It also addresses the evolution of the QA role toward a more strategic profile, embedded within a quality model assisted by intelligent systems, where human intervention remains an essential factor for oversight, validation, and results control.

The Origins and Evolution of QA, and the Rise of AI

With the emergence of software and digital applications, quality control adopted a predominantly reactive approach focused almost exclusively on defect detection. However, the growing complexity of systems exposed the limitations of this model, driving a shift toward a more preventive and collaborative approach to quality assurance. This transition was supported by practices such as shift-left testing, test automation, and continuous testing within CI/CD environments — establishing QA as a core discipline within the software development lifecycle.

Against this backdrop, the rise of Artificial Intelligence introduced a new paradigm in how quality processes are conceived. This is not merely an incremental evolution, but a structural shift in the way validation processes are designed, prioritized, and executed.

The Impact of AI on the SDLC and QA

The impact of AI, however, has not been confined to QA alone. Its integration has unfolded progressively and transversally, affecting both development and validation phases — generating a direct impact on the final quality of software.

On one hand, development teams have incorporated generative AI tools for code generation, such as Copilot or Claude, significantly increasing delivery speed. Yet this advancement also introduces new risks related to the quality and maintainability of generated code, due to potential inconsistencies with the broader application context.

On the other hand, QA teams have integrated AI across multiple stages of the testing process, transforming the way quality assurance strategies are designed, executed, and maintained.

According to various industry reports — including QA and Software Testing in 2025 (based on over 100 development teams) and BrowserStack’s State of AI in Software Testing 2026 (based on over 250 technical leaders) — more than 60% of organizations have already incorporated AI into parts of their testing workflows, particularly in regression, smoke testing, and risk-based prioritization.

AI adoption is also extending to other areas of the SDLC, such as business analysis — where it supports requirements and feature definition — and design, facilitating the generation of interfaces and prototypes in tools like Figma. This reflects an increasingly transversal impact across the entire software development lifecycle.

As a result, the sense that AI has become a standard part of the toolstack for all stakeholders in the software development lifecycle is growing across the industry. This adoption is generating impact at both operational and strategic levels, redefining processes, roles, and quality metrics.

Benefits

Following several years of generative AI model adoption, the following key benefits can be identified within the QA domain:

• Test Case Generation: Automatic generation of test cases from code, functional requirements, or user stories.
  • Example: Given a user story such as “the user should be able to reset their password,” the system automatically generates cases covering valid/invalid passwords, expired sessions, multiple failed attempts, field format validations, and more.
• Test Prioritization: Intelligent test prioritization based on criticality, change impact, and risk analysis.
  • Example: Following a change to the checkout flow, the system automatically prioritizes tests related to tax calculations, discounts, and payment gateways.
• Log Analysis & Processing: Analysis, rewriting, and summarization of logs, along with detection of duplicate test cases or incidents.
  • Example: In an execution that has generated hundreds of log lines, the system groups repeated errors, summarizes the issue into a single incident, and reduces noise and manual analysis time.
• Self-Healing Tests: Automatic test maintenance, adapting to changes in interfaces or system flows.
  • Example: If a button changes from id="submit-btn" to id="submit-button", the system automatically updates the selector without requiring manual intervention.
• Root Cause Analysis: Automated failure analysis and support in identifying root causes.
  • Example: Faced with a login test failure, the system correlates backend logs, authentication changes, and database errors — suggesting a token service issue as the root cause.
• LLM-based Evaluation: Automated results evaluation using LLM models capable of analyzing test outputs, system responses, and logs to determine their validity or relevance based on defined criteria.
  • Example: Rather than validating only status codes, an LLM assesses whether an API error message is contextually coherent with the nature of the failure.
• Agentic Testing Systems: Autonomous agent-based systems capable of planning, exploring applications, generating scenarios, executing tests, and reporting results iteratively — adapting their behavior based on outcomes.
  • Example: An autonomous agent explores an application, identifies critical flows, dynamically generates tests, executes scenarios, and adjusts its strategy based on results.

Taken together, these advances accelerate the testing cycle across its various phases — analysis, design, execution, and reporting — particularly in well-structured environments with sufficient context available.

