Generative AI Powered Application Services / LLM Models

Looking for GEN AI-Powered
Development Services?

Launch your New/Existing Product or Application with GPT Model Integration – Our Expert AI team can help you Develop Cutting-Edge Solutions using Open-Source models and OpenAI’s GPT3/GPT4 API services.

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Our GPT-Powered Services

New Application Development - GPT3,4

Transform Your Idea into a Sophisticated Product with Our Expert AI Engineers.
We Utilize GPT-3 Model and APIs to Develop Customized Products and Applications.
Our Services Include Prototyping, UI/UX Designing, App Development, DevOps, Quality Assurance, and Cloud Engineering.

Existing Application Integrations - GPT3,4

Integrate the Power of OpenAI’s GPT3 Model into Your Existing Application - Let’s Talk and Improve Your Product with Amazing New Features.
GPT3 Integration is Highly Effective for Chatbots, Content Creation Tools, and Question-Answering Systems.

Enhance Applications NLP with GPT plugins

We offer custom NLP Services to extract valuable insights from unstructured data and develop tailored features to meet your specific needs - such as Data Annotation to Fine-Tune GPT-3 for your product’s use cases

Are you seeking a solution for Seamless Insight Retrieval through a Smart Document Analysis Assistant?

Supercharge Your Insights with elsAi: The Ultimate Text Document Intelligence Companion! Elevate Your Document Analysis and Transform Data into Actionable Knowledge. Try elsAi Today for Effortless Answers.

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Our GPT Service offerings

Natural Language Processing (NLP)

Developing intelligent applications that can understand and respond to user queries using natural language. This includes chatbots, virtual assistants, and voice-enabled applications.

Content Generation and Curation

Developing tools that use GPT models to create high-quality and unique content for your applications, such as blog posts, social media updates, product descriptions, and more.

Recommendation Engines

Developing recommendation engines that use GPT models to offer personalized and relevant recommendations to users based on their interests and behavior.

Predictive Analytics and Insights

Developing tools that use GPT models to analyze data and provide actionable insights and predictions for decision-making and business strategy.

E-commerce Solutions

Developing e-commerce solutions that use GPT models to offer personalized product recommendations, search results, and more to increase user engagement and drive sales.

Language Translation

Developing language translation tools that use GPT models to provide accurate and natural translations of written or spoken content.

Sentiment Analysis

Developing sentiment analysis tools that use GPT models to analyze user feedback and social media data to determine the overall sentiment towards a product, brand, or topic.

Data Analysis and Mining

Developing tools that use GPT models to extract and analyze data from large datasets to uncover patterns, insights, and trends.

Our GPT Webinar

Success Stories

GPT-Powered HR Portal

GPT-Powered ConsTech

GPT-Powered LogiTech

GPT-Powered HR Portal

Our HRTech solution, powered by GPT, offers a range of innovative features including resume parsing and matching with job descriptions, content improvement suggestions to enhance the resume's fitment with the job description, and automated cover letter drafting based on the job description and resume

Problem

Solution

Impact

Problem Statement

High volume of resumes: Recruiters may receive hundreds or even thousands of resumes for a single job opening, which can be overwhelming and time-consuming to review.

Difficulty in identifying relevant skills: Recruiters may struggle to identify key skills and qualifications needed for specific job roles, leading to missed opportunities to hire top candidates.

Limited time and resources: Reviewing and improving resumes can be a time-consuming process, and recruiters may have limited time and resources to dedicate to this task.

Inconsistency in evaluation: Different recruiters may evaluate resumes differently, leading to inconsistent evaluations and hiring decisions.

Limited expertise: Recruiters may have limited expertise in certain fields, which can make it challenging to identify key skills and qualifications needed for specific job roles.

Problem Statement

Time-consuming: Preparing contracts manually can be a time-consuming process, especially for large and complex projects. This can lead to delays in the project and increased costs.

Inaccuracies: Manual contract creation increases the risk of inaccuracies in the document that in turn leads to disputes and legal issues down the line.

Lack of standardization: Without a standardized process for preparing contracts, there is a risk of creating inconsistent and non-standardized documents.

Difficulty in tracking changes: When preparing contracts manually, tracking changes and revisions can be difficult. This can lead to confusion and misunderstandings among parties.

