Engineering Digital Products
AI-powered extraction pipeline that turns raw PDFs, spreadsheets and CSVs into clean, structured question banks, no manual data entry required.
Building quiz content from existing documents (PDF, XLSX, CSV question sets) meant hours of manual copy-paste and reformatting before a single question could be used.
We built a Next.js application using Gemini for extraction, with dynamic batching and automatic truncation recovery for large documents, streaming progress to the user via Server-Sent Events so long extractions never feel like a black box. Output is stored in Firebase Realtime Database, with the whole pipeline deployed on GCP Cloud Run for scalable processing.
Multi-Format Extraction (PDF / XLSX / CSV)
Gemini-Powered Question Parsing
Dynamic Batching
Truncation Recovery
Real-Time Progress (SSE Streaming)
Firebase RTDB Storage
GCP Cloud Run Deployment
"Converted a manual, document-by-document data entry task into an automated extraction pipeline, removing the single biggest bottleneck in building structured question content at scale."
Designer eyewear store with browser-based AR try-on, letting shoppers see frames on their own face before buying online.
Fully automated pipeline that turns structured question data into branded MCQ videos and publishes them to YouTube on a schedule, zero manual editing.
Whether you need a robust ERP, a customer-facing app, or a scalable SaaS platform, we have the engineering expertise to build it.
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