Case
Studies
01
Health-care
Connecting clients
In the healthcare sector, understanding how physicians perceive clinical evidence is critical to shaping effective communication strategies and supporting informed medical decisions. One of our recent projects focused on building a mobile-based physician survey system designed to measure changes in medical attitudes after reviewing scientific product literature.
Our client, a multinational healthcare organization, needed a secure and scalable digital solution to collect structured feedback from physicians across multiple regions. The objective was to assess how reading newly published clinical materials influenced doctors’ perspectives on specific treatment approaches, therapeutic positioning, and real-world application.
We designed and deployed a mobile-first survey platform optimized for physician engagement. The system allowed participants to access curated product literature directly within the application, ensuring a consistent reading experience before completing the questionnaire. By integrating literature viewing tracking with survey logic, the platform ensured data accuracy and compliance while maintaining ease of use.
To enhance reliability, we implemented structured survey flows with conditional logic, time-based tracking, and response validation mechanisms. The system captured baseline attitudes prior to literature exposure and compared them with post-reading responses, enabling clear measurement of perception shifts. This approach provided quantifiable insight into how evidence-based communication influenced medical decision-making.
Data security and compliance were central to the solution. The platform was built with secure authentication, encrypted data transmission, and role-based access control to protect sensitive information. The backend architecture supported real-time aggregation, data cleansing, and analytical reporting, allowing stakeholders to monitor participation rates and response trends dynamically.
Beyond simple data collection, we developed an integrated analytics dashboard that visualized changes in therapeutic preference, confidence levels, and adoption intent. These insights enabled the client to evaluate communication effectiveness, refine messaging strategies, and better align scientific engagement with clinical expectations.
The project successfully collected high-quality, structured feedback from physicians at scale, providing measurable evidence of how medical literature influenced treatment attitudes. By combining mobile accessibility, intelligent survey design, and advanced analytics, we delivered a comprehensive digital research solution that strengthened client–physician connection and transformed raw feedback into actionable strategic intelligence.
01
AI Agent
Intelligent SKU Mapping
In complex enterprise environments, product inquiries and SKU mapping often require extensive manual effort. Sales and pre-sales teams traditionally rely on experience and manual database searches to match customer requirements with the correct product configurations. This process is time-consuming, error-prone, and heavily dependent on individual knowledge.
To address this challenge, we developed a customized AI Agent designed specifically for automated SKU recommendation and requirement mapping.
The system integrates internal product databases, historical quotation records, specification documents, and structured SKU rules into a unified knowledge framework. Using natural language processing and intelligent matching algorithms, the AI Agent can interpret customer inquiries, analyze requirement descriptions, and automatically generate accurate SKU suggestions in real time.
Instead of manually searching through spreadsheets or product catalogs, pre-sales teams now input customer requirements into the AI-powered interface. The system performs semantic analysis, cross-references technical parameters, and recommends optimized SKU combinations based on compatibility, historical usage patterns, and business logic.
Beyond simple keyword matching, the AI Agent understands contextual relationships between specifications, use cases, and configuration constraints. It continuously improves its accuracy by learning from confirmed quotations and finalized orders, creating a self-optimizing recommendation loop.
The impact on operational efficiency has been significant. Manual retrieval and SKU mapping time has been reduced by more than 50%, allowing pre-sales teams to respond faster and focus on higher-value customer engagement. Accuracy and consistency have improved, while dependency on individual expertise has decreased.
By transforming manual SKU search into an intelligent, automated process, this AI Agent has elevated quotation workflows from reactive lookup to proactive decision support — accelerating response time and strengthening competitive positioning.
01
Smart Manufacture
Traceable In & Out
In the manufacturing sector, warehouse efficiency directly determines operational performance. One of our key projects focused on building an intelligent Warehouse Management System (WMS) integrated with Autonomous Guided Vehicles (AGVs), enabling full automation of inbound and outbound logistics.
Our client was facing increasing order volume, complex SKU structures, and heavy dependence on manual operations. Inventory accuracy relied on human experience, material handling required multiple handovers, and outbound preparation consumed significant time and labor. The entire process lacked real-time visibility and standardized data control.
We redesigned the warehouse operation model from the ground up.
First, we implemented a centralized WMS platform that digitized every inbound, storage, picking, and outbound action. Each pallet and SKU was assigned a unique traceable identifier, linked to system-driven location management. Real-time inventory updates replaced manual recording, ensuring data accuracy across the entire warehouse.
Second, we integrated AGV mobile robots into the workflow. Through API-based communication between WMS and the robot control system, task instructions were automatically generated and dispatched. Instead of operators manually transporting goods, AGVs executed intelligent routing, optimized path planning, and automatic docking for storage and retrieval.
The result was a synchronized system where goods movement was driven by data rather than manual coordination. Inbound registration, shelf allocation, picking instructions, and dispatch confirmation became fully traceable digital events.
Operational performance improved dramatically. The outbound preparation process, which previously required approximately one hour of manual coordination and physical handling, was reduced to just ten minutes through automated task sequencing and robotic execution. Inventory accuracy increased, labor dependency decreased, and workflow stability significantly improved.
Beyond efficiency gains, the system provided management-level visibility. Real-time dashboards displayed inventory turnover, space utilization, robot utilization rate, and task completion metrics. Decision-making shifted from reactive adjustment to predictive optimization.
By combining intelligent WMS architecture with AGV robotics, we transformed a traditional warehouse into a data-driven, automated logistics hub — ensuring that every item entering and leaving the facility is recorded, traceable, and precisely controlled.