Projects/Automated Content Processing Pipeline
GitHub
Pipeline · AutomationLiveData Engineer

Automated Content Processing Pipeline

End-to-end document transformation pipeline — structured ingestion, parallel processing, and automated output generation.

PythonFastAPIDockerGitHub Actionsasyncio

~40%

Fewer inconsistencies

Parallel

Processing architecture

Docker

Containerised deploy

01

Problem

Unstructured document content (PPT/PDF) needed to be transformed into structured, consistent formats at scale. Manual processing was slow, inconsistent, and didn't scale beyond a small number of documents.

02

Approach

Designed a stateless FastAPI backend with async parallel processing to handle multiple documents simultaneously. Implemented layout-aware chunking to preserve semantic structure across long documents, with CI/CD via GitHub Actions.

03

Outcome

Reduced data inconsistencies in automated outputs by ~40%. Pipeline handles concurrent document processing with consistent throughput, fully containerised with Docker for reproducible deployments.

Data Pipeline

Doc IngestParse & ChunkTransformValidateStructured Output

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