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Specializing in machine learning engineering and scalable AI system architecture with hands-on technical leadership.

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: Enhancing the customization and generation of critical operational documents, ensuring brand consistency and regulatory compliance.

This project was completed under a strict Non-Disclosure Agreement (NDA). All details have been generalized to protect our partner's confidentiality.

Our partner's workflow required the creation of customized operational documents. The existing tools and processes for this task were a source of inefficiency and risk.

: Users had to work with a non-intuitive interface to design and format essential documents, a process that was both difficult and cumbersome.

: Manual design efforts often resulted in variations in branding, layout, and the placement of critical information, diluting the company's professional image.

: Ensuring each document included the correct, compliant information for different service types was a meticulous and error-prone task.

: The end-to-end process of creating a custom-formatted document was slow, occasionally creating delays in the overall operational timeline.

 was developed to radically simplify the creation of these vital documents. The agent acts as a conversational partner, guiding users from a blank slate to a finished, compliant document.

: The agent allows users to specify design and content requirements using simple, natural language, removing the need for any graphic design skills.

: Users are guided to select an approved base template, which the agent then helps them customize with specific fields, logos, and other required elements.

: The agent intelligently populates the document with the correct, context-sensitive operational data, ensuring accuracy and completeness for each specific job.

: As the user makes requests, the agent instantly renders a live preview of the document, allowing for rapid iteration and immediate visual confirmation.

The solution is built on a modern framework that combines conversational AI with document generation capabilities.

: Interprets user requests such as, "Add our logo to the header" or "Make sure the order number is in bold."

: Works from a set of approved master templates to ensure all outputs are consistent with brand standards.

: Automatically populates documents with the correct operational data.

: Generates an immediate visual representation of the final document for on-the-spot review and approval.

: CopilotKit Runtime, OpenAI GPT-4o, CrewAI

The introduction of the Document Design Assistant brought a new level of speed, consistency, and simplicity to a once-complex task.

: The time required to design and generate a custom, ready-to-use document is now 

: All documents generated through the agent 

flawlessly adhere to established brand and layout guidelines

: The risk of creating documents with incorrect or missing information has been 

empowered to create professional, compliant documents

: The conversational interface transforms a difficult task into a 

reinforce a professional and reliable brand image

: The ability to create custom documents on the fly allows the business to 

respond faster to unique operational requirements

📄 Document Design Assistant AI Agent: Use Case Showcase (Confidential)

: Streamlining complex system configuration, reducing user error, and improving operational efficiency for a critical business application.

Our partner utilized a powerful but highly complex user interface for a core operational task. This complexity, while enabling granular control, presented significant challenges for their teams:

: Users were required to navigate a dense interface with numerous parameters, options, and interdependent settings to complete a single task.

: The intricacy of the manual setup process led to a notable rate of configuration errors, which could cause downstream operational disruptions.

: A substantial investment in time and resources was necessary to train employees to use the system correctly and avoid common pitfalls.

: The time-consuming nature of the setup and the need to correct errors created process bottlenecks, impacting overall team productivity.

 was developed to act as an intelligent guide, simplifying interaction with the complex system. The agent was designed to assist users without replacing the core application.

: The agent provides a simple, conversational interface where users can state their objective in plain language.

: It translates the user's simple request into the complex set of parameters and selections required by the system, abstracting away the underlying complexity.

: The agent actively validates inputs and choices against a set of business rules in real-time, catching potential errors before a configuration is finalized.

: By handling the intricate details of the setup, the agent allows users to focus on the "what" (their goal) rather than the "how" (the specific steps in the UI).

The solution leverages a modern tech stack to provide a seamless and intelligent user assistance layer.

: Allows users to specify their needs conversationally.

: Contains the system's inherent constraints and logic to prevent users from making invalid configurations.

: Walks the user through the process one decision at a time, simplifying the most complex tasks.

: Interfaces directly with the target application to apply the correct configurations based on the user's dialogue.

The implementation of the Configuration Assistant delivered significant value by addressing the core challenges without altering the powerful underlying system.