Risks

That said, AI integration also introduces significant new risks and limitations:

• Incomplete Test Cases: Generation of incomplete or incorrect test cases due to biases in training data. Some reports indicate that between 20% and 40% of automatically generated tests require manual review or correction.
  • Example: The system generates tests for a registration form but omits critical scenarios such as security validations, due to biases in the training data.
• Scenario Complexity: Difficulty modeling complex scenarios, particularly in critical systems.
  • Example: In a banking system, the model may fail to correctly represent flows that depend on multiple regulatory conditions, intermediate states, or external systems.
• Contextual Understanding Gaps: Difficulty detecting defects arising from business logic, system integration, or contextual coherence.
  • Example: A test passes at a technical level because the system fails to detect an incorrectly applied discount, not understanding the business logic associated with that promotion.
• False Positives/Negatives: Inaccurate defect detection — either reporting non-existent errors or failing to identify real failures under certain conditions.
  • Example: The system accepts an incorrect data result as valid because it is structurally and formally well-formed.
• Excessive Dependency: Potential erosion of technical knowledge within teams due to over-reliance on automated tooling.
• Automation Bias: A tendency to accept AI-generated results without sufficient validation. Research suggests that up to 30–40% of incorrect decisions made by AI systems go unchallenged.
• ROI: Difficulty objectively measuring the return on investment.
• Hallucinations: Model hallucinations — the generation of incorrect but apparently coherent results. Estimated rates range from 5% to 30% in complex tasks, depending on context.
• Non-Functional Testing: Limited capacity to deliver value in performance, scalability, security, or observability testing compared to functional testing.

These risks reflect a still-significant gap between the theoretical potential of AI and its actual performance in complex or critical contexts — where human oversight remains an essential element.

The Emergence of New Metrics

In this new landscape — where the integration of Large Language Models (LLMs) enables test case generation to be automated at scale — it becomes necessary to introduce new metrics capable of evaluating these non-deterministic systems through measurement approaches that go beyond simply quantifying how much is being tested, focusing instead on the real utility of that testing.

Unlike traditional testing, where outcomes are binary (pass/fail), AI-based systems require metrics that capture degrees of adequacy, coherence, and usefulness of the generated responses.

Some of the most relevant and emerging proposals include:

• Test Effectiveness Rate (TER): The proportion of tests that detect real defects relative to the total executed.
• Signal-to-Noise Ratio: The relationship between relevant results (valid defects) and generated noise (false positives or redundant tests).
• AI-generated Test Reliability: The degree of confidence in automatically generated test cases, assessed through cross-validation, golden datasets, or model-assisted review.
• Defect Detection Efficiency (DDE): The ability to detect defects in early stages of the development cycle.
• Actual Coverage vs. Generated Coverage: The difference between the theoretical coverage generated by AI and the effective coverage of critical functionalities.
• Test Maintenance Overhead: The effort required to maintain, correct, or filter automatically generated tests.
• LLM Evaluation Score: Assessment of the quality of generated responses using evaluator models (LLM-as-a-judge), based on criteria such as relevance, coherence, and correctness.
• Hallucination Rate: The proportion of AI-generated responses containing incorrect or unverifiable information.
• Task Success Rate: The percentage of tasks correctly completed by autonomous systems or AI-based assistants.
• Consistency Score: The degree of stability of generated responses when faced with equivalent or slightly modified inputs.

These metrics reflect a paradigm shift in quality evaluation — moving from a deterministic model based on coverage and execution, to a probabilistic model centered on the reliability, consistency, and utility of AI-assisted systems.

Adapting the QA Role in an AI-Assisted Environment

Beyond its impact on development and QA processes and on validation metrics, AI adoption is driving a significant transformation that directly affects the competencies and responsibilities of QA professionals.

Traditionally, the QA role focused on requirements analysis, test case design, test execution, and defect reporting. In the current context, this role is evolving toward a more strategic profile — oriented toward the oversight, validation, and governance of automated systems.

This consolidates the human-in-the-loop paradigm, in which the QA professional takes on supervisory, validation, and audit functions that may vary depending on the seniority of the profile.

Differential Impact by Experience Level

Junior profiles (testers) AI acts as an accelerator for learning and productivity, enabling:

• Assisted test case generation
• Standardization of defect reports
• Increased execution speed
• Reduced technical barrier to entry

Mid-level profiles (analysts) Value is centered on:

• Improved requirements analysis
• Supervision and validation of AI-generated scenarios
• Incorporation of business knowledge into models
• Identification of edge cases and complex dependencies

Senior profiles (leads) AI facilitates:

• Definition and optimization of quality strategies
• Advanced metrics analysis and new KPI development
• Filtering of noise generated by large-scale automation
• Alignment between technical quality and business objectives

Transversal capabilities Across all levels, a new key competency is emerging: the ability to craft effective prompts and provide adequate context to AI systems.

