Enterprise search that understands context
A unified cross-product search that interprets relationships—not just keywords—to deliver human-level understanding of your workspace content.
Search that actually understands context, not just keywords.
The problem
Most enterprise search systems stop at matching tokens. When employees look for "recent vendor contracts" or "battery discharge issue in Q4", typical search engines drown them in loosely related documents.
We built Ziqara Enterprise Search to answer the question you meant to ask, not just the one you typed.
How it works
Our context engine fuses three layers:
-
Semantic memory
- Embeddings trained on real enterprise datasets (technical docs, support threads, legal text).
- Detects intent even with ambiguous phrasing ("vendor agreements 2024" → finds current NDAs).
-
Organizational awareness
- Each workspace’s search index respects access levels and project boundaries.
- Results are automatically ranked higher when shared collaborators or common files exist.
-
Cross-product retrieval
- One search hits your entire Ziqara Cloud: docs, chats, and notes - merged into a single relevance stream.
- Duplicates collapse intelligently to show you “the latest truth”.
Results
Teams using the private beta saw:
- 2.7× faster time-to-answer for internal queries.
- 40% fewer redundant Slack questions about old projects.
- Better onboarding - new hires find answers without asking.
Designed for trust
- Zero data used for external model training.
- All results are served through encrypted workspace contexts.
- Admins get transparency logs for every query event.
What’s next
We’re expanding context-awareness to images, PDFs, and spreadsheets. Enterprise search should feel like asking your most knowledgeable coworker - not like fighting your file system.