“Traditional document review is broken. We built a platform that combines AI intelligence with human insight to transform how teams analyze documents.”
Design Process
Discover & Define → Frame the Challenge → Design & Deliver
1. Discover & Define
The ask: How might we accelerate document analysis while protecting sensitive information and enabling team collaboration?
Key insights from research:
- Organizations drowning in document volumes with manual review bottlenecks
- Reviewers spend 80% of time reading, only 20% analyzing
- Privacy concerns prevent adoption of cloud-based AI solutions
- Teams struggle to share findings across distributed workflows
We mapped the entire document review lifecycle, identifying pain points and opportunities for AI assistance.
2. Frame the Challenge
- Volume overload makes comprehensive review impossible
- Inconsistent analysis across different reviewers
- Privacy requirements block most AI solutions
- Collaboration barriers prevent knowledge sharing
- No unified workflow from upload to insights
We needed to build a platform that could process documents at scale while maintaining privacy and enabling seamless collaboration.
3. Design & Deliver
AI Analysis Engine
- Integrated multiple LLM backends (OpenAI, Gemini, Ollama)
- Built custom prompts for strength/weakness identification
- Implemented text chunking for efficient processing
- Created structured output parsing for consistent results
Privacy-First Architecture
- Local redaction pipeline removes sensitive data before analysis
- Supports on-premise deployment with Ollama
- Hierarchical permissions for document access
- Audit trail for compliance requirements
Collaborative Workspace
- Real-time progress tracking via WebSockets
- File tree visualization for intuitive navigation
- AI findings + human insights in unified view
- Pin and prioritize important discoveries
Launch & Iterate
- Deployed with pilot team processing 500+ documents
- Iterated on redaction accuracy based on user feedback
- Enhanced AI prompts for industry-specific analysis
- Added batch processing for large document sets
- Integrated with existing document management systems
Outcomes
70% reduction in document review time
3x more insights identified per document
100% privacy compliance maintained
500+ documents processed in pilot phase
0 data breaches or privacy incidents
Lessons Learned
- Privacy enables adoption — on-premise options crucial for sensitive data
- AI augments, doesn’t replace — human insight remains essential
- Workflow integration matters — CLI and API access drove power user adoption
- Real-time feedback changes behavior — WebSocket updates kept users engaged
- Structured prompts ensure quality — careful prompt engineering delivers consistent results
Want to transform your document workflows?
Let’s build AI-powered solutions that respect privacy while delivering insights.
Let’s talk