Knowledge of DevOps practices is also gaining relevance — enabling the integration of these systems into CI/CD pipelines and supporting selective test execution, where systems themselves determine which tests to run based on code changes, dependencies, and defect history, and prioritize them according to risk.

Feedback loops allow these systems to learn continuously from results, progressively optimizing coverage, prioritization, and testing effectiveness.

However, this advanced automation demands constant oversight to prevent biases, incorrect decisions, or loss of control over the quality process. As a result, the QA professional evolves into an orchestrator of quality in AI-assisted environments.

New Role: QA for AI Systems and Agents

Yet the transformation of QA from functional tester to quality orchestrator is not the only role-level shift the industry is experiencing.

The proliferation of AI-based systems introduces a new dimension in QA: the need to validate non-deterministic systems.

Unlike traditional software — where expected behavior is fixed and verifiable through deterministic assertions — AI systems generate probabilistic and variable outputs for the same input. As a result, QA must validate not so much the accuracy of a specific response, but the adequacy of behavior within an acceptable range. This involves assessing aspects such as:

• Coherence and relevance of responses
• Robustness against diverse or adversarial inputs
• Consistency of results when faced with equivalent inputs
• Presence of biases in generated responses
• Model degradation over time (model drift)

In this context, LLM evaluation frameworks become especially relevant — combining the use of golden datasets, automated evaluation through evaluator models (LLM-as-a-judge), and human validation.

In short, a new QA role is emerging — one in which the object of testing is no longer the various application types previously worked with, but rather the assurance of quality in non-deterministic models, where the validation focus shifts from expected outputs to the adequacy of behavior within a variable and acceptable range.

Costs and Challenges of AI Adoption in QA

All of this AI adoption and the transformation it drives across development and QA processes represents a significant investment — not only at the technological level, but also organizationally, operationally, and in terms of talent. This transformation, closely tied to the evolution of the QA role, introduces new demands that must be addressed from a strategic perspective.

Technical Costs

• Integration of AI tools into existing pipelines
• Architectural adaptation to support advanced automation
• Management of more complex infrastructures (processing, storage, observability)
• Need for additional tooling to monitor, audit, and validate AI systems

Operational Costs

• Increased process complexity
• Continuous oversight of automated systems
• Management of noise generated by large-scale automation
• Maintenance of models, prompts, and associated configurations

Organizational and Talent Costs

• Need for upskilling in new competencies (prompt engineering, AI literacy, DevOps)
• Greater demand for technically proficient profiles capable of validating AI-generated results
• Risk of technological dependency and loss of internal knowledge if not properly managed

Economic Costs

• Licensing fees for specialized AI-based tools
• Computational costs associated with advanced model usage
• Investment in team training and upskilling
• Potential increase in senior profiles required for oversight and validation

Various industry studies reflect that initial implementation costs can be significantly higher than those of traditional frameworks, particularly during integration phases. Furthermore, the lack of specialized talent and the difficulty of integrating with legacy systems rank among the main barriers to adoption — which ultimately depends on model maturation, organizational adaptation, and team learning curves.

Accordingly, AI adoption in QA must be approached as a medium-to-long-term strategic investment, not as an immediate cost optimization.

Substitution or Complementarity?

With all of the above in mind, let us address one of the most recurring debates in the industry: will Artificial Intelligence replace QA professionals?

Current evidence points clearly toward a scenario of complementarity. AI acts as a co-pilot that automates repetitive, low-value tasks — allowing professionals to focus on higher-complexity activities such as exploratory testing, complex scenario validation, user experience evaluation, and contextual analysis, playing a more strategic role centered on validation, oversight, and decision-making.

In fact, academic research indicates that AI adoption in testing still lags behind its use in development — evidencing a testing gap where human capabilities remain critical to guaranteeing the final quality of software.

Ultimately, far from disappearing, the role is evolving: the greater the automation, the greater the need for oversight, technical judgment, and business understanding.

As Margarita Simonova notes in the Forbes Technology Council piece The State of Testing in 2025: AI suggests, but the decision still belongs to humans.

Conclusion

Artificial Intelligence has established itself as a transformative force in QA, redefining both the processes and the roles associated with quality assurance.

