AI document assistant
Multi-format document workspace with semantic search, transcription-aware Q&A, and source-grounded answers across PDFs, Office files, tables, and media.
Results at a glance
PDF, Word, Excel, CSV, JSON, and audio/video ingestion
Semantic retrieval with citation-style sourcing
Interactive exploration without brittle keyword-only search
Challenge
Teams needed a single place to upload heterogeneous documents and media, search by meaning rather than filenames alone, and ask questions that respect the underlying sources instead of producing unattributed guesses.
What Habrig built
- Streamlit-based operator UI for uploads, corpus browsing, and chat-style Q&A scoped to selected documents
- Clear separation between retrieved excerpts and model answers to keep outputs auditable
- Embedding and indexing pipeline suitable for mixed modalities with chunking tuned per file type
- Retrieval layer combining dense semantic search with filters for document sets and formats
- Optional transcription path for audio/video aligned to the same retrieval stack
- GPU-backed inference where latency and throughput warrant it, with fallbacks for lighter deployments
- Repeatable environment definitions so demos can move from laptop to a small server without rework
Outcomes
- Faster answers across large document sets without manual tagging every file
- Reduced “trust but verify” loops thanks to source-grounded responses
- A reusable pattern for internal knowledge bases and customer-facing doc portals
Technology
frontend
Streamlit UI focused on upload, search, and grounded Q&A flows
backend
Python services for ingestion, embeddings, retrieval, and orchestration (including T5-class tooling where appropriate)
database
Vector-oriented retrieval with metadata for filenames, types, and source spans
infrastructure
Deployable on modest GPU or CPU hosts depending on corpus size and SLA
monitoring
Structured logs around ingestion jobs, query latency, and retrieval misses
cicd
Scripted checks and pinned dependencies for reproducible analyst-facing builds
Execution detail
Product & frontend
- Streamlit-based operator UI for uploads, corpus browsing, and chat-style Q&A scoped to selected documents
- Clear separation between retrieved excerpts and model answers to keep outputs auditable
Backend & data
- Embedding and indexing pipeline suitable for mixed modalities with chunking tuned per file type
- Retrieval layer combining dense semantic search with filters for document sets and formats
- Optional transcription path for audio/video aligned to the same retrieval stack
Platform & delivery
- GPU-backed inference where latency and throughput warrant it, with fallbacks for lighter deployments
- Repeatable environment definitions so demos can move from laptop to a small server without rework
Plan your next release
Tell us what shipped, what is at risk, and what success looks like. We will respond with a practical path.
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