Our client, a dynamic e-commerce company, sought to automate several internal processes to improve efficiency and scalability. With rapid growth, it became increasingly difficult to manage internal workflows, including employee onboarding, task management, document generation, routine queries, etc. These manual processes were consuming too much time and slowing down operations. The client wanted a custom AI bot to simplify and accelerate onboarding while automating other internal processes like handling administrative requests, generating reports, and managing internal communications, etc.
New hires had to manually request access to systems and information, leading to delays
Routine tasks like document creation, report generation, and internal requests were taking up significant employee time
Information was spread across various systems, causing delays in accessing necessary data
The client’s manual processes couldn’t keep up with their rapid expansion, creating operational inefficiencies
We partnered with the client to develop a custom AI bot that could automate not just onboarding but a range of internal processes. Several specialized teams contributed to the project
Process Workflow Optimization: Analyzed the client’s internal workflows to identify bottlenecks in onboarding and other administrative tasks
Requirement Documentation: Collaborated with the client to define key areas for automation, ensuring the AI bot would handle critical internal processes
Custom AI Model Development: Developed an AI model capable of handling onboarding, administrative tasks, document generation, and internal queries
Natural Language Processing (NLP): Implemented advanced NLP features to allow the bot to understand and respond to a wide variety of internal requests, enabling smooth automation of both onboarding and other processes
API Integration for Internal Systems: Created robust APIs to ensure the bot could access HR, task management, administrative systems etc.
Data Management Optimization: Optimized backend infrastructure to support high-speed data access for the bot, ensuring quick responses to internal requests
User-Friendly Dashboard: Developed a web interface where employees could interact with the AI bot to request documents, manage tasks, and access onboarding information
Integration: Guaranteed optimal backend response speed to handle all incoming requests efficiently, integrated to the Dashboard alerting developed be DevOps Team.
CI/CD Automation: Built CI/CD pipelines to ensure the quick and smooth rollout of updates to the AI bot as the client’s requirements evolved
Monitoring and Alerts: Implemented real-time performance monitoring and alert systems to ensure optimal functionality of the AI bot at all times
The project was executed in the following phases
The BA conducted workshops with the client’s HR and operations teams to understand their needs
We developed a project plan with specified milestones to track how the project is progressing
The AI Team began by building and fine-tuning a custom AI model tailored to the client’s internal operations.
The Natural Language Processing (NLP) part was introduced, allowing the bot to understand and process a wide range of internal requests in human language
The Back-End Development Team created robust APIs to connect the bot with internal systems like HR databases, document repositories, and task management tools.
The Front-End Development Team developed a user-friendly interface for employees to interact with the bot through the company’s internal web platform.
The DevOps Team implemented the deployment using Continuous Integration and Continuous Deployment (CI/CD) pipelines.
Monitoring Systems were put in place to track the bot’s performance, ensuring that any issues such as slow response times or system errors were detected and resolved immediately
The custom AI bot improved the client’s internal operations significantly:
Automated document generation and information access reduced onboarding time by 65%
Routine administrative tasks, such as internal request processing saw a 50% reduction
Internal queries were handled and resolved 400% faster, with the bot responding to requests in under 2 minutes
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