Universal Data Ingestion Pipeline for GenAI & RAG

We built a universal data ingestion and knowledge pipeline that can take almost any type of file – PDF, Excel, XML, JSON, images, audio, video and more – and turn it into a structured, searchable and richly annotated knowledge layer. Instead of custom ETL for every project, this pipeline gives our GenAI agents and chatbots a single, consistent way to understand and use content.

The system preserves complex structures such as tables, sections, hierarchies and relationships between documents, and stores them in the right place: relational databases, vector databases or graph databases, depending on what the use case needs. On top of that, it generates embeddings, optimised indexes, summaries and metadata so RAG workflows can return accurate answers fast.

In practice, this pipeline is the foundation layer behind multiple Sparky* solutions – from domain chatbots and copilots to analytics dashboards and research tools.

What this solves

Organisations usually have information scattered across formats and systems: reports in PDF, models in Excel, logs and APIs in JSON or XML, images and scans, call recordings, training videos and more. Traditional ETL pipelines are rigid, format-specific and time-consuming to adapt, which makes every GenAI or RAG project slow and expensive to start. Even when data is ingested, important structure is often lost, making downstream answers shallow or unreliable.

Our data ingestion pipeline solves this by giving a single, format-agnostic entry point for all content. It automatically classifies file types, parses them with the right tools (including OCR and speech-to-text where needed), and keeps the logical structure intact – sections, tables, entities, relationships and temporal context. This dramatically reduces time-to-first-answer for new GenAI projects, improves recall and precision in RAG, and ensures that knowledge is reusable across multiple agents and products instead of locked into one-off integrations.

How we did it

We designed the pipeline as a modular, event-driven system composed of specialised components for detection, parsing, enrichment and storage. Ingestion connectors handle uploads, API feeds and data lake integration, routing each file to format-specific processors that perform parsing, OCR, transcription and normalisation. The resulting representation is enriched with embeddings, keywords, entities, summaries and custom domain metadata.

A flexible storage layer chooses the optimal backend per content type and use case: relational databases for transactional data and reference catalogs, vector databases for semantic search and RAG, and graph databases for highly connected knowledge such as entities, relationships and provenance. Indexing strategies and chunking rules are tuned for GenAI workloads, making it easy for agents and chatbots to retrieve the right context quickly. Monitoring, logging and replay capabilities allow us to trace every item from raw file to enriched knowledge object, which is critical for debugging, compliance and continuous improvement.