Ivan Katkov
Selected work/Case 07

Independent AI product · Founder build · Independent product

Moving an AI video-generation product from idea through launch

I combined product strategy, UX, full-stack implementation and AI workflow integration to launch a focused video-content MVP.

0→1product pathIdea through launch
AIgeneration workflowUser-facing content creation
Founderdecision scopeProduct and engineering ownership
CASE / 07 SYSTEM ONLINE
Moy AIAn end-to-end AI content product for the Russian market

IMAGE PLACEHOLDER · Replace through public/images/cases and the case-study data file.

01 · Executive summary

The shortest honest version.

Situation

Russian-market creators needed a focused path from prompt and product input to generated video content.

Intervention

I designed the user flow, implemented the application and integrated the AI generation workflow.

Result

The idea became a launched MVP and a direct test of product, architecture and prioritization decisions.

02 · Context

Why the engineering work mattered.

Moy AI was an independent experiment in simplifying AI-assisted video-content creation.

The project required choosing a narrow user loop instead of building a general AI platform.

03 · System design

A clear path from signal to production.

01Creative input

Focused prompt and configuration experience.

02Job API

Validated requests and generation state.

03AI workflow

Provider orchestration and asynchronous processing.

04Result

Generated asset status and user delivery.

Constraints that shaped the solution

  • Limited independent-product resources.
  • Variable AI generation latency.
  • Need for clear job and error states.
  • Pressure to validate the product before expanding scope.

04 · Decision ledger

The trade-offs behind the implementation.

DecisionReasonTrade-offOutcome
Design around one creation loop

A broad editor would delay validation.

Fewer customization paths in the first release.

The MVP remained buildable and understandable.

Expose generation state

AI jobs do not complete like normal requests.

More UI states and backend coordination.

Waiting and failure behavior became explicit to users.

05 · Change

Before and after.

BeforeBroad AI-video concept

AfterFocused creation workflow

BeforeProvider-level API behavior

AfterProduct-level job states

BeforeProduct hypothesis

AfterLaunched MVP

06 · Outcomes

Technical change translated into team value.

Technical
  • Built the core full-stack generation workflow.
  • Connected user-facing states to asynchronous AI processing.
  • Delivered a launchable independent product.
Organizational
  • Made product, UX and engineering decisions in one ownership loop.
  • Prioritized a coherent MVP over platform breadth.
  • Gained direct evidence from shipping rather than planning alone.

07 · Evidence boundary

Precise claims build more trust than inflated ones.

  1. No adoption or revenue figures are claimed without source data.
  2. The case focuses on direct founder and implementation responsibility.
  3. Provider-specific details may change; the workflow model is the durable artifact.

08 · Reflection

What the project changed in my engineering judgment.

What worked

A narrow creation loop made the product possible to ship and exposed the real operational complexity of AI generation.

What I would improve next

I would validate willingness to pay and generation economics before expanding editor capabilities.

What this demonstrates

Zero-to-one product engineering and disciplined AI-MVP scope.

Next case · 08Two years of full-stack contract delivery