Far from representing a threat, its adoption constitutes an opportunity to evolve toward a more efficient, strategic, and contextually aligned model — one suited to the growing complexity of modern software development.

In a context characterized by the acceleration of code generation and the mass production of software, QA takes on an even more critical role as a guarantor of quality. The effective integration of AI will enable professionals not only to increase their productivity, but also to reinforce their positioning as key actors within the SDLC.

Nevertheless, a realistic perspective is essential in the current climate of heightened expectations around AI. While its capabilities are significant, its implementation is far from fully autonomous or free of limitations. Issues such as inconsistent output generation, lack of business context, the presence of biases, and the need for constant oversight demonstrate that these technologies still require substantial human intervention.

In this sense, the value of AI lies not in replacing the QA professional, but in amplifying their capabilities. The gap between expected potential and current reality stems largely from the quality of integration, the adequacy of context provided, and the critical capacity of teams to interpret and validate AI-generated results.

In this new landscape, competitive advantage will not reside merely in adopting AI, but in the ability to integrate it critically, efficiently, and in alignment with product quality objectives. Because, ultimately, quality is not a property of software — it is the result of the decisions made by those who build and validate it.

References:

BrowserStack. (2026). State of AI in Software Testing 2026. Recuperado de https://www.browserstack.com/blog/inside-the-state-of-ai-in-software-testing-2026/

CopilotQA. (2025). QA and Software Testing in 2025: Trends, Challenges, and AI Adoption. Recuperado de https://copilotqa.com/qa-and-software-testing-in-2025/

Forbes Technology Council. (2025). The State of Testing in 2025: The AI Adoption Gap. Recuperado de https://www.forbes.com/councils/forbestechcouncil/2025/12/15/the-state-of-testing-in-2025-the-ai-adoption-gap/

Forbes Technology Council. (2025). AI Is About to Reshape Millions of Software QA Jobs. Recuperado de https://www.forbes.com/councils/forbestechcouncil/2025/10/06/ai-is-about-to-reshape-millions-of-software-qa-jobs/

Wifitalents. (2025). AI in Quality Assurance Testing: Statistics and Trends. Recuperado de https://wifitalents.com/ai-quality-assurance-testing-industry-statistics/

Anthropic. (2024). Understanding AI Hallucinations and Model Behavior. Recuperado de https://www.anthropic.com/research

Financial Times. (2025). AI hallucinations become a growing concern for enterprises. Recuperado de https://www.ft.com/content/e074d3a9-7fd8-447d-ac0a-e0de756ac5c5

arXiv. (2026). An Empirical Study on AI-Assisted Software Testing in Real-World Repositories. Recuperado de https://arxiv.org/abs/2603.13724

arXiv. (2026). The Testing Gap: Adoption of AI in Software Development vs Quality Assurance. Recuperado de https://arxiv.org/abs/2601.21305

arXiv. (2025). Challenges and Limitations of AI in Software Testing: A Systematic Review. Recuperado de https://arxiv.org/abs/2504.04921

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During my time at Capitole, I've learned to push my own limits with passion and creativity, working alongside incredible teammates and giving my best in every project.

Irene DuranSenior QA Engineer

My experience with Capitole Consulting has allowed me to collaborate with diverse clients and industries, leading large-scale global projects. ... Throughout these experiences, one thing has remained constant: Capitole's close relationship with its consultants, the continuous learning opportunities they provide, and the overall satisfaction of the team.

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Oscar SotoSenior IT Program Manager

Capitole is the home that has nurtured much of my professional growth. I always feel comfortable presenting new ideas and proposals, and I have no doubt it will continue to be the place that supports my future steps as well.

Sara AbarrouBusiness Manager​

Since joining Capitole in 2023, I've found much more than just a professional project: a team that welcomed me with open arms from the very beginning. ... From the start, I've received exceptional treatment and have been fortunate to find people I now consider friends, not just colleagues. Over these years, I've been able to grow professionally, taking on new challenges and expanding my responsibilities. I'm incredibly grateful for the trust placed in me and look forward to continuing to grow and contribute alongside this fantastic team.

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Angela LópezService Manager

These years at Capitole have been a great experience thanks to the close support and continuous guidance they provide on a daily basis. ... I greatly value the constant training opportunities they offer us to continue growing. Furthermore, there's a strong team culture where professionals are truly valued. It's the everyday details that make all the difference and make you feel at home, I’ve felt fully supported every step of the way.

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Jorge MessinaScrum Master

The post Careers appeared first on Capitole.

