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Qa consulting

QA consulting

We optimize your QA workflows so that you stay in control throughout software delivery


When to call on QA consultants

Genislab helps companies overcome all types of testing challenges and implement a QA strategy tailored to the desired outcomes.

Poor software quality

We design a comprehensive, systematic QA process to help you eliminate quality issues and bottlenecks.

Structural changes in the company

The Genislab team looks into legacy workflows and suggests how to best reshape them to fit into the transformed environment.

In-house QA underperformance

We review and fine-tune existing QA practices to ensure that quality requirements are met.

High testing costs

Our consultants optimize your spending on software QA without compromising on the quality or frequency of releases.

Adoption of new methodologies

We identify ways of retrofitting testing infrastructures in line with continuous testing, SAFe, Agile, and TDD practices.


Contact our team
Achieve maximum efficiency of your QA with Genislab’s consulting services.


Genislab’s quality assurance consulting process

For each case requiring consulting, we follow an established three-step process to provide a comprehensive coverage of software testing workflows in place and offer viable changes to each faulty aspect. To attend to the varying needs of our clients, we also have several collaboration schemes, each with a different service scope.

1. Discovery

The Genislab team starts with the following:

  • Interview key stakeholders
  • Analyze current workflows and practices
  • Determine deficiencies and their root causes

2. Strategy formulation

Next, our consultants proceed to:

  • Decide on the QA practices to be replaced or introduced
  • Identify the testing tool kit fit for purpose
  • Draw up a transformational roadmap

3. Implementation & transformation

We offer three distinct engagement modes for the final stage:

Full-on implementation by Genislab

  • Joining your QA team to implement full-scale transformation
  • Introducing new solutions
  • Monitoring their performance
  • Educating your staff about the reformed processes
  • Addressing any arising issues

Shared responsibility

  • Cooperating with your in-house QA team to adopt continuous changes
  • Taking on the agreed scope of tasks
  • Providing actionable recommendations to the team

Post-implementation support

  • Looking into the performance of reformed QA workflows on request
  • Assist in addressing any occurring bottlenecks and errors


The effects of QA consulting


Refined software testing strategy to meet quality gates


Transparent, measurable, and controllable QA processes


Testing infrastructure setup and optimization (tracking systems, testing environments)


More effective workload planning and collaboration between units involved in the SDLC


Faster product releases and shorter time to market


Enhanced end product quality and higher customer satisfaction level


Optimized software development and QA costs


Accumulation of best testing practices and continuous upskilling of in-house teams



Why Genislab?

With over 20 years in the software testing industry, we earned ourselves a reputation of knowledgeable and reliable quality assurance consulting partners due to our:

Talent-driven staffing policy

Genislab employs over 1,100 full-time QA professionals, possessing in-depth experience in numerous industries and testing disciplines. At our proprietary QA Academy, we help our employees broaden their knowledge, acquire and practice up-to-date skills.


Diverse experience

Our consultants have been implementing both manual and automated testing workflows and adapting Agile methodologies for QA for almost two decades now.

The culture of excellence

Being an ISO 9001 certified company, we master process maturity and constantly apply best practices to help clients get confident in the software quality.


How do we utilize AI to increase the quality of the solution? We distinguish between three
potential applications, namely translation, search and sequence anticipation and analysis
and elaborate on each of them below. Some of the examples we provide implement more
than one of the possible applications of AI to achieve their goal.

Translation means that one artifact is converted by the AI to another artifact.
API specifications, namely swagger documentation of the APIs, are translated into executing
code that tests the APIs. This can serve to ease the task of creating ATDD acceptance tests
once the interfaces of the APIs are defined.
Another translation could cluster an existing
set of regression tests in order to minimize them
yet another translates requirements to
their associated risk.
We envision that many more translations are possible, especially with the introduction of large language models new use cases will be explored. For example,

it is possible with state of the art large language models to translate description of a low
level programming task to useful code snippets

Using AI to search is a classical application of AI to testing.

For example genetic algorithms are used to test the solution for security vulnerabilities, e.g., AI search has also been applied to non-functional chaos directed test generation  and to functional
testing of APIs .

Many time the search is guided by an implicit or explicit coverage model that serves to guide the search. For example, in the case of security vulnerabilities models of code coverage are applied.

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

The last application of AI occurs through sequences and analysis.

Anomaly detection has been applied solution logs in order to analyze and debug the system. Even more exciting large language models with generative capabilities are attempting to anticipate the next program statement a programmer enters with some success.

Anomaly detection has nuances and a range of approaches that apply. For example, a black Friday activity on the system represents a change in the way the system is used but is not a stable ongoing change but a single abrupt occurrence. Techniques have been developed to identify change that is stable over time and requires a change in the solution. They are name drift analysis,