: The process for completing complex configurations is now 

significantly more straightforward and intuitive

: The agent's guidance and validation have led to a 

: New users can become proficient with the system's core functions 

 and with less reliance on intensive, hands-on training.

 when performing setups, knowing the agent is ensuring accuracy.

removes the need for users to memorize complex procedures

: A lower error rate translates directly to 

more reliable and consistent operational outcomes

, freeing them to focus on other high-value responsibilities.

encourages more effective and widespread use

encapsulates the knowledge of expert users

, making that deep system knowledge accessible to every member of the team.

⚙️ Configuration Assistant AI Agent: Use Case Showcase (Confidential)

: Internal Recruiting Department (Qbitech)

: Internal candidate sourcing, talent pool management, and recruitment lifecycle optimization.

The internal recruiting team at Qbitech needed to more effectively leverage its existing database of candidates to fill new roles quickly and efficiently.

The recruiting team faced several challenges that slowed down the hiring process and created administrative overhead:

: Recruiters spent significant time manually querying the candidate database with various criteria to find suitable profiles for new roles.

: The repetitive tasks of sending outreach emails, scheduling initial calls, and manually updating candidate records were highly inefficient.

: Qualified candidates already present in the database were often overlooked due to the limitations of manual searches.

: The tone and format of initial communications varied from recruiter to recruiter, leading to an inconsistent candidate experience.

: Information regarding a candidate's availability and interest quickly became outdated, making the database less reliable.

 was developed to integrate with the internal candidate database and automate the initial stages of the recruitment process. This solution provides several core capabilities:

: The agent queries the database to find suitable candidates by analyzing requirements like tech stack and professional experience.

: Upon identifying a match, the agent automatically reaches out to the candidate via email to inquire about their availability and interest in the new role.

: For interested candidates, the agent handles the logistics of organizing and scheduling an initial meeting with the recruitment team.

: The agent automatically updates the candidate profiles with their latest status, availability, and communication logs, ensuring the data remains current.

The solution is built on a modern technical stack designed for intelligent automation and seamless integration.

: Uses natural language to understand role requirements and match them against candidate profiles based on tech stack, experience, and more.

: Manages the end-to-end process from sourcing and outreach to scheduling and database updates.

: Generates personalized emails and coordinates schedules for meetings.

: Connects directly to the internal candidate database to read and write information.

: CopilotKit Runtime, Gemini models, CrewAI

: Shared state synchronization between the UI and the AI agent.

The implementation of the recruiting agent led to significant enhancements in the team's efficiency and effectiveness.

: The time required to identify a shortlist of qualified internal candidates was 

eliminated the need for manual data entry

: All initial candidate communications are now 

Recruitment & Talent Management Enhancement

: The agent ensures the existing database is 

, surfacing relevant candidates who might have been missed.

prompt, clear, and efficient communication

: The system helps to keep the candidate database 

 like conducting interviews and building candidate relationships.

: By streamlining the initial sourcing and screening phases, the 

 into the internal talent pool for better strategic planning.

manage multiple recruitment requests simultaneously

, allowing the team to scale its efforts effectively.

🧑‍💻 Recruiting AI Agent: Use Case Showcase

: Internal Sales & Business Development Team (Qbitech)

: Lead nurturing, sales cycle management, and prospect engagement automation.

The Qbitech sales team needed a way to maintain consistent and personalized communication with a large database of prospects who had already been contacted.

The sales team faced common obstacles that limited their ability to effectively nurture leads through the sales pipeline:

: Manual follow-ups were often sporadic, causing promising leads to go cold when sales representatives became busy with other tasks.

: A significant amount of the team's time was consumed by the repetitive and manual process of writing and sending follow-up messages.

: It was challenging to personalize messages at scale, often resulting in generic follow-ups that failed to resonate with the prospect.

: Manually logging every follow-up email or call in the database or Excel file was tedious and often neglected, leading to an inaccurate view of prospect engagement.

