Sparky*
Sparky*
Sparky*
Data platforms

Universal Data Ingestion Pipeline for GenAI & RAG

The challenge
Company knowledge sits in PDFs, spreadsheets, XML and JSON, scans, recordings, and videos. Classic ETL is rigid and format-specific, so every GenAI project starts slow, and structure such as tables gets lost on the way.

What we built
We built a universal ingestion pipeline that takes almost any file, from PDF and Excel to audio and video, and turns it into a structured, searchable knowledge layer.

It keeps tables, sections, and links between documents intact, and stores each piece where it works best: relational, vector, or graph databases. Embeddings, summaries, and metadata are generated on the way in, so RAG answers are accurate and fast. It is the foundation behind several Sparky* chatbots, copilots, and dashboards.

Business value

  • GenAI and RAG projects start faster and cost less
  • One entry point for every file type
  • More accurate answers because structure is kept
  • Built-in monitoring of data quality

Where else it applies
The same approach transfers to publishing archives, legal and compliance libraries, research institutes, and corporate knowledge bases. It belongs to our Data Platforms and Data Lakes work: one trusted place for data that is scattered across systems, sensors, and files, made ready for reporting and AI.

Task

Turn any type of file into knowledge that GenAI can use.

  • Strategy

    One format-agnostic ingestion layer, a schema-agnostic knowledge model, and storage in SQL, vector, and graph databases.

  • Design

    Modular processors with OCR and speech-to-text, enrichment for embeddings and summaries, and pluggable storage.

  • Client

    Media company from Germany (EU)

  • Tags

    data ingestion, data lake, data processing, graph db, Media, RAG, vector db

Open Project

Let’s build together

Got a deep-tech challenge?

Tell us the problem. We’ll show you the closest thing we have already built and how we would adapt it to your data.

Tell us what you’re working on
Back
Next Project
Real-time Multilingual Speech-to-Text Platform