Trusted by The World’s Best
Honestly, here are the Reasons that Stop
You From Achieving Your Goals!
Testing only at the feature level
Reasons this maybe a bad idea:
Building test cases for existing & new features only will result in very less test coverage, missing out on essential functional defects of the system on a broader level.
Regression testing bottlenecks
Regression testing, however, would be best if you had the
tech expertise to build and prioritize test cases for test
execution and, to get accurate results from the test.
Reasons this maybe a bad idea:
Automated regression testing, when done in-house
with limited domain expertise, will lead to
compromising functionality defects at a very later
stage where it’s too cumbersome to fix.
Here’s how you can Fix Your Problems
with Zuci’s Functionality Testing Service
We have served our clients over the years to help them deliver
quality software of all type, including:
Enterprise web applications
Mobile applications
Web applications
Desktop application
Functional Test Craftsmanship
Upon requirement gathering, Zuci’s test engineers will define the scope of functional testing, test environment, test tools, and identify the number of test cases needed to test the functionalities of your software.
Manual & Automated Functional Tests
We take complete responsibility of the end-to-end manual and automated testing activities right from the test plan through the production.
Manual testing – develop test cases for each functionality and carry out a combination of hundreds of tests to ensure stability and working of the software
Automated testing – create automation test scripts for regression testing to ensure the newly incorporated changes or features doesn’t affect the previously working functionality of the software.
Collaborative Agile Test Process
As quality ambassadors, we approach testing with an agile mindset and ensure complete traceability of the testing activities in tandem with your team. You will stay in the know of the testing progress and defects at regular intervals through test reports.
Improved Quality
We analyze test reports and offer recommendations to intensify software quality in accordance with your business requirements.
How FarmERP helped double farmer’s
productivity in the drought-prone regions
of Maharashtra, India.
FarmERP works on developmental projects in India with an objective to help smallholder farmers improve productivity, farm productivity and grow residue free produce and also helped establish Farm-to-Fork traceability of the final products.
Introduction
We worked with a reputed Life Sciences company delivering nutrition and crop health solutions for plants and animals, works on developmental projects in India with an objective to help small farmers improve productivity, farm productivity and grow residue free produce.
FarmERP was the technology partner in this project that planned to reach 100 thousand resource poor, small area farmers in 2 years.
Challenge
- Reaching out to these smallholder farmers was one of the first challenges, which we addressed by appointing 250+ field executives for multiple groups.
- These farmers were situated in remote areas, away from the educated urban towns and cities, and so they had no access to just in time expert advice. This hindered their progress and limited their potential.
- Considering the sheer number of farmers included in the project, bringing information on a central database in itself was an exhaustive task.
- These farmers are resource-poor that is, they face problems securing basic resources for their farms. Helping them double their productivity and in turn their profitability, hence becomes that much more vital.
- Climate data was not available, advance alerts on climate and weather forecasts were not available.
Approach
First phase of implementation started for 25,000 smallholder farmers mainly in the drought-prone regions of Vidarbha and Marathwada. The main crops of the season were Cotton, Tur and Soybean.
Through the field executives, the FarmERP digital platform captured, integrated and analysed data and converted it into actionable insights to support farmers as well as policy makers.
- First and foremost, every acre of land was Geo tagged by the field executives.
- Farmer-wise, plot and crop-wise data was periodically collected by the field executives using the FarmERP mobile client application.
- Crop schedule, nutrition, crop health and water requirement advisory was consistently provided to farmers based on the collected data.
- Daily agronomic activities, crop observations along with photographs were recorded with date, time and location, and synched up real-time with the centralised server.
- Based on all this information and climatic conditions, agronomists sent timely recommendations and prescriptions, remotely.
Benefits
- All the data for farm economics was periodically and consistently captured
- Since we had the collective yield estimates and proposed harvesting dates of crops of all the farms, generated through the FarmERP platform, we could establish market linkages with buyers for those crops.
- Our analytical dashboards helped Project Managers and Senior Project Management of the partner company that supplies inputs to these farmers, to monitor progress and take decisions accordingly. They also benefited from the periodically updated data to predict their future sales.
- The yield estimates that were captured at various levels, were also used by policy makers. This proved helpful for the insurance companies to validate during claims coming from farmers.
- The FarmERP digital platform also helped establish Farm-to-Fork traceability of the final products.
- The productivity of 75% of the total farms had doubled by the end of this project. We conducted an impact analysis for the rest 25% farms.
- The timely advice by expert agronomists, related to accurate nutrition, irrigation, crop health and more, helped double the productivity, and in turn, increased the profitability of these farms.
- A very valuable data asset is created to the project which will further be used for predictive modelling and machine learning based algorithms which will in turn benefit the farmers in the next season as well.