Solutions

To build a scalable and reliable ERP system, we focused on designing a modular architecture that could support multiple business domains while maintaining consistency across shared data and workflows. In the development process, we prioritized flexibility, allowing business rules and processes to be introduced through configuration rather than constant code changes.

At the same time, we ensured system stability through structured implementation of cross-module interactions and controlled release cycles. Testing was integrated into the development process to verify data integrity, business logic, and system behavior under real operational conditions.

Key solutions

• Modular ERP architecture design. We structured the system into interconnected modules for HR, finance, and operations to enable scalability and maintainability.
• Unified data model implementation. Core entities were standardized across modules to ensure consistency and eliminate data duplication.
• Configuration-driven business logic. Business rules were implemented through flexible settings, reducing the need for code-level changes.
• Custom module development. We built tailored functionality to support internal workflows not covered by out-of-the-box ERP features.
• Seamless third-party integrations. APIs were used to connect external systems and ensure reliable data exchange.
• Role-based access control. Precise permission models were implemented to support different departments and user roles.
• Performance optimization. We improved system responsiveness through query optimization and efficient data handling.
• Controlled deployment and updates. Release processes were structured to introduce new features without disrupting ongoing operations.

Solutions

We developed a secure, role-based corporate wellness platform that unified activity tracking, wearable synchronization, gamified challenges, and HR analytics into a single scalable system. The architecture was designed to protect sensitive health-related data while supporting thousands of concurrent users across regions.

AI-assisted development helped us accelerate feature delivery, while senior engineers ensured regulatory compliance, stable integrations with wearable devices, and reliable reporting accuracy for HR stakeholders.

Key solutions implemented:

• CI/CD pipeline for controlled and frequent updates
• Role-based access control for employees and HR managers
• Multi-device wearable integration with stable data synchronization
• Gamified challenges and engagement features
• Real-time activity tracking and analytics dashboards
• Secure authentication and encrypted data processing
• Scalable cloud infrastructure for multi-region rollout
• Automated reporting tools for HR and management

Challenge

When we joined the project, the platform was already in active use but lacked the structured testing practices needed to support stable, large-scale growth. New features were being released frequently, but without systematic validation, bugs were slipping through, and users occasionally experienced performance drops or inconsistent behavior across browsers.

The client needed more than just bug fixing — they required a partner who could build a complete testing process from scratch and ensure seamless collaboration with their in-house development team. We also had to address several technical and organizational gaps that stood in the way of product quality and delivery speed.

Key challenges included:

• Tight release cycles with little time for quality checks
• No established QA process or documentation
• Frequent regressions after new releases
• Limited test coverage across devices and environments
• Absence of in-depth accessibility testing
• Need for performance testing under realistic load conditions

Solutions

We started by assembling a dedicated QA team to work alongside the client’s developers and product owners. Our first step was to establish clear testing processes, create documentation, and define acceptance criteria for ongoing development. With a structured approach in place, we could test early and often, preventing defects rather than just detecting them.

Our team also helped identify critical areas for test coverage and introduced systematic regression testing. We expanded testing across browsers and devices, ensured compliance with accessibility standards, and monitored performance with each release to ensure consistency and responsiveness for users.

Key activities included:

• Improving issue reporting and traceability
• Designing and implementing end-to-end QA workflows
• Creating and maintaining regression test suites
• Performing cross-browser and cross-platform compatibility testing
• Running accessibility audits and fixing critical issues
• Conducting exploratory testing for newly developed features
• Participating in sprint planning and retrospective sessions

Challenge

The client wanted to strengthen their product offering with AI-driven capabilities that would make everyday work simpler and more efficient for end users. However, reaching this goal required overcoming several obstacles that directly impacted business value.

Key challenges the client’s team faced included:

• Time-consuming reporting — employees had to rely on technical skills or developer-prepared queries to extract insights from company data.
• Inefficient workflows — routine questions about sales, stock, or suppliers took significant time to resolve manually.
• User adoption risks — without a modern, intuitive interface, the solution could struggle to attract new clients and stand out in a competitive market.
• Cost concerns — existing third-party chatbot tools were either too limited or too expensive to be a sustainable option.
• Evolving requirements — the client’s vision was still forming, and many specifications had to be clarified and refined during development.
• Handling unstructured and large documents — standard parsers failed on files over 10 pages, requiring custom development.
• Integrating structured and unstructured data — the chatbot needed to combine insights from synchronized database tables and uploaded documents.
• Performance bottlenecks — ensuring acceptable response times under heavy queries was a recurring challenge.
• Scalability for future clients — the architecture had to support eventual multi-tenant use without extensive rework.

