Creative Spark: Unleash the Power of Generative AI
Transform your interactions with cutting-edge vision and language AI. Explore our revolutionary suite of generative AI solutions, from intuitive chatbots enhancing customer service to legal assistants facilitating informed decisions. Discover the future of search with our AI engine, tailored to understand your needs and provide insightful results. Unleash the full potential of AI to revolutionize both vision and language experiences.
NLP & Large Language Models Expertise
We leverage the power of Natural Language Processing (NLP) and Large Language Models (LLMs) to craft intelligent solutions that understand and interact with human language. Our expertise spans various techniques, including:
- Fine-tuning Pre-trained LLMs: We take advantage of pre-trained giants like BERT, GPT-3, or RoBERTa, customizing them for specific tasks and domains through fine-tuning.
- Leveraging Retriever-Augmenter-Generator (RAG) Models: We utilize RAG models for advanced tasks like question answering or summarization. These models combine retrieval from factual databases with text generation for a comprehensive response.
- Semantic Embeddings with Vector Databases: We employ vector databases like OpenSearch or Pinecone to efficiently store and search high-dimensional semantic representations of text data, enabling tasks like text classification or information retrieval.
This diverse skillset allows us to build robust and tailored NLP solutions, seamlessly integrated with your existing infrastructure, for real-world impact.
Unveiling Our Comprehensive NLP Suite
Our comprehensive NLP Suite equips you with a powerful toolkit to unlock the insights hidden within your text data. Here's a glimpse into some of the core functionalities:
This suite is built upon industry-standard libraries and tools like spaCy, NLTK, TensorFlow, and PyTorch, ensuring flexibility and scalability for your specific needs.
Deployment Strategies
Cloud-Based Infrastructure
Leverage leading cloud platforms for scalable and cost-effective deployment, applicable to any AI model type (NLP or Vision).
API Integration
Integrate functionalities seamlessly into existing applications through well-documented and secure APIs, enabling smooth data exchange and model utilization.
Containerization
Use containerization technologies like Docker to package both NLP and Vision models along with their dependencies, facilitating easy deployment and portability across different environments.
Edge Computing
Deploy models on edge devices closer to the data source, suitable for scenarios requiring low latency, offline functionality, or when there are resource constraints, applicable to both NLP and Vision projects.
This diverse deployment expertise ensures your NLP solution integrates seamlessly into your existing infrastructure, maximizing its accessibility and impact.
Development Process We Follow
Streamlined Project Delivery: From Scoping to Deployment
Scoping and Needs Assessment
Understanding client goals, data availability, and desired outputs like classifications, predictions, and insights.
Data Preprocessing and Feature Engineering
Cleaning, preparing, and structuring data for optimal model performance, with data augmentation for Vision projects.
Model Selection and Training
Choosing the appropriate model architecture based on the task (e.g., classification, segmentation for Vision) and training it with relevant data.
Evaluation and Refinement
Rigorous testing and iteration to ensure accuracy, effectiveness, and generalizability, including metrics for both NLP (F1 score) and Vision (accuracy, precision, recall).
Deployment and Integration
Seamlessly integrating the AI solution into the client's workflow, considering user interface design and data flow.
Ongoing Support and Maintenance
Continuous support, monitoring model performance, and adapting the model to maintain accuracy and address evolving data or requirements.
Case Studies
Knowledge Based QA System
Streamlined RFI Response Workflow for a Fintech Company
Our innovative web application streamlines the process of responding to Requests for Information (RFIs) by leveraging a Knowledge-based Question Answering (QA) System. Utilizing the wealth of knowledge contained within previous RFIs, our system embeds this information into a vector database. When a new RFI is submitted, our QA system retrieves similar QA pairs from the knowledge base, generating accurate and relevant answers efficiently.
Key Features
- Automatically fills RFIs with optimal answers rapidly.
- Seamless integration of Large Language Models (LLMs) for natural language understanding and generation.
