Weaviate gives us fast, semantic search across unstructured call data, crucial for surfacing insights in real time. Its native vector support and scalability made it the best fit for building an AI native platform like Insight7.
We chose MongoDB for data storage because of its flexibility in handling unstructured and semi-structured data at scale. MongoDB’s document based model allows us to store complex metadata and insights without rigid schema constraints, making it ideal for fast iteration and evaluation at scale.
What's great
schema flexibility (5)high scalability (7)flexible document model (5)
We selected OpenAI to power our LLM APIs because of its performance, stability, and continual improvements in conversational and contextual understanding. OpenAI models provide the nuanced comprehension we need to evaluate calls accurately, identifying patterns, surfacing product gaps, and scoring qualitative interactions reliably. Its developer friendly APIs also allowed us to move quickly from prototype to production without compromising on capability.