Digital transformation solutions that revolutionize your industry

Unleash digital transformation solutions for your industry, drive innovation, and optimize processes to create real impact. We’re here to elevate your business.​

Explore our solutions

Guiding financial institutions through the complexities of tech solutions to streamline processes and enhance security.

Includes:

• Retail Banking
• Corporate and Investment Banking
• Financial Services
• Insurance
• Holding Companies
• Reinsurance
• Brokers

• Social Protection
• Public Pensions
• Asset Management
• Private Banking
• Payments
• Stock Market

Transforming the retail customer experience and empowering our clients in the development of marketplaces and digital sales and distribution channels.

Includes:

• Fashion and Apparel
• Jewellery and Watches
• Cosmetics and Personal Care
• Food and Beverages
• Home and Décor
• Tourism and Travel
• Consumer Technology

Transforming telecommunications and media with cutting-edge technology to deliver connectivity and content distribution that meets the expectations of the most demanding customers.

Includes:

• Telecommunications
• Media
• Entertainment
• Advertising & Marketing

• Information Technology
• Streaming and Digital Content
• Social Media
• Cable and Satellite Television

We are the partner for the growth of tech development teams, whether for digital services or products, or for applying digitalisation to enhance services.

Includes:

• Software Manufacturers
• Consulting
• Health and Wellness
• Education and Training

• Transportation and Logistics
• Real Estate Services
• Marketing and Advertising
• Cleaning and Maintenance Services

We drive the digital transformation of public organizations, helping them adapt to the future and proactively address current challenges.​

Includes:

• ​Public Administration​​
• ​Public Education​​
• ​Public Health​​
• ​Security and Defense​​
• ​Social Services​​

• ​Public Transportation​​
• ​Waste Management and Environment​​
• ​Infrastructure and Public Works​​
• ​Culture and Leisure​​
• ​Regulation and Oversight​

Supporting our clients in ensuring precision engineering for critical systems, prioritising safety and innovation.​

Includes:

• Aircraft Manufacturing​
• Aerospace Components​
• Satellites​
• Space Exploration​
• Weapon Systems​
• Defence Technology​

• Shipbuilding​
• Navigation and Control Systems​
• Maintenance, Repair, and Overhaul (MRO)​
• Simulation and Training​
• Research and Development​

We optimise operational efficiency while ensuring safety and sustainability in the automotive and railway sectors by delivering innovative technologies.​

Includes:

• Land Transportation​​
• Automobile Manufacturing​​
• Automotive Components​​
• Automotive Technology​​
• Train Construction​​
• Railway Infrastructure​​

• Railway Service Operations​​
• Supply Chain Logistics​​
• Maintenance and Repair​​
• Transport Innovation​​
• Electric Vehicle Charging Solutions​

We drive healthcare and life sciences through digitalization innovation, advancing both research and patient care.

Includes:

• ​Drug development and manufacturing​
• ​Vaccines​
• ​Medical biotechnology​
• ​Agricultural biotechnology​
• ​Medical equipment manufacturing​
• ​Diagnostic technologies​

• ​Research centers​
• ​Clinical trials​
• ​Hospitals and clinics​
• ​Telemedicine​
• ​Dietary supplements​
• ​Epidemiology

Pioneering sustainable solutions for the energy transition, addressing evolving needs, and supporting our clients in navigating the transition and the challenges of emissions reduction.​

Includes:

• Renewable energy​
• Conventional energy​
• Electrical grids​
• Natural gas distribution​
• Water supply​
• Wastewater treatment​
• Recycling and composting​
• Hazardous waste disposal​

• Energy efficiency technologies​
• Energy audits​
• Energy markets​
• Supply contracts​
• Smart grids​
• Energy storage​
• Energy equipment​
• Renewable energy technologies

Optimizing processes, digitalizing, and implementing intelligent solutions to enhance productivity and operational efficiency.​

Includes:

• Machinery and Equipment​
• Industrial Automation​
• Electronic Devices​
• Electronic Components​
• Measuring and Control Equipment​
• Industrial Communication Systems​
• Networks and Communication​

• SCADA Systems​
• Programmable Logic Controllers (PLCs)​
• Packaging​
• Chemicals, Plastics, Paints​
• Semiconductors​
• Construction Materials

Shaping the future of sustainable agriculture and revolutionizing the food and beverage industry through innovative technology.​

​​Includes:

• Food Manufacturing​
• Beverage Manufacturing​
• Agriculture​
• Livestock Farming​
• Animal Feed Production

Using technology to enhance and optimize travel and hospitality, whether through technological advancements, operational efficiency, or customer satisfaction.​

Includes:

• Tourism​​
• Travel Agencies​​
• Airlines​​
• Maritime Transport​​
• Hotels​​
• Restaurants​​

The post Industries appeared first on Capitole.

