We’ve built a portfolio of industry-specific innovative software solutions that drive and powers the business success. Our custom-fit solutions are efficient, reliable, and user-friendly. With a focus on technical excellence and client satisfaction, we’ve earned a reputation for exceeding expectations and driving long-term growth.
The digital world has been moving with a huge rhythm, which will not stop. For one of our clients, we’ve developed a smart and powerful recruiting software as a service (SaaS) platform designed to modernize and simplify the hiring process to keep pace. the platform we developed includes essential ATS features to streamline recruitment and help hire the best talent quickly and efficiently.
Our team worked closely with clients to understand their unique business needs and goals, delivering exceptional service with clear and transparent communication throughout the development process. The team refurbished the existing basic patient management mobile application to a feature rich application that provides secure and easy access to medical records, appointment scheduling, medication management, symptom tracking, and communication with healthcare providers. The team prioritized privacy, HIPAA compliance, and data security, ensuring that the application meets industry best practices.
The Transport and Logistics Management Portal is a comprehensive system designed for one of our leading logistics and transportation client. Platform provides end-to-end automation covering all modes of transportation, allowing businesses to manage shipments from quote to delivery without manual intervention. With real-time shipping rates and carrier management, the platform optimizes shipping routes to reduce transit times and transportation costs and provides full visibility into the shipment lifecycle. It also includes warehouse management, document management, a customer portal, and reporting and analytics tools, making it a powerful tool for logistics and transportation company looking to streamline their operations, reduce costs, and achieve growth.
This custom software platform was created to manage coworking and flexible workspaces, featuring space management, resource booking, membership management, billing and payment, security and access control, communication and collaboration tools, automated check-in, integrations, reporting and analytics, and a mobile app. These features allow for efficient management of workspace resources, membership details, billing and payments, security, and collaboration between members. The platform was built on Python with Node.js, Django, and React frameworks, and hosted on AWS with scalable storage and networking infrastructure. Databases used include MySQL and MongoDB, and Stripe’s payment processing tool was integrated. The platform also includes messaging and email tools and integrations with Slack and Microsoft Teams.
This proprietary software solution is multi-module management system designed for our client in the spices industry to manage their import and export operations. It includes landed cost calculation, shipment and inventory tracking, and document generation and management. With real-time access to order history, inventory availability, and financial analysis, businesses can streamline operations and make informed decisions. The software also automates the supply chain, enabling businesses to track orders, shipping, and returns. With its Certificate of Analysis feature, the software ensures that the spices meet the required standards. Overall, this software solution offers a comprehensive solution that helps businesses in the spices industry improve their productivity, streamline their business processes, and improve their bottom line.
The AI Powered Matching Algorithm is a tool designed to match job descriptions with candidates and vice versa. The algorithm uses experience details such as roles and responsibilities, and skills from candidate resumes to match with the requirements and skills mentioned in the JD. The algorithm then provides a matching percentage for the candidate and JD, allowing recruiters to source and screen thousands of resumes in seconds. This matching and ranking feature enables recruiters to save time for bulk screening of candidate profiles and avoid any potential biases in decision-making.
The input data for the tool is text data, and it uses unsupervised learning and GRU technologies. The algorithm has immense ability to identify patterns in the data without any prior knowledge of what those patterns might be. This allows the algorithm to discover and match candidates based on factors that may not have been identified as relevant by the recruiter. Overall, the AI Powered Matching Algorithm is a great tool for recruiters in the hiring process, helping them to quickly and efficiently match candidates with JDs and vice versa, saving time and avoiding potential biases.
A software tool (API) that uses Natural Language Processing (NLP) and Machine Learning (ML) algorithms to scan and analyze job description documents and extract information such as job roles and responsibilities, requirements, and required skills for the job posting. The JD parser saves recruiters time while posting a job opening by eliminating the need for manual typing or copy-pasting. The output of the JD parser can be used to pre-populate the parsed details onto the respective fields in the job form while posting a job opening.
