Langchain

Captain Chatbot was built with the objective of simplifying access to organizational knowledge through conversational AI. We engineered an intelligent backend that understands user intent, retrieves the most relevant documents, and crafts responses using a Large Language Model (LLM). The chatbot is multilingual, supported by language detection and translation of workflows within the pipeline. As a part of our AI development services, we implemented a RAG-based architecture to ensure every response is sourced from actual company knowledge rather than relying on general-purpose language generation. The system includes a knowledge retrieval layer (using embeddings and vector databases), a semantic matching engine, and a response generation module. Additionally, it is supported by asynchronous task execution, API integration layers, and background job handling to ensure consistent performance.

Squareboat’s WhatsApp Chatbot was developed to handle a growing volume of incoming messages related to hiring and sales. The chatbot operates through a structured decision flow, first identifying the nature of the query — whether it concerns careers or sales and then dynamically adjusting the conversation path. The chatbot automates the first level of interaction, significantly reducing the need for manual intervention. This improves the user experience by offering fast and reliable communication on a familiar platform. This is just one example of how chatbots are revolutionizing market research and operational automation across industries.

My Sustainability Score is built to deliver ESG reports that stand up to scrutiny. From initial data ingestion, where we pull in gold-standard documents, internal policies, and online disclosures, to the construction of a custom ESG knowledge base using Neo4j, we ensure no detail is missed. We structured the system by industry, then defined relevant indicators and mapped them to a dynamic questionnaire. Starting with AI integration services, we implemented a pipeline that ingests company documents, scrapes ESG-related data from public sources, and processes everything through an intelligent backend.Drawing from advancements in LLM-powered assistants, the tool transforms this data into structural ESG insights. It then evaluates each question, highlights strengths, detects areas for improvement, and calculates an indicator-wise and overall ESG score. The final report breaks down ESG performance by category, bringing together scores, strengths, gaps, and improvements in an organized structure.