
How we work
Build production-ready software up to 75% faster with agentic engineering, MindK's ready-to-use agents and AI building blocks. We balance this speed with a strong focus on AI governance, security, HIPAA compliance, and oversight by Senior-level engineers..
Don't view MindK as just a vendor. We co-develop software as partners, with the same level of care dedicated to our own products.
Lean team + AI
When time-to-market is critical, MindK recommends our agentic engineering framework. You get a production-ready system, delivered up to 75% faster with a team of one Solution Architect and a (part-time) Proxy Product Owner.
01Scrum team
For complex software, we provide a full-fledged Scrum team. The exact roles are tailored to each individual project, including AI Developers, QA, DevOps, and Data Engineers; PM/Delivery Managers, Product Owner, Designers, and Tech Leads
02You get the same Senior engineers in both approaches, just faster with AI. This allows humans to focus on high-value tasks and decisions that require expert judgment.
Business value above feature delivery
The client's interests always come first. We judge all work by the usefulness of the product and its market relevance.
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Your product's business value is our North Star
We help you form a value proposition and discern must-haves from expensive distractions. Every feature is viewed through the lens of the larger market, user needs, competitors, and the client’s business goals.
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We're responsible for the result, not just implementation
MindK takes ownership of the product metrics and market performance when others might only care about delivering features on time.
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The market defines the product's direction
Often, what seems like “secondary” features turns out to be the key to success. The team validates every assumption and adjusts the roadmap in response to new data.
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An architecture that's meant to adapt easily
We design software that’s easy to extend or take in a new direction without an expensive rebuild. You don’t get the same problems associated with vibe-coded apps.
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We look beyond the product
You don’t have to know all about the target market, tech or regulatory restrictions. It’s our job to offer alternatives, explain the risks and limitations, help you adapt business processes and team roles.
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100% transparency
We collaborate side by side with the client. The team has regular discussions of priorities and open data on progress, risks, and blockers.
Product discovery
- AI accelerates: meeting capture, requirements structuring, market and competitor research.
- Humans own: scope decisions, risk assessment, and the trade-offs behind them.
- What you get: technical understanding of how to achieve the business goals, early visibility into risks and dependencies.
Design and setup
- AI accelerates: environment scaffolding, CI/CD boilerplate, baseline security config.
- Humans own: architecture, deployment model, and security baseline decisions.
- What you get: production-ready infrastructure and CI/CD, reliability and security controls from the start.
Iterative development
- AI accelerates: boilerplate code, first-draft tests, documentation updates, log analysis, first-pass debugging.
- Humans own: engineering judgment, code review, quality gates on every change.
- What you get: new features delivered in small, reviewable increments.
Testing and go-live preparation
- AI accelerates: UAT and BDD scenarios, end-to-end tests, release notes.
- Humans own: production readiness, edge cases, integrations.
- What you get: release readiness backed by coverage of real user journeys and fewer regressions
Support & improvement
- AI accelerates: monitoring, anomaly detection, incident analysis, backlog refinement.
- Humans own: prioritizing fixes and planning iterations from usage data.
- What you get: fast response to issues and post-launch work driven by real usage.
Ready-made AI foundation for your product
Voice Automation
Runs inbound and outbound calls end-to-end, including payer calls with long hold times, and returns both a transcript and the structured data extracted from the conversation.
Chat Automation
Holds multi-turn conversations in your product or on your site, executing actions against your systems mid-conversation instead of only answering questions.
Prospect Scoring & Sales Outreach
Ranks accounts and open opportunities against your closed-won patterns and enrichment data, then drafts the first-touch message from each account's own signals.
Document (Report/CV) Evaluation
Reads a document against a defined rubric and returns a structured assessment with the passage that justified each rating.
Lead Intake & Qualification
Captures inbound leads from forms, email, and phone, asks the qualifying questions, and writes a normalized record into your CRM with a disposition attached.
IVR Navigation
Works through payer and vendor phone trees, entering identifiers and menu selections to reach a representative or a data endpoint without a person waiting on the line.
Web Portal Automation
Signs into third-party portals that expose no API, retrieves or submits what the workflow needs, and captures evidence of what was done.
Interactive Q&A for Course Promotion
Answers prospective-student questions about curriculum, prerequisites, schedule, and cost from your own course material, then captures the contact when the interest is real.
Fax, SMS, and Email Automation
Sends and receives across legacy channels, parses inbound documents into structured records, and routes them into the right workflow.
Data Normalization
Maps records arriving from different EMRs, clearinghouses, and vendors onto one internal schema, including the code sets and payer identifiers that differ by source.
Data Cleansing and Enrichment
Removes duplicates, repairs malformed fields, and fills gaps from external sources, attaching a confidence value to every value it changes.
Human-in-the-Loop Review
Routes any output below a confidence threshold or inside a defined risk category to a named reviewer, and logs the decision.
Source-Grounded Output
Constrains output and action to your retrieved documents and returns the supporting passage with each answer.
Sensitive Data / PHI Anonymization
Detects sensitive information, strips PHI from the data that goes into external AI services, and restores the missing info in the UI.
Verification of Insurance Benefits
Retrieves plan-level benefit detail (deductible status, copay, coinsurance, visit limits, authorization requirements) from payer portals and calls, and returns it as a structured record before the patient arrives.
Eligibility Verification
Confirms active coverage and plan identifiers with the payer ahead of each visit and flags appointments where coverage changed since the last encounter.
Prior Authorization
Determines whether a medical procedure requires authorization, assembles documents the payer asks for, submits them, and tracks the case until a determination comes back.
Claim Automation
Scrubs claims against payer-specific rules before submission, then monitors adjudication and routes denials into the right work queue with the reason code already attached.
What
our
clients
say
Innovation balanced by robust AI governance
Quality controlled before sprint 1
Human review of AI-generated code
Sensitive data is stripped before reaching external models
Every AI-assisted change leaves an audit trail
Let's work together
Let us know about your technology challenges and we'll contact you
within 24 hours to schedule a free strategy session.
FAQ
- What are the core values driving your collaboration with a client?
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.
- Do you use any classical approaches to software development or their modifications?
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.
- How do you reduce uncertainty inherent ot software projects?
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.
- How exactly do you use AI at each stage of the SDLC?
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.
- What components of code are generated by AI? How do you detect hallucinations and technical errors?
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.
- What quality standards do you use?
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.
- How do you ensure the security of confidential code and data on AI platforms?
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.
- How do you ensure reliability and performance in production?
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.