Enterprise systems are crucial for enhancing productivity and decision-making among employees and customers.Integrating LLM based systems into enterprise systems enables intelligent automation, personalized experiences, and efficient information retrieval, driving operational efficiency and strategic growth.However, developing and evaluating such systems is challenging due to the inherent complexity of enterprise environments, where data is fragmented across multiple sources and governed by sophisticated access controls.We present Enter-priseBench, a comprehensive benchmark that simulates enterprise settings, featuring 500 diverse tasks across software engineering, HR, finance, and administrative domains.Our benchmark uniquely captures key enterprise characteristics including data source fragmentation, access control hierarchies, and cross-functional workflows.Additionally, we provide a novel data generation pipeline that creates internally consistent enterprise tasks from organizational metadata.Experiments with state-of-the-art LLM agents demonstrate that even the most capable models achieve only 41.8% task completion, highlighting significant opportunities for improvement in enterprise-focused AI systems.

Tracking sugar intake has become a trending practice, much like keeping track of calorie intake. However, consumer-grade systems or devices for measuring or detecting sugar levels of unlabeled food items are close to none. In this paper, we discuss how low-cost Multi-Spectral Imaging (MSI) technology can be used to identify the sugar level of grapes in a simple, yet effective manner. Here, grapes are used as a representative for fruit or food. Freshly harvested grapes were used for spectral data collection. Unispectral portable multispectral camera EVK – UNS52000 which has a spectral range of 700-940 nm across 10 spectral bands was used for data collection. Actual sugar content was determined using a handheld refractometer and used for reference. We evaluated the performance of two well-known machine learning-based regression algorithms such as Random Forest (RF) and Light Gradient Boosting Machines (LightGBM). Spectral data derived from images captured in 10 bands were used as features or independent variables and actual sugar measured using a refractometer was used as a reference or the dependent variable in regression models. We tested the model performance using different feature scenarios: a) All 10 spectral bands, b) Top 5 spectral bands and c) Top 3 spectral bands. Results showed that R2 was 0.92 for both RF and LightGBM with negligible variation in RMSE (1.05-1.06 °Brix) when using all 10 bands. There was minimal difference in terms of R2 and RMSE. However, there was a significant difference in model training time with LightGBM being much faster than RF. Moreover, by selecting the top 5 and 3 features, we reduced the R2 to 0.89 and 0.78 respectively in the case of LightGBM. While there is a reduction in R2, selecting the top 5 bands will be useful from an operational perspective in terms of computational power and cost of device development. We aim to design and develop the device using the selected top 5 bands for operational on-the-fly prediction of sugar content in grapes. The proposed approach could be helpful for various stakeholders such as people with Hypoglycemia or Diabetes, grape producers to gauge the maturity of grapes, and industrial use cases for vineyards.

Post-pandemic, wellness and healthcare sector is vocal about balanced food consumption, inclusion of fresh fruits and regular exercise. Freshness and sugar contents of fruits are crucial to understand before their consumption. A normal RGB camera can provide information about the freshness of fruits based on the surface composition of images generated by the camera. Interestingly, the Multi-Spectral Imaging (MSI) provides information that is superior to a standard RGB camera, as it considers the NIR band. In this article, the authors have deliberated how MSI can be used to predict the sugar level and freshness in fruits, particularly grapes using modified EfficientNet-B0 as a Deep Learning (DL) model for the analysis. For predicting sugar level, the average value across the 5-fold cross-validation (cv) achieved by the DL model was RMSE of 2.68 (std 0.33) ◦Bx, MAE of 2.11 (0.34) ◦Bx, MAPE of 12% (2%) using MSI and RMSE of 7.76 (1.79) ◦Bx, MAE of 6.65 (1.69) ◦Bx, MAPE of 41% (9%) using RGB camera images. For freshness prediction, the DL model achieved an average 5-fold cv accuracy of 88.35% (9.71%) using MSI and 82.22% (7.9%) using RGB camera images. Results indicated that MSI can predict both the sugar level and freshness whereas RGB camera images can be used only for predicting the freshness but not the sugar level prediction of the grapes. The findings suggest that MSI offers a valuable and versatile solution for quality assessment in the fruit industry, enabling better dietary choices and healthcare regimes.