With the desire to move beyond traditional reporting tools and deliver an intelligent, user-friendly experience to their customers, the client turned to us to bring the chatbot idea to life.

Solutions

To address the client’s requirements, our team designed and delivered a modular AI-powered chatbot that could be seamlessly integrated into their inventory and sales management platform. The solution was built with scalability, cost-efficiency, and ease of use in mind, combining Google Cloud services, a modern frontend, and intelligent data handling.

Modular architecture

The system was structured as a set of independent yet interconnected modules, which ensured flexibility, easy scaling, and simplified future enhancements:

• Data ingestion and parsing — a secure upload flow with short-lived links allowы users to import files safely. A custom parser was developed to handle large documents exceeding standard model limits, enabling the chatbot to process certificates, reports, and images efficiently.
• Structured data synchronization — dedicated modules synchronized sales, inventory, and supplier tables from the client’s database with BigQuery, ensuring the chatbot always worked with up-to-date records.
• Vectorization and retrieval — documents and table data were embedded and stored in BigQuery’s vector index. Similarity search (cosine and Euclidean distance) was used to retrieve the most relevant context for each query.
• LLM-powered query processing — Vertex AI with Gemini models (including Gemini 1.5 Flash for fast responses) served as the reasoning engine. Queries were reformulated against the retrieved context, ensuring accurate, domain-specific answers.
• Frontend interface — a web app built with Next.js provided a responsive, browser-based UI. AG Grid was integrated to give users Excel-like capabilities for working with large data tables, supporting import/export and advanced filtering.
• Security and access control — single sign-on enabled users to authenticate via their existing systems. Pre-signed URLs ensured secure file uploads, and role-based access was built into the architecture.
• Scalability and performance — the backend services were deployed on Cloud Run, enabling serverless autoscaling for handling query spikes. Performance improvements included caching frequently asked questions and categorizing documents to optimize search efficiency.

Implementation highlights

• Cost-efficient AI integration — by combining Vertex AI with in-house retrieval, the solution avoided the high per-query costs of third-party chatbots, making the system financially sustainable.
• Future-ready design — the architecture was built with multitenancy in mind, preparing the client to onboard new customers with minimal additional development.
• Developer-friendly environment — unit tests and modular design ensured quick iterations, while the use of widely adopted frameworks like Next.js and AG Grid simplified maintenance and onboarding.
• User-centric interface — the chatbot was embedded directly into the client’s product workflow, allowing users to move seamlessly from their system to the chatbot without switching tools.

By combining these modules, the chatbot delivered a smooth and reliable experience, enabling users to interact with both structured and unstructured data as naturally as asking a colleague.

Challenge

The client’s logistics management platform had been running on an outdated monolithic architecture that no longer met the company’s growing operational demands. Over time, performance degraded, updates became increasingly risky, and integrations with new tools, such as cloud analytics or fleet tracking, were impossible without significant manual work.

The system’s limited scalability also made it difficult to process higher shipment volumes, while inconsistent data synchronization between modules caused billing errors and delayed delivery reporting. The client needed a way to modernize their platform without pausing active logistics operations, ensuring data integrity, compatibility with existing infrastructure, and measurable performance improvements.

Key challenges:

• No clear process for automated testing or continuous deployment
• Outdated .NET framework and monolithic architecture limiting scalability and integrations
• Slow system performance during peak shipment processing periods
• Frequent data inconsistencies between order tracking and billing modules
• Lack of real-time analytics and visibility into operational metrics
• High maintenance costs due to legacy code dependencies
• Limited support for API-based integrations with modern logistics tools
• Manual report generation slowing down decision-making
• Risk of data loss during system updates or server migrations

Solutions

Our goal was to modernize the client’s legacy logistics system without disrupting active freight operations. We started by analyzing the existing monolithic .NET codebase and gradually transitioning it to a microservices architecture hosted on AWS, ensuring stable performance and scalability.

Modernization included rebuilding the core modules for order processing, billing, and route tracking using .NET, Node.js, and PostgreSQL while introducing new APIs for smooth data exchange between systems. A React-based web interface replaced the outdated frontend, giving dispatchers and accountants a faster, more intuitive workspace.

To improve data integrity and reliability, we implemented automated testing through TestRail and Jira, set up CI/CD pipelines, and introduced monitoring tools for proactive maintenance. The project also added real-time analytics dashboards powered by Python, helping managers identify shipment delays, cost overruns, and route inefficiencies instantly.

