AI AutoTech Co-pilot

#Automotive

The AI-powered intelligent copilot system is designed for for developers and engineers focuses on revolutionizing the diagnostics and symptoms processing. This project has evolved from a prototype to a scalable, production-grade system, serving leading global clients and demonstrating significant traction.

Natural Language Processing

Impact

The system revolutionizes the way developers and engineers diagnose the problems and process symptoms by providing AI-driven insights and recommendations. This enhances the accuracy and speed of diagnostics, leading to improved client satisfaction and operational efficiency.

Services we provided

Developed a working Proof of Concept that later scaled to production level

Provided a comprehensive API for real-time interaction and testing

Supplied thorough project documentation and maintained a robust codebase

Tech Stack

Python

Pytorch

Tensorflow

SpaCy

NLTK

Scikit-Learn

Streamlit

Elasticsearch

AWS Sagemaker

CDK

EC2

Lambda

ECS

RDS

CloudFormation

Docker

Git

CLIP

Qdrant

Weaviate

OpenSearch

Challenges and Solutions

🧐 Challenges

Development of a RAG (Retrieval-Augmented Generation) chatbot/knowledge base as the foundation of the system’s AI functionalities.
Integration of advanced symptom processing methodologies with state-of-the-art data augmentation using LLMs.

💡 Solutions

Led the deployment of complex data clustering and syntax parsing methodologies which are integral to the AI functionalities, improving data handling and processing capabilities.
Implemented an Intent Classifier and semantic search with augmented customer datasets, significantly refining the system’s accuracy in information retrieval.
Utilized vector databases for efficient data management in high-volume environments, enhancing system responsiveness and reliability.
Implemented Unsupervised image filtering pipeline using Multimodal Transformer(OpenAI CLIP)

Business value

The intelligent copilot system streamlines the pre-diagnostics, greatly reducing time and increasing accuracy.
Enhanced data processing and retrieval capabilities lead to better decision-making and higher productivity.
Scalable and robust architecture ensures reliability across multiple tenants, handling thousands of documents and customer support data effectively.

User flow

1. An engineer enters his problem into the system.

2. The system accesses relevant data from cloud storages(AWS) and vector databases.

3. The RAG model, with the help of LLMs, analyzes the query and related data for comprehensive understanding.

4. The chatbot is integrated into the delivery service's system, streamlining operations.