Next generation ai lab

advance automation & validation

Auto-Test Generation:


Combining best practices and emerging tech such as AI and ML to build your automated QA workflows
we follow through with a proven strategy to deliver thorough, value-driven service. These are the key milestones of our refined QA automation roadmap:

    Automation scope definition


    Tool selection


    Framework implementation


    Environment configuration


    Test data preparation


    Test script development


    Test run and result analysis


Automated test support and monitoring


Rely on automated testing to:

Save effort and time on high-volume testing activities

Enhance the breadth of in-house testing capabilities without hiring extra human resources

Eliminate human error in high-risk QA processes

Spot errors and malfunctions in the released software as soon as they occur


What test automation brings to your SDLC

  • Acceleration

    Automation facilitates shifting quality assurance left and uncovering critical bugs early in the SDLC.

  • Testing cycle optimization

    Automation allows for tapping into Agile practices, such as task-based and behavior-driven development.

  • DevOps transformation

    Automation enables setting up a continuous testing environment, which is an inherent part of DevOps.

  • Granular quality assessment

    Automation makes it possible to embed quality gates into the testing lifecycle, thus strengthening quality control.

  • Competitive edge

    Automation is rapidly replacing manual and model-based QA, becoming an essential requirement in any competitive software development project.

Our Notable AI Agents for QA

We polish software quality and speed up testing cycles leveraging the following types of testing:


Performance testing

With server- and client-side performance testing types and a special focus on scalability and load testing, we ensure high loading speed. We verify that software smoothly operates under maximum loads daily and during peak seasons, such as Cyber Week, New Year, Independence Day, and Labour Day sales, and other shopping events.


Localization and internationalization testing

When testing eCommerce websites, we verify their operation and usability in different regions. We ensure the translation doesn’t contain any gaps and grammar, spelling, and punctuation mistakes. We also check if currency and other symbols match cultural specifics.


Integration testing

We provide smooth interaction of eCommerce apps with extensions, CRMs, ERPs, inventory management systems, and email marketing products and ensure smooth data flow between them.


User experience testing

With usability testing, we verify that eCommerce websites are intuitive and easy to use to identify areas for improvement and create a better online shopping experience.


Cybersecurity testing

To prevent hacker attacks, protect personal and financial customer data, and spot exploitable vulnerabilities, we apply security testing tailored to clients’ business needs and software specifics, including penetration and compliance checkups.


Data migration testing

Client data should be carefully migrated when moving to another eCommerce platform or in case of a significant legacy system upgrade. We verify that information is transferred fully and without duplicates.


Functional testing

Our testers screen crucial elements, such as a homepage, search bar, navigation, product details pages and lists of recommended products, pricing, shopping cart, operations with credit cards, diverse payment options, social media integration, and post-purchase functionality to confirm the flawless operation of our clients’ eCommerce solution.


Automated testing

In addition to manual testing, we set up automated QA workflows from scratch and enhance existing test automation solutions to help eCommerce companies cope with high regression testing scope, accelerate app testing time, decrease costs, increase test coverage, and reduce human errors.

Compatibility testing

We help eCommerce companies ensure a consistent user experience regardless of the browser, OS, screen resolution, connectivity protocols, or other variables by performing cross-browser and cross-platform testing.

AI-powered remediation

More advanced applications and AI-powered tools today can process security alerts and offer users step-by-step remediation instructions based on input from the user, resulting in more effective and tailored remediation recommendations. 

Enhaned threat intelligence using generative AI

Generative AI is increasingly being deployed in cybersecurity solutions to transform how analysts work. Rather than relying on complex query languages, operations, and reverse engineering to analyze vast amounts of data to understand threats, analysts can rely on generative AI algorithms that automatically scan code and network traffic for threats and provide rich insights. 

Google’s Cloud Security AI Workbench is a prominent example. This suite of cybersecurity tools is powered by a specialized AI language model called Sec-PaLM and helps analysts find, summarize, and act on security threats. Take VirusTotal Code Insight, which is powered by Security AI Workbench, for example. Code Insight produces natural language summaries of code snippets in order to help security experts analyze and explain the behavior of malicious scripts. This can enhance their ability to detect and mitigate potential attacks. 

Stronger password security using LLMs

While scary to think of this power in the hands of hackers, AI also has the potential to improve password security in the right hands. Large language models (LLMs) trained on extensive password breaches like PassGPT have the potential to enhance the complexity of generated passwords as well as password strength estimation algorithms. This can help improve individuals’ password hygiene and the accuracy of current strength estimators. 

Dynamic deception capabilities via AI

While malicious actors will look to capitalize on AI capabilities to fuel deception techniques such as deepfakes, AI can also be used to power deception techniques that defend organizations against advanced threats. 

AI-assisted development

The goal is to reduce breaches, improve the nation’s cybersecurity, and reduce developers’ ongoing maintenance and patching costs. However, it will likely increase development costs.  

AI-based patch management

AI-based patch management systems can help identify, prioritize, and even address vulnerabilities with much less manual intervention required than legacy systems. This allows security teams to reduce risk without increasing their workload. 

Automated penetration testing

Penetration testing is a complex, multi-step process that involves gathering information about a company’s environment, identifying threats and vulnerabilities, and then exploiting those vulnerabilities to try to gain access to systems or data. AI can help simplify these parts of the process by quickly and efficiently scanning networks and gathering other data and then determining the best course of action or exploitation pathway for the pen tester. 

