Abdul Basit Khan

Case study 06

Movium

AI-assisted motion graphics with validation, preview, and async rendering.

AI + Remotion

Motion graphics system

Client
Independent product
Duration
Active product
Role
Solo end-to-end product engineer

How it works

SOURCESContractsReportsPoliciesEmailsIngestionParse & CleanChunk TextGenerate EmbedsValidatepgvectorSemantic SearchUser QueryNatural LanguageLLM PipelineContext AssemblyPrompt EngineeringOutput ValidationZod Schema CheckStructured OutputRecommendationsExtracted DataSummariesActionsMLOps & GovernanceModel LifecycleMonitoringCompliance30%+ manual work eliminatedThrough targeted AI automation across operationsRAG ARCHITECTURE · ENTERPRISE AI STRATEGY

Situation

Motion graphics can be slow to produce when teams need repeatable data-driven videos, branded updates, or structured visual explanations.

Build

I built an AI-assisted motion platform that separates prompt generation, code validation, preview, rendering, private storage, and sharing into a controlled product flow.

Result

Movium is useful proof for AI product engineering, LLM-to-code workflows, queue-based systems, and generated-code validation around video rendering.

Key decisions

  • Separated interactive API traffic from LLM and render workloads with queues.
  • Validated generated code before it reaches preview or rendering paths.
  • Kept private media storage and share access behind controlled product boundaries.
  • Designed the product around repeatable data-backed motion, not generic prompt output.

Tools used

FastifytRPCReactPostgreSQLRedisBullMQRemotionLLM APIs

My role

ProductBackendWorkersEditorValidationRender pipeline

Exploring something similar?

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