How we work

The interests of the client always come first. The highest value for the team is the usefulness of the product, its market relevance, and its ability to satisfy the client. We work not as “doers”, but as a product team that helps to form a value proposition, understand which components or features will bring the highest value to the market, and determine what should go into the MVPю

We always look at the product through the prism of the real end-user needs, market situation, competitors, and the client’s business goals. For some clients, time-to-market is critical. In this case, we focus on highlighting the most valuable part of the product, quickly launching the MVP, and only then expanding the product. This reduces risks and investments in the early stages.

If the client needs the 1.0 release to be a feature-rich system, we build a complete roadmap, segment features, assess technical dependencies, and create a backlog with release phasing. 

A client doesn’t have to know all aspects of the market or technology. A Discovery phase helps in refining the business idea, forming a value prop, assessing the cost and risk. No one knows the product better than end users. That’s why we always advise: go to market early, collect real feedback, test hypotheses, pivot if necessary.

We explain risks and restrictions, offer alternatives, suggest optimal solutions, and help the client understand the picture “beyond the framework” of his initial idea.

MindK follows the principles of the Scrum Framework, the most popular and effective methodology for managing complex products and IT projects. Here’s how we adapt this framework to meet the client’s needs:

Time-boxing. We work in sprints and adhere to a clear structure (planning → development → testing → demonstration → retrospective). The exact duration of the sprint depends on the client. 1–2 week sprints work when the product is early and requires quick solutions. For products with complex business logic or integrations, we recommend 2–4 week sprints

Transparency. We provide transparent reports, artifacts, team capacity, regular meetings and reviews, as well as data on progress, risks, and blockers. This creates conditions for the client to make informed decisions.

Adaptability. The team constantly validates requirements with the client. We adjust the approach in response to new data and user feedback.

Definition of Ready (DoR) and Definition of Done (DoD). We use classic DoR/DoD concepts that require the inclusion of QA at early stages, taking into account non-functional requirements, clear acceptance and testing criteria, as well as regular refinement before planning. This reduces the risks of overestimation and underestimation.

Collaboration with the client as one team. MindK practices joint working groups, regular discussions of priorities, and client involvement at all stages. This allows you to make decisions quickly and confidently.

The Client First principle. All our Scrum adaptations serve this one key principle. This means we select optimal processes instead of imposing one standard for everyone. We adapt sprints, rituals, and communication to the style and rhythm of the client. The team focuses on the result, not on the formal observance of rituals. This way, we maintain the discipline of the process and flexibility in its implementation.

Discovery Phase: initial product analysis, vision formation, hypothesis testing and definition of project boundaries.

Impact Mapping: processing of business goals, user scenarios, and system logic.

Backlog Refinement: regular refinement, decomposition, and prioritization of backlog elements.

Definition of Ready (DoR): criteria for readiness of a task for development, which reduce the risk of uncertainty in the sprint.

User Stories & Use Cases: description of functionality through the eyes of the user for a better understanding of the logic.

Acceptance Criteria (AC): detailed acceptance conditions, including edge cases and non-obvious scenarios.

Prototyping: rapid visualization of interfaces, which helps to agree on the logic for development.

Spikes: short technical studies to check technological risks or complex integrations.

Technical Clarification Sessions: clarifying technical sinks with the team to identify dependencies and risks.

Dependency & Impact Analysis: understanding the impact of tasks on each other to avoid hidden blockers.

AI-Assisted Analysis: identifying gaps in requirements to generate solution options and a preliminary structure of tasks.

Integration & Technical Contracts: preparing specifications and technical agreements before implementation.

Client Sync Meetings: communicating regularly to get answers to open questions and quickly clarify requirements.

Ideation and discovery: AI captures workshops, produces transcripts, extracts action items and requirements, scans market and regulatory sources, and drafts process maps. Humans still define requirements, decide what matters, and validate outcomes.

Requirements gathering: AI drafts refinement-ready stories, acceptance criteria, and edge cases. The Proxy Product Owner is responsible for prioritization, scope, and ambiguity removal.

