CASE STUDIES

  Objective

Automate and standardize the process of seed quality analysis to support procurement decisions and improve consistency across grading centers. 

  Our Solution

• • Designed a lightweight AI-based model capable of segmenting and classifying seed images captured via mobile devices.
• • Enabled automatic grading of seed quality based on visual parameters like size, shape, and surface texture.
• • Delivered results through a user-friendly interface that worked offline, requiring minimal training for on-ground staff.

  Procedure

• • Field staff captured seed samples using their mobile cameras under guided lighting conditions.
• • The captured images were automatically processed by an AI model embedded within the mobile app.
• • The model segmented individual seeds from the background and classified them into pre-defined quality categories.
• • Results were instantly displayed and synced to the centralized dashboard, allowing the FPO manager to view batch-wise grading outcomes.
• • Historical data was logged to improve the model's accuracy over time and assist in future procurement decisions.

  Impact

• • Reduced grading time by over 60%, enabling faster procurement cycles.
• • Increased consistency and accuracy in quality classification across centers.
• • Created a digital audit trail for all seed batches, improving traceability and decision-making for FPO managers.

  Objective

Streamline the peanut procurement process for farmer networks by introducing transparency in quality grading, real-time pricing, and centralized decision-making.

  Our Solution

• • Developed a mobile-first procurement app enabling field staff to capture crop quality through guided checks at the farmgate level.
• • Integrated live wholesale market rate tracking to help staff calculate fair and competitive prices instantly.
• • Built a centralized dashboard for FPO managers to monitor procurement volumes, quality trends, and pricing decisions across all touchpoints.

  Procedure

• • Field agents capture details of peanut samples using the mobile app at the time of collection.
• • The AI model analyzes visual features like pod size, shell damage, discoloration, and fungal signs.
• • Based on detected quality, market rates, and FPO-set rules, the system auto-generates a fair procurement price.
• • All transactions are logged in a dashboard, giving FPO managers visibility into quality-grade distribution, prices paid, and procurement trends by location.
• • The pricing model continuously adapts based on market volatility and manager feedback.

  Impact

• • Significantly reduced quality-related disputes by standardizing the grading process at the source.
• • Accelerated procurement cycles by eliminating manual calculations and enabling on-the-spot decisions.
• • Enhanced profitability for both FPOs and farmers through better price transparency and quality alignment.

  Our Solution

We designed a custom, intelligent algorithm specifically tailored for dental practitioners. This advanced solution:

Continuously Learns and Evolves:

• • Trained extensively on historical diagnostic data from individual dentists.
• • Utilizes collective insights from diagnoses performed by multiple dentists across the platform.
• • Incorporates standardized medical coding guidelines (code book) to ensure accuracy and compliance.

Enhanced Prompt Understanding:

• • Employs a dynamically evolving Retrieval-Augmented Generation (RAG) system and GenAI capabilities.
• • Identifies and predicts potential patient questions, proactively suggesting precise and relevant diagnostic and treatment recommendations.

Impact Achieved:

• • Our client's implementation of this AI-driven assistive solution led directly to a significant revenue increase:
• • Revenue Growth: Achieved a 20% boost in revenues by successfully upselling this enhanced capability to their existing customer base, demonstrating substantial value and tangible benefits for dental practices.

  Our Solution

• • Leveraging advanced neural networks and knowledge graphs, we developed an AI solution capable of accurately identifying and categorizing various objects within the large volume of construction drawing submissions received by state departments.
• • We constructed a dynamic, continually evolving data dictionary to facilitate precise pattern matching for incoming drawings, significantly enhancing the accuracy and efficiency of compliance checks.
• • Additionally, our solution features an intelligent reporting system integrated with self-correcting markup technology, enabling a fully automated review process.

  Benefits:

• • Reduced repetitive submissions by over 80%, significantly easing human reviewer workloads.
• • Proactively identified 75% of code compliance issues upfront.
• • Decreased the overall permitting cost by 15%, directly benefiting citizens.

  Our Solution: Real-Time Vision and Predictive Insights for Maritime Management

• • Streaming-Based Vision Model: Continuously processes live data from onboard CCTV cameras, enabling immediate visual analysis across multiple locations on the ship.
• • Customizable Parameterization: Flexible model configurations tailored to individual ships and diverse customer requirements, ensuring optimal functionality for varied scenarios.
• • Automated CTA Alert System: Delivers prompt alerts to ship management, enabling rapid response to detected anomalies or compliance issues.
• • Integrated Predictive Analytics: Advanced predictive modeling layered on top of collected data, offering actionable insights for proactive decision-making and improved operational efficiency.

  Benefits:

• • Improved Operational Efficiency: Timely identification and management of anomalies reduce downtime and enhance productivity.
• • Enhanced Compliance: Real-time alerts significantly improve adherence to maritime compliance standards.
• • Proactive Decision Making: Predictive analytics enable ship management to anticipate issues and implement preventative measures.
• • Cost Optimization: Early detection and swift management responses lead to reduced maintenance costs and improved resource allocation.