Revolutionizing
your digital journey with innovative solutions​

We provide comprehensive technological solutions that optimize, streamline, and transform your business to ensure sustainable growth and long-term success.

Contact our team

Unleash the power of our
cutting-edge technology solutions​

We drive your growth with innovative solutions to help you stand out in a constantly evolving world.

Software QA Data & AI Infra & Cyber Engineering Methods & Transformation

Software Development

Transforming business challenges into solid, secure software solutions aligned with each organization’s technological evolution. Architecture, engineering, and development come together to build maintainable systems ready to evolve, leveraging Spec-Driven Development and AI agents to accelerate delivery, reduce errors, and raise the quality bar.

• Architecture

Scalable, resilient, and secure software architectures designed to sustain the evolution of digital products and platforms; reinforced by patterns, technical standards, and design principles that support maintainability, quality, and solution governance.

• Digital Software

From application assessment and modernization to brand-new products, every solution is tailored, engineered to best practices, and built with quality and automation at its core, accelerating value delivery at every stage.

• Integrated Software

Systems, services, and channels connected through APIs, events, and enterprise integration platforms, creating traceable, governed solutions that reduce operational errors, improve efficiency, and facilitate the evolution of the digital ecosystem.

• Enterprise Software

Extending and customizing enterprise platforms across CRM, ERP, document management, corporate portals, e-commerce solutions, and digital workplaces. Each platform is adapted to business processes, with usability, security, governance, and operational efficiency at the forefront.

• Embedded Software

Embedded software for specialized hardware and connected devices, optimized for technically constrained environments requiring high reliability and continuous operation.

• UX/UI

User-centered digital experiences shaped by UX research, information architecture, interaction design, and visual design, resulting in intuitive, accessible, and consistent interfaces that simplify use and enhance the digital experience.

​

Contact us

Quality Assurance

We maintain constant quality control at every stage of development, overseeing each phase through verification processes and continuous testing to ensure the highest quality standards.​

• QA Auditing and Design

We analyze and optimize your quality processes, designing a tailored strategic plan to achieve peak excellence and competitiveness.​

• Testing

From design to the execution of functional tests (Integration, Regression, E2E, UAT…) and non-functional tests (Performance, Compatibility, Maintainability…), we ensure your applications meet the highest quality standards, guaranteeing optimal performance and reliability.

• Reporting

We provide key reports and documentation that deliver full visibility into your applications’ status, enabling smarter decision-making and optimizing development processes.​

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Data & AI

End-to-end AI solutions built across the full spectrum, from training custom machine learning models and computer vision systems to deploying agentic AI architectures powered by both open-source and proprietary models, following spec-driven development practices and deployed across major cloud platforms.

• SQL and NoSQL Databases

Data storage solutions designed and optimized for their intended use, deploying SQL and NoSQL systems based on key parameters such as flexibility, scalability, performance, and ease of use in production environments.

• Data Architecture

Robust, scalable architectures built across different environments and cloud platforms, designed to enhance data analysis and exploitation capabilities with an AI-centric vision that enables system evolution.

• Data Warehouses

Implementing the latest trends in data management and analytics, from Data Lakehouse and Data Mesh to Data Fabric and Data Hub, always selecting the most suitable approach for each organization’s needs and characteristics.

• Data Visualization

Value extracted from stored information, developing relevant insights that support better decision-making across the organization.

• Data Governance

Governance models defined and implemented to ensure regulatory compliance within organizations, analyzing risks and action plans across corporate environments.

• MLOPS/ LLMOPS

Designing, deploying, and maintaining continuous delivery environments for AI needs, with proprietary and third-party solutions spanning cloud and on-premise infrastructures to facilitate AI governance, security, and control within each organization.

• Automatization

Driving end-to-end hyperautomation strategies as a technology partner from opportunity identification through to continuous operation and evolution. Process discovery and modeling, backlog building and prioritization, and scalable solution design are supported across technologies such as UiPath, Microsoft Power Platform, and n8n, with architectural standards ensuring traceability, observability, and governance through logging, monitoring, and business KPIs that measure and optimize the impact of each automation.