AI-powered risk assessments

AI is also being used to automate risk assessments, improving accuracy and reliability and saving cybersecurity teams significant time. These types of AI tools can evaluate and analyze risks based on existing data from a risk library and other data sources, and automatically generate risk reports. 

Our toolkit

The Home of Machine Learning


Release Content Drafter

Generation of artefacts dedicated for external use - User manuals


Release Content fine-tuning

Fine-tuning and improvement of created content


Generate test cases automatically based on the natural language input, ensuring comprehensive test coverage

  • Product guide generation
  • Deployment Auditing and Evaluation of Models
  • Audio Transcription
  • Labeling Data Translation Models
  • Pre-Training
  • Semantic Search
  • Increasing Inference Speed
  • Image Reducing Latency of Models

Our toolkit

One window Solution


Functional/configuration recommender

Assistant for improvements towards optimal product configuration settings and compatibility


Service Recommendation Guide

Interactive, step-by-step guiding support and recommender system for service and maintenance tasks


Functional Requirements Detector

NLP techniques are applied to analyze and understand the requirements documentation, user stories, and other relevant documents associated with system.


Engineering Co-pilot

Assistant tool supporting in the creation, recommendation and improvement of code, test-cases as well as code documentation and translation


Service Supportbot

Service tool providing prompt and immediate support through a Q&A-format


Code & Test-Case Generator (No-code/Low-code)

Creation of code scripts from scratch (no-code) with content input or with low coding knowledge requirements (low-code)

Join our AI Accelerator program

Trending Autonomous AI Agents

Genislab AI Usecase:Websites

Intuitive Assistance,Dynamic Search,Tailored Experience,Immediate Responses,Learning & Adapting,Feedback Loop,Content-Aware,Scalable & Customizable

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NextGen Validation Contracts

GeoGPT+ Tutorial: Web-Ready Map Visuals From GIS Forest Fire Data

Author(s): John Loewen, PhD Originally published on Towards AI. Step-by-step guide on using the GPT-4 GeoGPT+ plug-inDall-E image: impressionist painting of a heat map on a computer screen hovering over a forest fire GeoGPT+ improves geospatial analysis by providing real-time data integration and visualization of spatial data. What kind of mapping can GPTGeo+ (Geo+) create for me? A month or so ago, I wrote an article on how to leverage GPT-4 to create Python data visualization code to visualize a map of forest fires near my home. I have a NASA Forest Fires dataset in CSV format (with fire location data and intensity). It is ready for use, and I am curious what functionality is available with the Geo+ tool. Let’s find out what Geo+ can do with this data. The forest fire situation in Canada over the past 10 years or so has become very bad. Particularly in British Columbia, where I am from. Leading to awful pollution days like this: Forest fire smoke on the left, normal day on the right (Photo: David Loewen) To show the monthly effects of forest fires, I want to create a data visual that shows forest fires over time (by month) for British Columbia. And with all of it’s visualization capabilities, I want Geo+ to help me out! Let’s work together and step through how to get this done. I… Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI

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Machine Learning in Chemistry

Author(s): Tony Flores Originally published on Towards AI. Image adapted from Adobe Stock Machine learning is becoming a significant tool in the field of chemistry, providing new opportunities in various areas such as drug discovery and materials science. Machine learning algorithms, especially neural networks, are effective at identifying complex patterns in chemical data, which can lead to new insights and speed up processes that were previously dependent on traditional, more time-consuming methods. As we examine the impact of machine learning on chemistry, we will look at its uses, and how it not only simplifies regular tasks but also leads to advancements in understanding molecular complexities. Recently, the combination of machine learning and chemistry has made significant progress. Researchers are using advanced models like CNNs and RNNs for tasks such as creating new drugs, predicting toxicology, and modeling quantitative structure-activity relationships. The pursuit of models that are interpretable and explainable is becoming more important, giving scientists a better understanding of why predictions are made. Additionally, the use of multi-modal data and the development of transfer learning techniques are expanding what can be achieved in predicting material properties and optimizing synthesis planning. These recent trends highlight the growing collaboration between machine learning and chemistry, pushing scientific research into new areas and influencing… Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI

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Jacek Zukowski

Some challenges came from operating on various language versions of SAP, but in most cases, we have selectors that can match the data.

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State of Art Cyber Security through Continuous adaptive model training

  • Advanced Persistent Threats (APT) Management
  • Architecture and Design
  • Business Continuity and Disaster Recovery
  • Cloud Security
  • Communication and Reporting
  • Cryptography and PKI
  • Data Analysis and Interpretation
  • Digital Forensics
  • GovernanceRiskand Compliance
  • Hacking
  • Identity and Access Management
  • Incident Management and Disaster Recovery Planning
  • Incident Response
  • Information Security Management and Strategy
  • Legal and Ethical Considerations
  • Malware Analysis
  • Network Security
  • Penetration Testing and Vulnerability Assessment
  • Physical Security
  • Regulatory Compliance
  • Risk Management
  • Scripting
  • Secure Software Development Lifecycle (SDLC)
  • Security in Emerging Technologies
  • Security Operations and Monitoring
  • Social Engineering and Human Factors
  • Software and Systems Security
  • Technologies and Tools
  • Threats Attacks and Vulnerabilities
On the top of Mistral fine-tune model with 22,000 hand-crafted cybersecurity and hacking-related data pairs.