Implemented solutions:

• Established continuous system monitoring to detect and resolve issues early
• Migrated monolithic architecture to modular, cloud-based microservices on AWS
• Rebuilt core logistics and billing modules using .NET and Node.js for better maintainability
• Developed REST APIs for seamless integration with third-party analytics and tracking tools
• Replaced outdated frontend with a responsive React interface for dispatch and billing teams
• Added automated test coverage and CI/CD pipelines for faster, safer deployments
• Implemented real-time analytics dashboards for shipment status and cost tracking
• Optimized PostgreSQL data structure to improve query speed and reporting accuracy
• Integrated Docker-based containers for isolated, reliable environment management

Challenge

The client’s logistics operations were growing rapidly, but their existing Odoo setup lacked the flexibility to handle real-time coordination between multiple regional warehouses. Dispatchers relied on spreadsheets and manual scheduling, leading to frequent route overlaps, inconsistent delivery times, and limited shipment visibility.

Additionally, the system wasn’t integrated with their warehouse and fleet management tools, making it difficult to track vehicle locations or update delivery statuses automatically. The client needed a modular, scalable solution that could extend Odoo’s capabilities without disrupting day-to-day operations.

Key challenges:

• Growing operational costs caused by manual data entry and poor synchronization between teams
• No unified transportation management system to handle route planning and delivery coordination
• Manual dispatching processes increasing the risk of scheduling conflicts and human error
• Lack of real-time tracking for vehicles and shipments across multiple regions
• Data inconsistencies between Odoo and third-party warehouse management tools
• Limited automation in driver assignment and route optimization
• Insufficient visibility into performance metrics such as delivery times and fleet utilization
• Difficulty scaling logistics operations within the existing Odoo framework
• Outdated workflows that slowed down decision-making and response to delivery changes

Solutions

To address the client’s growing logistics complexity, our team designed and implemented a custom Odoo module fully integrated into their existing ERP and warehouse management systems. The goal was to extend Odoo’s functionality without overhauling the client’s infrastructure while introducing automation and real-time data flow across all logistics processes.

We developed a transportation management component in Odoo using Python, PostgreSQL, and REST APIs for secure communication with external tools. The React frontend provided dispatchers with an intuitive interface for route visualization, vehicle status monitoring, and driver assignment. The backend included Node.js microservices hosted on AWS to ensure performance stability, scalability, and continuous availability.

Implemented solutions:

• Enhanced data security and access control across logistics modules using role-based permissions
• Designed a transportation management module within Odoo for automated route creation and driver scheduling
• Integrated real-time vehicle tracking via REST APIs connecting Odoo with the client’s fleet tracking system
• Built a React-based dashboard displaying live delivery progress and route optimization insights
• Automated status synchronization between Odoo and warehouse management systems to eliminate manual data entry
• Implemented microservices on AWS for scalable processing of delivery and route data
• Added analytics tools for monitoring delivery times, route efficiency, and fleet utilization
• Set up continuous testing and deployment pipelines to ensure reliability during future system updates

Challenge

Despite being live for several years, the client’s mobile apps faced growing QA challenges. A small internal QA team was stretched thin, fragmented processes slowed releases, and insufficient documentation blocked automation. The distributed development environment further complicated collaboration, leading to inefficiencies and an increased risk of critical bugs reaching production.

Key challenges:

• Unstructured release testing lacking transparency and repeatability.
• Minimal internal QA resources balancing support and testing tasks.
• Disconnected workflows across time zones and distributed teams.
• Outdated test documentation limiting test coverage and automation.
• Low visibility into edge-case and payment error scenarios.
• Inconsistent app performance across diverse Android and iOS devices.
• Manual testing bottlenecks restricting QA scalability.

Solutions

We embedded a dedicated team of manual testers who took full ownership of mobile release testing, bringing structure, transparency, and flexibility to QA processes. Our team rebuilt test documentation, aligned distributed teams, and implemented workflows that replaced ad-hoc practices with reliable, repeatable release cycles.

By adjusting our work schedules, we ensured smooth real-time collaboration during critical phases while enabling near-continuous testing coverage across time zones. Proactive participation in process discussions and proposing QA improvements allowed us to contribute beyond routine testing tasks.

Key activities:

• Establishing stable, repeatable release QA workflows.
• Rebuilding and expanding test documentation for automation readiness.
• Thoroughly testing critical payment error scenarios.
• Synchronizing QA efforts across distributed teams.
• Employing time zone differences for near round-the-clock testing.
• Adjusting work hours for real-time collaboration during releases.
• Driving process improvements and QA best practices.
• Scaling QA efforts without expanding in-house resources.

