We have developed a classifier that is capable of interpreting the correct meaning of a transaction accurately. This classifier categorizes each transaction as either eligible or ineligible, providing explanations that include the specific sub-sections under which a transaction is assigned an eligible or ineligible status.

Adopted a suite of AWS tools and services, including Textract for extracting text and data from invoices, Comprehend for natural language processing, SageMaker for machine learning, and GroundTruth for labeling data. Python was used to create custom automation scripts. This automated the extraction and analysis of data from invoices.

We used Python to automate the data collection process, streamline data extraction and reduce human error. We manually scrapped data ensuring no valuable information was missed. In-house scripts were developed that continuously monitored changes in the OEM websites, enabling us to keep the database up-to-date and accurate.

We used GPT-4 APIs, to understand and process legal documents. LangChain was used to establish a secure and efficient blockchain-based platform for managing and verifying the integrity of legal data. Agents, built using advanced AI, were trained to semantically search for information within legal documents, summarize the content, and highlight key terms.

We leveraged open-source software to build a customer ticketing solution that allowed for better tracking and visibility of customer issues and ensured that no query was left unattended. Integrated ML into the platform to automate the categorization and assignment of tickets to the most appropriate customer service agent.

We implemented a robust authentication process for customer verification. Once verified, customers gained access to a wide range of topics they could search. The search included different pages of content, paragraphs, activity matrices, and multiple-choice questions (MCQs) to assess the user's knowledge. We also developed a secure payment gateway (and integrated with Stripe) so that users can purchase tokens to unlock additional content and features.

Defined a comprehensive set of rules and KPIs that are crucial in determining the value of dealership. These rules encompassed various aspects such as historical sales data, market trends, technical indicators and financial health. We also created a sample paragraph that serves as the foundation of the valuation report which was created with information extracted from the dealership's data, ensuring that the generated report is highly descriptive and informative.

We deployed Dolly, a language model, and fine-tuned it using dataset with the assistance of the transformers library. This transition to Dolly significantly reduced the cost of operations while maintaining high accuracy in product analysis and key data extraction.

Databricks Dolly assisted us in efficiently managing and processing the large volumes of data contained within Amazon product reviews. We used Huggingface Transformers, a powerful natural language processing framework, to extract, summarize, and structure the information from these reviews. We generated comprehensive set of FAQs based on the content of reviews and their corresponding answers.

Deployed a pre-trained model capable of generating key features, including pros and cons of products. This model improved the efficiency of the review extraction process. We also deployed a trained GPT using Prompt Engineering techniques to provide attribute-specific details for each product, further enriching the content on the product detail pages.

We employed advanced natural language processing techniques (a trained GPT model) which analyzed and extracted key value pairs from the unstructured dataset. The key value pairs extracted by the model can be used by customers in their product searches.