Limited collaboration: Manual contract preparation can limit collaboration between parties involved in the project, resulting in a less comprehensive and potentially less effective contract.

Problem Statement

Time-consuming process: Manual analysis of driver behavior can result in delays in providing premiums to customers due to the time-consuming process and significant resources required to gather and analyze the data.

Lack of accuracy: Manual analysis of driver behavior is prone to human error and subjectivity, potentially resulting in unfair pricing for customers and increased risk for the insurance company due to inaccurate assessments of risk and pricing.

Limited data analysis: Manual analysis of driver behavior may provide a suboptimal risk assessment and pricing due to its limitation to basic metrics such as age, location, and driving record, which may not provide a comprehensive picture of driver risk.

Inefficiency: The manual process of analyzing driver behavior and providing premiums can be inefficient, potentially leading to decreased customer satisfaction and increased operational costs for the insurance company due to higher costs and slower decision-making.

Lack of scalability: Manual analysis of driver behavior and providing premiums may become increasingly difficult to manage as the number of drivers and policies increase, potentially leading to bottlenecks and delays in decision-making.

Our GPT Powered Solutions - Watch Now!

Our GPT-Powered Services

Our GPT Service offerings

Natural Language Processing (NLP)

Content Generation and Curation

Recommendation Engines

Predictive Analytics and Insights

E-commerce Solutions

Language Translation

Sentiment Analysis

Data Analysis and Mining

Our GPT Webinar

Success Stories

GPT-Powered HR Portal

Problem Statement

High volume of resumes: Recruiters may receive hundreds or even thousands of resumes for a single job opening, which can be overwhelming and time-consuming to review.

Difficulty in identifying relevant skills: Recruiters may struggle to identify key skills and qualifications needed for specific job roles, leading to missed opportunities to hire top candidates.

Limited time and resources: Reviewing and improving resumes can be a time-consuming process, and recruiters may have limited time and resources to dedicate to this task.

Inconsistency in evaluation: Different recruiters may evaluate resumes differently, leading to inconsistent evaluations and hiring decisions.

Limited expertise: Recruiters may have limited expertise in certain fields, which can make it challenging to identify key skills and qualifications needed for specific job roles.

Solution Overview

We worked with the startup to collect a diverse dataset of resumes and job descriptions from various industries and preprocessed the data to ensure consistency and quality.

We trained a GPT-3 model on the preprocessed data to learn the patterns and relationships between resumes and JDs.

We developed a resume parsing and matching algorithm using the GPT model that could accurately parse resumes and match them with relevant JDs based on job requirements and qualifications.

We used the GPT model to suggest content improvements for the resumes, such as adding relevant skills and qualifications to better match the JDs.

The solution also included an automated cover letter generation system that could generate personalized cover letters for each job application based on the JD and the candidate's resume.

Business Impact

Time and cost savings: Automates many of the time-consuming and manual tasks involved in the recruitment process, reducing the time and resources required for recruitment, and enabling companies to fill vacancies faster.

Improved candidate quality: Helps companies identify and attract top-quality candidates based on their qualifications and fitment with the job requirements, improving the chances of finding the right candidate for the job.

Reduced bias: Reduces bias in the recruitment process by focusing solely on objective job requirements and qualifications, improving the fairness and equality of the recruitment process.

Enhanced candidate experience: Provides candidates with a more personalized and engaging application process, increasing their satisfaction and likelihood of accepting the job offer.

Increased productivity: Reduces the workload for recruiters and hiring managers, enabling them to focus on more strategic and value-adding activities, such as interviewing and assessing candidates.

GPT-Powered ConsTech Solution

Train GPT on existing contract templates for Construction companies to automatically draft new contracts. Analyse 100+ page contract documents and provide Q/A module to ask questions about it.

Problem Statement

Time-consuming: Preparing contracts manually can be a time-consuming process, especially for large and complex projects. This can lead to delays in the project and increased costs.

Inaccuracies: Manual contract creation increases the risk of inaccuracies in the document that in turn leads to disputes and legal issues down the line.

Lack of standardization: Without a standardized process for preparing contracts, there is a risk of creating inconsistent and non-standardized documents.

