Case Study: How Instabase turns unstructured data into insights with Weaviate
Key results
The challenge
Instabase's enterprise AI application platform processes over 500K highly varied documents per day and must scale with growing customer demand. Its engineering team needed a database that could index, store, and retrieve massive data volumes with very high accuracy and low latency, and deploy anywhere — including on-premises — to meet regulated customers' data-residency requirements.
The solution
Instabase chose Weaviate after benchmarking retrieval accuracy and latency against alternatives for complex aggregation and composition queries. Weaviate's open-source, AI-native architecture supported both cloud and on-prem deployment, and out-of-the-box hybrid search and distance metrics plus LLM integrations let developers avoid building capabilities from scratch.
“Accuracy determines the amount of savings any large institution can get. If the results aren't accurate or take too long to surface, a human needs to get involved, and the cost savings are greatly reduced. So accuracy and speed are critical for us.”
SGShaunak GodboleHead of Infrastructure Engineering, Instabase
The results, in context
Instabase stores 50K+ tenants in its Weaviate cluster and queries tenant-specific data within milliseconds, and it supports the ingestion and indexing of 450+ data types for a single customer solution. It sees consistent results across documents ranging from single-page handwritten notes to 400-page financial filings.