In partnership with a client, thinkbridge developed a custom machine learning (ML) model that respects data privacy and operates cloud-natively. This model harnessed a data collection mechanism to gather historical data and reviewer actions, enabling effective training of the ML system. Integration of Open AI’s LLM API and fine tuning is in progress. The implementation of this ML model led to the elimination of manual reviewers, significantly enhancing workflow efficiency. The model demonstrated impressive adaptability, starting with over 90% accuracy and quickly evolving to exceed 98% accuracy within three months. This continuous improvement allows the model to adjust to new data patterns as new clients are onboarded, streamlining the financial review process and ensuring more consistent and informed decision-making.

Job Order Understanding: Deeply understands job requirements for accurate candidate matching. | Candidate-Job Auto Matching: Uses AI to align candidates with jobs based on key criteria. | Boolean Search String Generation: Efficiently creates search strings to find suitable candidates. | Automated Candidate Sourcing and Screening: Streamlines initial recruitment stages by autonomously processing candidates. | Automated Personalized Outreach: Crafts tailored messages to candidates based on match scores.

This case study highlights thinkbridge's partnership with a rooftop solar solutions provider to tackle timely failure detection in solar systems, a critical issue causing energy and financial losses. The developed solution was a cloud-native monitoring platform, integrating IoT sensors and machine learning for proactive failure detection and quick resolution. The platform's impact was significant, offering real-time performance data, aiding in rapid issue identification and maintenance response. This led to better control and efficiency of solar resources, establishing the client as a leader in high-quality, efficient solar solutions. The client's market presence was strengthened, noted for exceptional quality and efficiency, with ongoing enhancements to further elevate their position. Impact: [Resolution Speed: Issues resolution improved by 80%] [Energy Loss Reduction: System faults-related losses decreased by 50%.] [Quicker Renewable Energy ROI: Enabled customers to achieve up to 3x faster returns on renewable energy investments.]

thinkHelper is a multifaceted tool designed for software engineers, leveraging OpenAI's Large Language Models. It assists in various aspects of software development, including providing explanations, fixing bugs, optimizing code, refactoring, code reviews, commenting, documentation, and generating unit tests. It supports multiple programming languages, enhancing the efficiency and quality of software engineering tasks.

thinkCover, a Visual Studio extension and powered by the OpenAI API, enhances software testing efficiency. For each function it generates a unit test for, it contributes between 0.2 to 1 point to the overall code coverage. This tool streamlines the process of creating unit tests, thereby improving the quality and reliability of software development.

thinkSQL is a developer-friendly domain database modeler powered by OpenAI's Large Language Models. It simplifies the creation of SQL scripts, covering both DDL and DML, along with various database functions. This tool automates and refines the database design process, providing intelligent insights for crafting efficient and robust database structures. Ideal for database professionals at all levels, thinkSQL blends AI capabilities with a practical interface, streamlining database development and ensuring alignment with project objectives.

thinkGenie is a Chrome extension, utilizing OpenAI's Large Language Models, created to aid the Software Quality Control (SQC) team. It specializes in generating Playwright test selectors, a crucial component in automated testing. This tool simplifies the process of selector creation, thereby boosting the efficiency and precision of testing workflows, and provides significant support to the SQC team in ensuring software quality.

thinkMatcher, utilizing OpenAI's Large Language Models, is a tool that compares code with requirements in any programming language, determining if the code meets those requirements. It highlights discrepancies and potential issues, ensuring software aligns with its intended specifications. This AI-driven approach enhances accuracy in code requirement alignment.

thinkPlanner, utilizing OpenAI's Large Language Models, is an AI-powered planning assistant that combines proprietary platforms to create strategic software development roadmaps. It includes intelligent problem discovery, dynamic user journey mapping, design opportunity analysis, and roadmap creation. Features like iterative refinement and architecture planning enhance software development, focusing on user-centric precision and efficiency.