Difficulty in tracking changes: When preparing contracts manually, tracking changes and revisions can be difficult. This can lead to confusion and misunderstandings among parties.

Limited collaboration: Manual contract preparation can limit collaboration between parties involved in the project, resulting in a less comprehensive and potentially less effective contract.

Solution Overview

The startup wanted to automate the contract drafting process for construction companies to save time and reduce errors.

We built a GPT-based solution that could train on existing contract templates and automatically draft new contracts based on user inputs.

The solution was customizable and scalable, allowing for new templates and features to be added as needed.

Extensive testing and iteration were conducted to ensure the solution was accurate and effective.

The solution was successfully deployed, saving time and reducing errors in contract drafting, and the startup received positive feedback from users.

Business Impact

Increased Efficiency: The solution automates the contract drafting process, reducing the time and effort required to prepare new contracts, which increases overall efficiency and productivity.

Reduced Errors: Using a GPT-based solution reduces the risk of errors in contracts, which can lead to legal disputes and other issues.

Consistency and Standardization: The solution helps ensure that all contracts are consistent and standardized, improving communication and collaboration between parties.

Cost Savings: Automating contract drafting can result in cost savings for businesses, freeing up resources for other areas and increasing profitability.

Improved Customer Experience: The streamlined contract drafting process can provide a better customer experience for clients, increasing their satisfaction with the business and improving the likelihood of repeat business.

GPT-Powered LogiTech Solution

Truck insurance - Building a plugin to interact with the AI engine that can parse truck driving telematics data to generate driver score. A bot to interpret the driver score and suggest improvements to increase score and reduce insurance risk.

Problem Statement

Time-consuming process: Manual analysis of driver behavior can result in delays in providing premiums to customers due to the time-consuming process and significant resources required to gather and analyze the data.

Lack of accuracy: Manual analysis of driver behavior is prone to human error and subjectivity, potentially resulting in unfair pricing for customers and increased risk for the insurance company due to inaccurate assessments of risk and pricing.

Limited data analysis: Manual analysis of driver behavior may provide a suboptimal risk assessment and pricing due to its limitation to basic metrics such as age, location, and driving record, which may not provide a comprehensive picture of driver risk.

Inefficiency: The manual process of analyzing driver behavior and providing premiums can be inefficient, potentially leading to decreased customer satisfaction and increased operational costs for the insurance company due to higher costs and slower decision-making.

Lack of scalability: Manual analysis of driver behavior and providing premiums may become increasingly difficult to manage as the number of drivers and policies increase, potentially leading to bottlenecks and delays in decision-making.

Solution Overview

The truck insurance company worked with OptiSol in developing a plugin that integrates with a ChatGPT AI engine to analyze telematics data and generate a driver score.

The ChatGPT bot within the plugin can interpret the driver score and suggest actionable improvements to increase the score and reduce insurance risk.

The AI engine can accurately assess driver behavior, eliminating the need for manual analysis.

The ChatGPT bot can identify areas where drivers can improve, providing detailed insights to minimize risk and enhance driver safety.

The solution should be designed with data privacy and security in mind, ensuring that sensitive telematics data is protected and that the driver's privacy is respected.

Business Impact

Increased accuracy: A ChatGPT-based AI engine can analyze large amounts of telematics data from trucks, leading to more accurate and reliable driver scores than traditional methods.

Reduced costs: By automating the process of analyzing telematics data, truck insurance companies can reduce the costs associated with manual data analysis and improve efficiency.

Improved risk assessment: Driver scores generated by the AI engine can help insurance companies better assess risk levels associated with individual drivers, allowing them to price insurance policies more accurately.

Personalized feedback: ChatGPT can be used to create a bot that provides personalized feedback to drivers based on their driver score, which can help improve their driving behavior and reduce the risk of accidents.

Competitive advantage: Truck insurance companies that adopt AI-powered solutions for risk assessment and driver feedback can gain a competitive advantage in the market, attracting more customers and improving customer satisfaction.

Testimonials of our Happy Clients

Rick Tigges

CFO, Bemodo

Ryan McClellan

CEO, iLookin.com, LLC

Chris Gordon

CEO, Wait for me

Oli Robinson