Case Study: Building Doka AI's Document Intelligence Platform

Doka started with a clear vision: make it dead simple for teams to get answers from their own documents. No more digging through folders, skimming 80-page PDFs, or rewatching hour-long meeting recordings. Upload it, ask a question, get a cited answer. That was the product thesis — and our job was to make it real.

The core technical challenge was building a RAG (Retrieval-Augmented Generation) pipeline that could handle wildly different content types. PDFs with complex layouts, slide decks with embedded charts, spreadsheets with structured data, and audio/video files that needed transcription before they could be indexed. Each format required its own ingestion pipeline, but they all needed to feed into a unified search and conversation layer.

We built the document processing pipeline as a series of specialized workers. PDF ingestion uses layout-aware parsing to preserve table structures and reading order. Audio and video files are transcribed with timestamps, so the system can cite specific moments in a recording. Every chunk is embedded and stored in a vector database, with metadata linking back to the exact page, slide, or timestamp.

The multi-tenant architecture was critical from day one. Teams need private workspaces with granular permissions — some documents are shared across the org, others are restricted to specific groups. We implemented tenant isolation at the data layer while keeping the application tier shared for cost efficiency. Each workspace gets its own embedding namespace, so cross-tenant data leakage is architecturally impossible.

The conversation interface supports multi-chat — users can have multiple simultaneous conversations against the same document or across an entire workspace. Every AI response includes citations with page numbers, timestamps, and highlighted excerpts. One click takes you to the exact source. This was non-negotiable; in professional contexts, trust requires traceability.

Doka launched with support for legal teams, finance departments, research groups, and product teams. The platform now processes thousands of documents and serves teams who rely on it daily for contract analysis, earnings report review, research synthesis, and onboarding new hires with institutional knowledge.