Automate
❯
Automate Process
❯Automated Quality Assurance
Large Companies
NLP - Text summarization
For:
Media Intelligence Analysts
Scope:
Using Abstractive Text Summarization to reduce analysis costs for media monitoring.
Goal:
Improve Operation Efficiency
Intelligent Document Processing Using AI
For:
Government & public entities
Government departments
AI-Based Solid Waste Classification
For:
Government Municipal Corporations, Solid Waste Management Companies
Large Companies
Small Companies
Entertainment and Media - Subtitle Creation
For:
Content creators
Scope:
Creating efficiencies for content creators via automatic subtitle creation for social video.
Goal:
Improved Employee Efficiency
Large Companies
Small Companies
Improve Business Decision
For:
Credit controllers
Scope:
Using Machine Learning to automate the assessment of creditworthiness for loan applicants at a bank.
Goal:
Improve Operation Efficiency, Increase Revenues
Intelligent Social Listening
For:
Local authorities, Government agencies
Automated Quality Assurance
For:
Quality Engineers
Large Companies
NLP - Machine translation
For:
Translation Managers and Translators.
Scope:
Using Machine Translation to scale content production and create a more streamlined and efficient translation process.
Goal:
Improve Operation Efficiency, Automate a Business Process
Procurement - Cost Analysis
For:
VP Global Supply Chain Management, Category Managers
Scope:
Realizing operational efficiencies and working capital improvements through automated spend classification.
Goal:
Reduce costs, Improved Employee Efficiency
How ML Can Improve Churn Prediction to Retain More Revenue for Insurers
For:
Portfolio Managers
Customer Retention
Goal:
Improved Operation
From Weeks Down to Hours: How insurers innovate with AI to shorten claims processing time and improve customer experience
For:
- Claims Management
- Customer Experience.
Goal:
Improved Customer Experience
General Public
Education - Smart Learning Content
For:
Online course creators and online learners.
Scope:
Empowering course creators to focus on complex decision-making and creativity with Computer Vision and Natural Language Processing.
Goal:
Improved Employee Efficiency
Large Companies
Accounting and Finance - Improve Profitability Reports
For:
Financial analysts
Scope:
Using Natural Language Generation to automate the production of commentary on profit and loss statements at a bank.
Goal:
Improved Employee Efficiency, Improve Operation Efficiency
Large Companies
Audio Signal Processing - Voice to text Conversion
For:
Lawyers and Judges
Scope:
Using Automatic Speech Recognition (ASR) to transcribe court case proceedings.
Goal:
Automate a Business Process
Precision Farming as a Service
For:
Farmers
Scope:
Use visual recognition to identify and help fight parasites attacking organic farms.
Goal:
Anticipate Risks, Improve Operation Efficiency
Autonomous Robot Improves Surgical Precision Using AI
For:
Hospitals using Autonomous robotic surgery via the STAR system
Goal:
Improve Operation Efficiency
Large Companies
Small Companies
Accounting and Finance - Automate Invoices and Expense Management
For:
Financial Controllers
Scope:
Using Image Processing and Optical Character Recognition to create operational efficiencies through automation of expense approval and reconciliation workflows.
Goal:
Improved Employee Efficiency
Automated Quality Assurance
For:
Quality Engineers
Problem addressed
An organization that can bring high-quality products to market ahead of its rivals has a huge competitive edge. However, it is not wrong to say that the sophistication of technical products has significantly increased, making them more error-prone. In order to meet the ongoing needs of speed to market and exceptional customer experience, Quality Assurance (QA) must evolve.
The majority of agile testing is still focused on manual testing and the labor-intensive creation of "automated" test scripts, which makes the process comprehensive and time-consuming. The slow and complex testing process allows checking only a few aspects of the application, a problem that even the most prominent firms are dealing with.
Description
This conundrum can be resolved by using artificial intelligence (AI), which can speed up the manual testing process. Automated quality assurance helps QA engineers to concentrate on looking into functionalities that are most likely harmful, by prioritizing the test cases based on already existing test cases and test logs.
Since AI agents can grow and learn on their own throughout the testing process, they will adapt to changes in the code base and find new application functions on their own without any human intervention.
Tests can be run as frequently as necessary because the AI readily works twenty-four hours per day, seven days per week. Most crucially, all of this can be done fast, with a higher probability of accuracy, in the background, and in real time.
Once QA engineers see this full scenario, they can decide which additional inquiries and fixes should be prioritized based on which features are most important to the client. Now, they are better equipped to analyze results and communicate results to stakeholders more quickly.
The final outcome is that customers continue to be satisfied and continue to spend money, which makes businesses delighted since profit replaces expense and wasted time.
Raw Data
AI: Perceive
Machine Learning
AI: Understand
Automate Process
AI: Act