Challenge

When we joined the project, development was nearly complete, but no testing had been done. There was no test strategy, no documentation, and no quality control in place. Our job was to validate the entire system from scratch and ensure that everything, from user uploads to large-screen broadcasts, worked seamlessly across different devices and conditions. Key challenges included testing the platform under simulated live-event conditions, ensuring stable cross-platform communication, handling large video files, and covering different mobile devices and browsers in our testing environment.

Solutions

We began by auditing all three applications and mapping out their interconnections. From there, we built a structured test checklist to guide our QA process. We used TestRTC and Dotcom-Monitor to simulate file uploads of different sizes, video processing, and 100+ users accessing the system at once. Testing was done on both real devices (2 iPhones and 2 Android phones) and cloud-based environments via Sauce Labs to expand device coverage. Our team worked in close coordination with developers through daily syncs and ongoing communication. Originally planned as a one-month project, the client extended the collaboration to six months based on the value we delivered early on.

Challenge

The client’s product for this project, a test management tool, was designed to be integrated with Jira and other issue tracking applications. After a series of updates, the client needed to make sure that the updated version of the product could be released and retained all of the functionality users had come to expect. Specifically, our scope of tasks included:

• Verifying the existing functionality
• Checking the presence of all UI elements
• Making sure that each UI element delivers the anticipated response
• Testing the compatibility of the solution with different platforms
• Confirming the correct localization of the product in four new languages
• Ensuring proper integration of the solution with third-party tools

The major challenge of this project was working under strict timeline constraints. For example, our team had to create a large number of test cases (200+) from scratch in a limited time. Another challenge was to plan and test all the possible integrations of the product with third-party solutions.

Solutions

Having collaborated with the client on multiple projects in the past, we were able to build effective collaboration from the start within time constraints. We appreciated the availability of comprehensive documentation that allowed us to quickly get the hang of the features that needed to be tested.

We started by enhancing the existing test suite, making sure that every possible feature of the product is covered by our testing efforts. Then we moved on to following our initial test plan. We also paid special attention to all the possible integrations of the product with third-party services to ensure the correct operation of the solution across all potential combinations.

The team for this project consisted of four manual QAs and one part-time QA lead. The types of testing we performed included:

• Smoke testing
• UI/UX testing
• Functional testing
• Confirmation testing
• Cross-browser testing
• Integration testing

Through weekly meetings with the project stakeholders and the development team, we achieved complete transparency and were able to quickly remove any roadblocks and get timely feedback to move forward with the project.

Challenge

Founded over a decade ago, the client’s company went from a small startup to one of the leading businesses in the travel industry. The product is available as a web application that is also compatible with mobile browsers, as well as native mobile apps for iOS and Android. Still, even though the company has been conquering the market for years with their increasingly popular product, there hasn’t been an established quality assurance procedure in place — testing was unsystematic, usually carried out by developers, and a significant amount of bugs were being found by the end users, which is the least desirable outcome for an up-and-coming solution.

Due to the limited attention paid to quality assurance for years since the product launched, and the absence of a comprehensive bug tracking system or project documentation, our team had to not just test the software, but also implement a reporting and documentation system from scratch. Moreover, the company did not follow any schedule for their releases, unveiling updates on a seemingly random basis, which also made the quality assurance process more difficult.

During the years of working on the project, we helped ensure the quality of 30+ versions of the three apps, each containing valuable updates and improvements. Depending on the scope of requirements, we performed the following types of testing:

• Functional testing
• UI/UX testing
• Compatibility testing
• Regression testing
• Localization testing
• Smoke testing
• Confirmation testing
• API testing
• Exploratory testing

It’s also worth noting that our team working on the project has remained the same throughout the entire collaboration, which means they are now ingrained in the development and delivery process and have even helped onboard new team members on the client’s side. Our team for this project included:

• 1 QA Team Lead
• 1 Manual QA

Through regular meetings and effective communication via Slack, our team can quickly react to changes in the application and developer requests, speeding up the release process and making sure the apps are quickly approved by the App Store and Google Play Store.

Solutions

We typically start the work on a testing project with reviewing the documentation and existing test cases, but with no documentation or test cases to review, our team had to establish the testing process with nothing to base it upon. However, that is something we have plenty of experience with. After spending some time investigating the development process and existing bug management practices, we were able to build a thorough process for documenting bugs, communicating with two distributed development teams, and tracking the progress of the discovered issues with the software.

We introduced GitHub as a bug tracking system that could also be easily integrated into the overall development ecosystem used by the client. Before our team came on board of the project, the release schedule was somewhat chaotic, but our efficiency and speed allowed the client to speed up the release process and gain more confidence in the solution. We also built an effective communication procedure with the client’s development team, so there were little to no delays and misunderstandings that could potentially damage the project.