The benefits of using a JD parser include the ability to parse various JD formats, saving recruiters time in the job posting process, and improving the accuracy and consistency of the job postings. By eliminating the need for manual processing, JD parser can help reduce errors and ensure that all relevant information is captured and categorized accurately.
The technology used in this JD parser includes Spacy, RNN, Logistic Regression, Regex, SVM, and Naïve Bayes. The accepted input data formats are .pdf, .txt, .doc, and .docx. Overall, JD parser is a useful tool for recruiters to streamline their job posting process and improve the quality of their job postings.
Resume Parser
A software tool (API) that uses Natural Language Processing (NLP) and Machine Learning (ML) algorithms to extract relevant information from resumes or CVs. It converts unstructured resume data into structured data that can be easily searched, filtered, and analyzed.
The benefits of using the resume parser include time-saving, standardization, increased efficiency, integration with ATS systems, multilingual support, customizable parsing rules, and improved candidate experience.
As for the technologies used, resume parser employs a variety of machine learning algorithms and natural language processing (NLP) techniques, such as Spacy, RNN, Logistic Regression, Regex, SVM, and Naïve Bayes, to analyze and extract information from resumes. Accepted formats for the input data include .pdf, .txt, .doc, and .docx.
JD (Job Description) Parser
Candidate Profiler
The Candidate Profiler is a scalable tool (API) designed to map and validate candidates for various job profiles based on their experience, education, and relevant work in the field. It uses machine learning to classify each sentence from the candidate’s work responsibilities and projects.
For example, Data science roles are bucketed into one of five categories: Machine Learning, Big Data engineering, Business Intelligence, Data Analyst, or DevOps. If the sentence doesn’t match these categories, it will be classified as “Others.” Based on this classification, the tool recommends the most suitable role for the candidate. The input data for the tool is text data. Overall, the Candidate Profiler is a useful tool for Data Science employers and employees to identify the most suitable job profiles for candidates based on their skills and experience.
JD Profiler
A scalable machine learning tool that maps and validates the parsed JD for various job profiles based on the requirements and skills mentioned in the JD. The tool uses a classifier to classify each sentence from the roles and responsibilities, skills, and requirements into one of five categories, Machine Learning, Big Data Engineer, Business Intelligence, Data Analyst, or DevOps for the Data science domain. The tool is designed for employers and employees in the Data Science domain, and it can recommend the most suitable job profile for the JD based on the role distribution.
The input data for the tool is text data, and both employers and candidates can benefit from its use. For employers, the tool can streamline the hiring process and improve the quality of job postings, while for candidates, it can save time and effort in the job search process by identifying the most suitable job profiles based on their skills and experience.
Matching Algorithm
Personal Data Scrubber
Developed and implemented a machine learning model/tool (API) using SpaCy to scrub sensitive personal data from records. The model was tested on a large dataset of documents and was found to be highly accurate in identifying and removing sensitive information. The model was also scalable, making it suitable for use in large-scale applications.
The project compared the performance of the SpaCy model with other machine learning models commonly used for data scrubbing, including CRF and SVM. The SpaCy model was found to be more accurate and efficient than the other models tested, making it a promising solution for organizations that need to scrub sensitive personal data from sensitive records as required by regulations.
The tool demonstrated the potential of machine learning models like SpaCy to improve the accuracy and efficiency of data scrubbing, helping to protect privacy while also enabling more effective data analysis and research.
PHI Scrubber
The machine learning model to scrub sensitive PHI data from medical records. The model was tested on a large dataset of medical records and was found to be highly accurate in identifying and removing sensitive information. The model was also scalable, making it suitable for use in large-scale applications.
The project compared the performance of the various commonly used for data scrubbing, including CRF and SVM. making it a promising solution for organizations that need to scrub sensitive PHI data from medical records.
The model has the high accuracy and efficiency of data scrubbing in the healthcare industry, helping to protect patient privacy while also enabling more effective data analysis and research.