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Infra & Cyber

We protect organizations’ digital assets with top-tier cybersecurity and advanced risk management strategies, implementing measures that ensure maximum protection.

• Cloud

We manage and optimize your cloud infrastructure, guaranteeing efficiency and data protection.​

• Cloud Architecture and Systems

We design scalable and secure cloud solutions that optimize performance and efficiency, ensuring smooth integrations, working on automation design and PaaS architectures.​

• ITSM (Support)

We provide comprehensive solutions to resolve incidents efficiently, ensuring continuous system availability and improving user experience. We integrate AI into our solutions to transform reactive services into proactive ones.​

• Cybersecurity Strategy

We design tailored strategies to protect your assets and mitigate risks, ensuring the security of your data and systems against cyber threats and attacks.​

• SOC, Pentest

We conduct thorough tests to identify vulnerabilities and improve incident response, strengthening your security in the process.​

• DevSecOps

We automate and secure the software lifecycle to protect your applications from threats and vulnerabilities.​

• Data Protection

We implement advanced solutions to safeguard your data from unauthorized access and loss.​

Contact us

Engineering

Comprehensive engineering solutions, from product design and development to commissioning and maintenance. Our expertise spans product design, systems engineering, supply chain operations, and industrial operations. We leverage technologies such as IoT, AR/VR, and OT systems to become the digital partner for industries.

• Product Design & Systems Engineering

Design and development of products using a systems-oriented approach, including mechanics, electronics, electrical systems, communications, and embedded software.

• Industry 4.0 - 5.0

Expertise across the entire OT system pyramid, from PLCs and sensors to MES systems, along with predictive maintenance solutions to extend the lifespan of your assets.

• Commissioning

Provision of resources and solutions for industrial asset construction projects, including the sensorization of facilities and distributed assets.

• Supply Chain Operations

Product quality processes and procurement of parts and materials, supporting both product industrialization and construction projects.

• Edge, IoT, Sensors

Industrial communications including sensors in a factory, defense radars or IOT assets monitoring. We provide expertise in communications from low level signal processing to Bus industrial comms.

Contact us

Methods & Transformation​

We power organizational transformation with dynamic practices that streamline processes, ignite a culture of continuous innovation, and seamlessly guide the shift to modern ways of working.

• Agile scale

We align cross-functional teams to accelerate value delivery, improving collaboration and efficiency across all areas.​

• Program Management

We coordinate multiple projects to optimize resources and minimize risks, applying both agile and traditional methodologies.

• Migration Projects

We facilitate the transition to new platforms with a comprehensive approach that ensures secure and efficient data transfer.

• Enterprise Architecture

We design frameworks that provide a clear view of processes, systems, and technologies, enabling informed decision-making.

• Systems Engineering

We design, implement, and optimize systems that align with your business needs, ensuring efficient and secure operations.

• Procurement Strategy

We evaluate and optimize your supply chain to ensure you get the right resources at the right time.

• Captive & Hub (BOT)

We create operations centers that enable businesses to outsource specific functions, optimizing costs and improving efficiency.

• Innovation Center

We create a collaborative environment that drives research and development, allowing businesses to explore new ideas and technologies.

Contact us

Strategic alliances with industry leaders

Success​ Cases

Discover how our tech solutions have transformed businesses and delivered outstanding results.​

Boosting performance: The key to effective testing

We enhanced efficiency and scalability through performance testing, integrating agile testing into development to optimize critical applications.

Discover the full case

Time and cost reduction: The AI and Big Data revolution​

We optimized processes with AI and Big Data, saving up to 96 hours per project and 3,000 hours annually on manual tasks. Discover how we boosted efficiency in an innovative way!

Discover the full case

Scaling to success: A game-changing Tech Hub​

We established a high-performance hub in Barcelona, assembling a team of experts in record time, successfully meeting all set SLAs.