- Utilizes RAG methodology for generating comprehensive and context-rich responses.
- Implements various RAG approaches including Naïve RAG, Window Sentence RAG, and Hierarchical RAG to cater to specific data requirements.
- The system was deployed on AWS EC2 servers.
Results
- Reduces RFI screening and filling time from months to a fraction, ensuring timely project execution and adherence to timelines.
- Minimizes errors in information extraction and response generation, thereby improving project quality and client satisfaction while reducing operational costs and boosting productivity.
Tech Stack
Text-2-SQL, Simplifying Data Retrieval
Empowering Non-Technical Users at Netsol
Background
Netsol, a leader in asset finance software for car financing and leasing, faced a challenge. Their clients, often lacking strong SQL expertise, relied on technical teams to generate reports and data queries. This created bottlenecks and slowed down access to crucial business insights. Netsol sought a solution to empower their everyday users to independently retrieve data using natural language, eliminating the need for complex SQL queries.
Solution
We developed a custom Text-to-SQL framework seamlessly integrated with Netsol's legacy ASCENT system. This user-friendly interface allows non-technical users to formulate data queries in plain English. The system translates these queries into optimized SQL, retrieves the relevant information from the ASCENT database, and presents the results in a clear and concise format.
Technical Details
- Tokenize and Encode text query and database schema items such as table and column names, and their foreign-key relationships using an off-the-shelf T5 tokenizer and a pretrained RoBERTa model.
- Train a Schema Item Classifier for the selection of relevant tables and columns.
- Generate the SQL Query skeleton and fill in the column and table names using a T5 decoder.
Outcome
The Text-to-SQL framework empowered Netsol's users to unlock the power of their data independently. This resulted in increased efficiency, improved access to business insights, and a more empowered user base. This successful project demonstrates our expertise in developing innovative AI/ML solutions that bridge the gap between technical capabilities and real-world business needs.
Enterprise Chatbot for Information Technology University
An Enterprise Chatbot designed for ITU University, which meticulously gathers and curates data on faculty, scholarships, and admissions. It emphasizes a comprehensive pipeline with data cleaning and cutting-edge technologies for optimal performance.
Architecture
A robust architecture including a question classifier, retrieval-augmented generation (RAG) module, code generation module, and the powerful GPT-3.5-Turbo-1106 as the Large Language Model (LLM). This setup aims to generate human-like conversational responses.
Features
- Seamless 24/7 Support: Access perfect 24/7 customer care, ensuring continuous assistance and support.
Tech Stack
Demo
This demo is specifically tailored for ITU University, offering a sophisticated chatbot solution designed to efficiently address user queries pertaining to faculty, admissions, and scholarships at the institution.
http://enterprise-chatbot.s3-website-us-east-1.amazonaws.comSimplifying Immigration with AI-powered Chatbot Assistants
Solution
An innovative solution using Large Language Models (LLMs) and Optical Character Recognition (OCR) to streamline immigration form-filling through an interactive chatbot assistant.
Features
- Conversational Guidance: The chatbot guides users conversationally through the application process, asking clear questions and adapting based on responses for a personalized experience.
- Intelligent Form Completion: The LLM automatically populates application sections (e.g., for Asylum and Withholding of Removal) with information gathered during the user's interaction.
Key Benefits
- Addresses errors and inconsistencies often associated with manual form filling.
- The chatbot streamlines the process, significantly reducing the time it takes to complete immigration applications.
- Interactive guidance and access to a legal knowledge base provide users with a better understanding of the application process and their rights.
Impact
The AI-powered chatbot solution provides a valuable tool for simplifying immigration procedures. Early results show a significant increase in user application completion rates and a decrease in errors. This case study demonstrates the potential of AI to democratize access to legal processes and empower individuals to navigate complex systems with greater confidence and efficiency. As we continue to develop and refine the solution, we envision expanding its functionalities to support different immigration pathways and languages, further advancing accessibility and inclusivity in the immigration process.