Overview
Nova set out to build an AI-powered analytics tool that could work with any LLM provider. The problem: stitching together 5 different vendor SDKs, a vector database, streaming infrastructure, and user authentication. Each vendor had its own API patterns, auth models, and failure modes. EEv3 unified everything behind a single interface.
Quick stats
- Industry
- AI / SaaS
- Time to 10k users
- 6 weeks
- LLM providers
- 3 (unified)
- EEv3 faces used
- 5
The Challenge
Five SDKs, five problems
Building an AI-powered analytics tool required stitching together 5 different LLM providers, a vector database, streaming infrastructure, and user auth. Each vendor had its own SDK, its own authentication model, its own rate limits, and its own way of failing. The integration layer was becoming more complex than the product itself.
OpenAI SDKAnthropic SDKGoogle AI SDKPinecone SDKAuth0 SDKThe Solution
One face to rule them all
EEv3's ee.ai face unified all LLM providers behind one interface. Switch between GPT-4, Claude, and Gemini with a single parameter change — no SDK swaps, no auth rewiring, no breaking changes. Vector search, streaming, and auth came free with the platform.
Nova's entire AI pipeline went from hundreds of lines of glue code to 10 lines of typed, tested, production-ready calls through the conductor.
The Stack
EEv3 faces used
ee.aiUnified LLM interface — GPT-4, Claude, Gemini in one call
ee.vectorVector search, embedding storage, semantic retrieval
ee.streamingReal-time streaming responses, SSE, WebSocket
ee.authUser auth, API keys, rate limiting, RBAC
ee.analyticsToken usage tracking, model performance, cost analysis
Results
Six weeks later
"We replaced 5 vendor SDKs with one ee.ai call. Our AI pipeline is 10 lines of code."
— Daniel Osei, Founder, Nova