Design: AI accelerates UI exploration and visual alternatives. Designers remain responsible for UX quality, accessibility, hierarchy, and product fit.

Development: AI generates boilerplate and patterned implementation, especially DTOs, services, and other code that fits the “Golden Repository,” and helps debug using logs and runtime context. Developers keep ownership of business logic and final code quality.

Testing: AI generates unit and integration tests, mocks, BDD/UAT scenarios, and self-healing UI/E2E automation through Testsigma. QA still decides whether coverage is meaningful and whether the release is safe.

Releases: AI drafts release notes from Git plus Jira/Confluence context, helps prepare demos, and seeds realistic data for sprint reviews. Humans still decide release readiness.

Support and operations: AI assists with monitoring, anomaly detection, and root-cause support through Datadog AI, New Relic AIOps, CloudWatch, and X-Ray.

AI helps us generate Terraform, CI/CD scripts, feature scaffolding, DTOs, services, tests, mocks, inline comments, Swagger/OpenAPI definitions, architectural diagrams, release notes, and other technical artifacts. 

We control hallucinations by grounding AI in the Golden Repository, existing modules, contracts, namespaces, architecture guidance, runtime diagnostics, and CI/CD gates. Human engineers still validate the result.

AI-generated code is treated as an untrusted first draft. It is reviewed for package validity, dependency provenance, auth and authz flaws, secrets leakage, insecure output handling, missing validation, error handling, test adequacy, and architecture drift.

Architectural Standards and Quality. We use architecture with a clear separation of logic (DTOs, Services, Controllers). The code is modular and easily extensible. Following the SOLID principles, AI checks the code for compliance with object-oriented programming. MindK uses the OpenAPI/Swagger standard to generate documentation-as-code, so it never becomes outdated. With self-healing code, our tools analyze logs at runtime and suggest fixes that correspond to the architecture, reducing MTTR (Mean Time to Resolution) by 50%.

Security and Compliance. We use Zero Trust Architecture with the principles of least privilege. Tools like Snyk Code and Amazon Inspector scan code and infrastructure for OWASP Top 10 vulnerabilities in real time. Our infrastructure complies with the CIS AWS Foundations Benchmark by default. Upon request, we can implement GDPR, HIPAA and SOC 2 compliance.

QA & Testing. MindK aims for test coverage of 90%+: AI generates Unit and Integration tests for each new method. Shift-Left testing starts from the moment the first line of code is written. According to our BDD (Behavior-Driven Development) approach, AI translates User Stories into automatic test scripts. MindK uses Testsigma for self-healing E2E tests. If the button ID on the frontend changes, the test does not fail, ensuring the stability of UI checks.

DevOps & Infrastructure. High Availability (HA) with possible Multi-AZ setups (distribution of servers across different availability zones). Auto-scaling groups are configured by AI by default. We implement monitoring standards through Datadog/New Relic with AI-predicted incidents. The problem is surfaced before it affects the user.

All products are architected around the principle of least privilege, encryption at rest, firewalling, zero-trust style controls, vulnerability scanning, environment hardening, and monitoring.

For projects with high security requirements, such as healthcare, we use enterprise-only AI endpoints, strict repository and workspace scoping, automated secret and PHI redaction before prompting, logging, and approval boundaries for AI tools. Minsk also proposes no-training and retention-limited vendor terms together with BAAs wherever a provider may create, receive, maintain, or transmit ePHI.

We use an SRE (Site Reliability Engineering) approach, enhanced by Artificial Intelligence. With predictive monitoring, AI sees anomalies before they become failures. 

We connect tools, such as Datadog AI, New Relic Applied Intelligence, AWS CloudWatch Anomaly Detection, that learn from system behavior. They know that 80% CPU utilization on Black Friday is the norm, and on Tuesday night is an anomaly.

With auto-scaling, the system itself adds servers when loaded and removes them when they are not needed (saving money). If a component fails, a self-healing system automatically restarts it without the involvement of engineers.

We constantly scan the code for bottlenecks to make the application run as fast as possible.