Discover the full case

Capitole has been a key partner in the development of our IT Hub in Barcelona. Thanks to their expertise in software development and Data Engineering, we were able to build a team of 35 professionals specialized in key technologies like ReactJS, Java, and Python in record time.​... Their agile approach and deep knowledge of AWS data infrastructure allowed us to design and deploy robust pipelines that significantly improved the efficiency of our data analysis processes. This translated into more informed, real-time strategic decisions.​ Capitole's ability to adapt to our specific needs, combined with their constant commitment to quality and results, has been crucial to the success of this project. We couldn’t have reached these milestones without their support.​

Read the full testimonial here

Stephane PrioletCTO Mano Mano

I hired Capitole in its early days and have had the pleasure of watching them grow over the years, even after our contracts ended. From the start, their approach was personal and always focused on providing solutions. What truly set them apart was their ability to integrate seamlessly into our team, committing fully during both good times and challenges.... They not only brought expert knowledge but also great people who enriched and improved the team dynamic. Working with them was a positive and enriching experience that left a lasting mark on our journey.​

Read the full testimonial here

Fernando Sánchez Desigual

At WK Spain, a leader in the Spanish market for SaaS applications for accounting, payroll, and taxation management, we worked with Capitole for over two years to accelerate our load testing. We started with two FTEs and expanded to three, always receiving a service tailored to our needs.... Capitole provided a technical leader, Florent, who designed and developed efficient test scenarios. This allowed us, in the second year, to integrate lighter tests into our delivery pipelines and execute them on-demand. Thanks to their support, we were able to identify potential component failures early when scaling, achieving a level of maturity in two years that would have taken much longer without them. Capitole continues to offer this service to WK Spain, demonstrating their commitment to best practices and allowing our internal team to focus on other critical aspects. I highly recommend their service to companies looking to implement advanced load testing efficiently and sustainably.

Read the full testimonial here

Damien Peteau Wolters Kluwer

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The post Solutions appeared first on Capitole.

In this post, we will discuss the digital transformation applied to the financial sector.

Thanks to the advancements in technology, the financial industry is undergoing a complete overhaul in all aspects. We are seeing a reduction in the number of bank branches, and customers are increasingly encouraged to carry out their financial operations through online banking or mobile applications.

The financial sector is digitizing in this regard to compete in an increasingly digital environment. Digitization is improving and transforming the way financial operations are carried out, improving efficiency, reducing costs, as well as offering more personalized and convenient financial services and products to customers.

Benefits of digital transformation

We are witnessing a total process of process automation in which the benefits of digital transformation for the customer/consumer are, among others, the following:

• Greater accessibility and convenience when carrying out operations or transactions anywhere in the world. It is no longer necessary to go to your branch to simply make an immediate transfer. Now it can be done from online banking or from the App.
• Greater time savings, the time to perform an operation or transaction. The focus is on immediacy, with a single click, a series of processes are triggered in milliseconds that allow the operation to be carried out.
• Reduction of operating costs for financial institutions. Bank branches are eliminated, allowing these costs to be allocated to new digitalization and security projects for online banking and applications.
• Increased security in financial operations. The security of operations is intensified by using encryption and authentication technologies to protect customer accounts and operations. Transactions are traceable, enabling the fight against money laundering and bank fraud.
• More efficiency and accuracy. By using technology, the operational risk arising from human activity is eliminated as far as possible. This provides greater transparency for the customer.

All of this digital transformation is driven by the following technological advances:

• Broadband networks and smart mobile devices
• Cloud computing services
• Exploitation of large amounts of data (Big Data) and artificial intelligence (AI)

Changes in business models

Digital transformation has changed the way we engage with customers giving rise to new services and business models. Likewise, it has also lowered some of the barriers to entry that made the financial sector an unconnected market. The result is a disruption that is transforming the competitive framework and the structure of the financial services market.

Technological advances have led to the emergence of new service and distribution channels for financial services such as electronic banking portals, first, and then mobile banking applications that meet the conditions of immediacy, worldwide and simplicity, conditions required by consumers and the new generations to be connected at any time and in any part of the world for their daily operations.

The growth of technology in the financial sector also increases the risk of financial institutions suffering from cyberattacks, compromising customer data or falling victim to fraud. In order to reduce cyber risks, it is essential that both financial institutions and public organisations provide digital education.

In short, from a competitive perspective, the digitalisation of the financial sector means that electronic portals and mobile applications allow providers to target larger markets and benefit from economies of scale without the need to establish and maintain an extensive physical network of branches.

This opportunity is being seized by new financial service providers with a highly digital distribution model. In addition, digital channels tend to increase the comparability of products and services across providers and reduce switching costs for consumers.

At Capitole, we support our financial institution clients in this digital transformation of their systems through technology projects.

Iván Garrido
Business Analyst at Capitole Consulting

The post Digital transformation applied to the financial sector appeared first on Capitole.