)

• The RAG based project focused on utilizing advanced technologies like Azure AI Search and Azure OpenAI to boost the efficiency and effectiveness of data retrieval and analysis processes within the domain of share brokering. The primary objective was to empower support agents to swiftly retrieve pertinent information. Leveraging natural language processing (NLP) techniques, Generative AI capabilities the project aimed to augment search functionality and generate insightful results tailored to the specific needs of share broker support agents.

Project: Client- Impaxx (Healthcare Insurance) Generative AI based NER for MSA, and premium suggestions.

• This project aimed at improving medical set aside (MSA) processes and determining medical insurance premiums using generative AI-based Named Entity Recognition (NER). We utilized advanced Generative AI techniques to extract entities from medical health records, including medications, medical equipment, ICD codes, CPT codes, procedures, prescriptions, customer names, dates of hospital visits, and diagnoses etc. This data was crucial for MSA purposes and played a significant role in assessing medical insurance premiums. To streamline the process, we employed preprocessing techniques and leveraged Form Recognizer to extract information from medical documents in PDF formats.

Project: Loss Reporting (Insurance)

• As part of the Loss Reporting project for an insurance company, I spearheaded the development of a solution to automate the extraction of critical information from loss reports. Leveraging technologies such as Pytesseract and Azure OCR, our goal was to extract key details including indemnity claims, medical claims, number of claims, total loss amount, loss reporting date, and claim information. The extracted data was then stored in an Excel format and seamlessly populated onto the front-end interface for easy access and analysis. Throughout the project lifecycle, effective communication with the frontend team ensured smooth integration and user-friendly interface design.

Project : Mental Health Assistant

• Due to Societal Stigma around mental health, the expensive nature of therapy, the inaccessibility of mental health practitioners lacks awareness of symptoms, and the need for therapy, a huge number of people are not diagnosed and treated in time for behavioral problems. The objective of this project is to develop easy access, an interactive platform that can help individuals experiencing anxiety or mild to moderate depression to feel calmer and soothe them down by using AI techniques like text/ speech analysis, sentiment analysis, self-assessment, grounding activities, and other interventions.

Project : Predicting soil radon gas concentration and anomalies for early earthquake warning system.

• Several studies have shown that unusual spikes in soil radon gas concentration are associated with earthquakes. Using delegated regression and LSTM we are trying to predict anomalies that trigger the earthquakes by forecasting radon values using meteorological parameters.

Involved in designing the user experience interface UI strategy, UI requirements, converting findings into UI designs.

• Support existing applications as well as develop new solutions for expanding the customer base.
• Designed Database for the application.
• Developing a new application as per the requirement of the institute.