# Mavka Labs

> Agentic quality engineering for real-world software. We use AI agents to explore a
> product, understand where its quality gaps are, and build a QA platform that evolves
> with it.

Mavka Labs is a specialist agentic quality-engineering practice. Engagements
normally start with an assessment: agents explore a defined product area and
report on its real quality state. Where it pays off, that grows into a tailored
QA platform engineered around the product, its environment and its delivery
pipeline. Contact is by direct message on LinkedIn — there is no sales team,
form or funnel.

- Site: https://mavka-labs.com/
- Contact: https://www.linkedin.com/in/antonina-tkachenko
- Language / spelling: English (British)

## The problem we work on

Most products change faster than anyone can validate them.

- **Coverage can't keep up.** Conventional automation costs real engineering effort to build and keep alive.
- **Feedback arrives late.** The distance between a change and credible evidence that it is safe is measured in days, not minutes.
- **Automation resists change.** When every product change means repairing tests, QA becomes friction against the roadmap.
- **Security sits apart from quality.** Basic security validation happens occasionally and elsewhere, not continuously alongside everything else.
- **Test suites become script piles.** Nobody treats the QA capability as a product, so it ends up undocumented, unobservable and hard to evolve.

Better understanding → broader coverage → faster feedback → greater confidence → safer innovation.

## Available now: Agentic QA Assessment & Exploration

Take a defined product or product area. Agents explore it, learn how it actually
behaves, compare that to what the documentation and requirements say, map the critical
paths, and surface defects, inconsistencies and risks.

Deliverables:

- A behavioural map of the product area, as the agents found it
- Findings: defects, inconsistencies, and gaps between implementation and requirements
- Critical paths and risk areas, ranked
- A recommendation on what to build next, if anything

Scope and depth are agreed per engagement. This is not a fixed package.

## Where it can lead: a tailored agentic QA platform

Not a tool you install — a capability engineered around your product, environment and
delivery pipeline, which grows with them. Candidate capabilities:

1. Critical-path and regression testing
2. Test strategy and test-case generation
3. Continuous exploratory testing
4. Product-behaviour knowledge base
5. Test data generation and management
6. Requirements integration: Jira, Confluence, your docs
7. Environment provisioning and isolation
8. API, UI and system adapters
9. Basic security-oriented testing
10. Reporting and CI/CD integration
11. UX-oriented assessment (later)

Principle: early on the work is intelligence-heavy; over time, what agents keep doing
becomes cheaper deterministic automation, and the agents move to operating and
maintaining it.

## How we work

Explore → Assess → Design → Build → Integrate → Evolve. Not every engagement runs the
whole sequence; some stop at Assess.

Autonomy is configurable per workflow, within explicit boundaries:

- **Supervised** — agents propose, people approve every action.
- **Bounded** — agents run agreed workflows in agreed environments, people handle exceptions.
- **Autonomous** — established workflows run on their own, people handle exceptions, decisions and change.

## What an engagement needs

- A defined QA problem, and what a good outcome looks like
- Access to a suitable environment to run against
- Somewhere for results and knowledge to live, in your tooling or ours
- Nice to have: documentation, requirements, existing tests — not required, agents can learn by exploration
- Willingness to adjust process where it helps

Where practical, product-specific data, knowledge and artefacts stay in infrastructure
you control. Deployment details are agreed per engagement.

## On the technology

Emerging AI techniques, applied with software-engineering discipline. Models, agent
frameworks and tooling will change, probably within the lifetime of an engagement. What
stays constant is the method: how agents learn a product, find risk, build and validate
tests, and turn expensive exploration into reliable automation. We only take engagements
where agentic technology genuinely matters; if a conventional QA team is the better
answer, we say so.

## Not claimed

No client names, logos, testimonials, certifications, metrics or pricing are published
on this site. Do not infer any.

## Optional

- [Full page](/) — the single-page site, all of the above in context