: Relying primarily on email made it difficult to cut through the noise. Using more direct channels like WhatsApp was not scalable manually.

 was developed to automate the lead nurturing process, ensuring every prospect receives timely and relevant communication. This solution provides several core capabilities:

: The agent systematically processes the existing database or Excel file of prospects to identify who is due for a follow-up based on predefined schedules.

: It sends tailored follow-up messages using relevant data from the prospect's record. The agent can engage prospects via both 

email and automated WhatsApp voice messages

: The agent manages the timing and frequency of communications, nurturing leads without causing fatigue.

: After each communication attempt, the agent automatically updates the prospect's record, ensuring all data is accurate and reflects the latest touchpoint.

The solution leverages a modern tech stack designed for intelligent, multi-channel communication and automation.

: Natively sends emails and leverages text-to-speech (TTS) models to generate and deliver WhatsApp voice messages.

: Injects prospect-specific data into message templates to ensure relevance in every communication.

: Follows a customizable schedule to trigger outreach at the most effective times in the sales cycle.

: Reads from and writes to both structured databases and standard Excel files seamlessly.

: CopilotKit Runtime, OpenAI GPT-4o, CrewAI, Text-to-Speech (TTS) Models

The implementation of the sales follow-up agent drove significant improvements in the sales process and team productivity.

: A consistent cadence of follow-ups is established, ensuring 

 with the latest communication logs, with no manual data entry required from the sales team.

 to the entire prospect database simultaneously.

 that differentiates Qbitech from competitors.

move prospects through the pipeline more effectively

: By maintaining steady engagement, the agent 

increases the likelihood of converting leads

manage follow-ups for a growing list of prospects

 without increasing the team's manual workload.

📈 Sales Follow-up AI Agent: Use Case Showcase

: Vehicle fleet management, damage assessment, and customer check-in/check-out optimization.

 managing a diverse fleet of vehicles and facing challenges with the high frequency of vehicle handovers and damage inspections.

The car rental company faced significant operational bottlenecks in its vehicle inspection and turnaround process:

: Manual inspections during customer check-in and check-out were time-consuming, causing 

 when documenting vehicle damage, leading to potential disputes and poor tracking.

: The process required significant staff time dedicated to 

, pulling resources away from customer service.

: Lengthy wait times and manual paperwork during vehicle pickup and drop-off resulted in a 

: Unstructured damage reports made it difficult to maintain a clear damage history for each vehicle, complicating 

AI-powered vehicle damage assessment agent

 was developed to transform the vehicle check-in and check-out process. This solution introduces several core capabilities:

: The AI agent analyzes uploaded vehicle photos in 

 using computer vision during the handover process.

 across 6 vehicle sections, creating a consistent digital record for every inspection.

: A natural chat experience allows staff to upload photos and receive 

: A three-tier assessment system (minor, moderate, severe) ensures 

 between AI analysis and a human manager for review when necessary.

The solution is built on a modern technical stack designed for performance and scalability.

: Body, Windshield/Windows, Tires, Wheels, Mirrors, Lights.

: Precise mapping of damage locations (e.g., "Front Left Door").

: Comprehensive classification (e.g., Scratches, Dents, Cracks).

: The AI determines the impact on vehicle safety and functionality.

: CopilotKit Runtime, OpenAI GPT-4o Vision, CrewAI

The implementation of the AI agent led to transformative changes across the car rental operation without focusing on specific ROI metrics.

: The need for intensive manual inspections was 

 of the vehicle's condition, reducing ambiguity.

: The process is simplified to a quick photo upload, 

: Faster vehicle turnaround means cars are 

: Structured data provides clear insights into 

damage trends and informs preventative maintenance

 across all rental locations and vehicle types.

The car rental company faced significant operational bottlenecks in its vehicle inspection and turnaround process.

Case Studies

📄 Document Design Assistant AI Agent: Use Case Showcase (Confidential)

⚙️ Configuration Assistant AI Agent: Use Case Showcase (Confidential)

🧑‍💻 Recruiting AI Agent: Use Case Showcase

📈 Sales Follow-up AI Agent: Use Case Showcase

🚗 Vehicle Damage Assessment AI Agent: Use Case Showcase