Our Power
What Powers the Eave Engine: The Stack & Secrets
Okay, so now you know what the Eave Engine does. But how does it actually pull this off — day in, day out, at scale? Here’s a look under the hood:
Our Core Tech Stack (a.k.a. The Brains of Eave)
1. FastAPI — Our backend is built on FastAPI, one of the fastest and most flexible Python frameworks out there.
Every function, every endpoint — built like modular apps for max scalability.
MongoDB as the database for fast, document-based storage — so we can scale as wide as we want without breaking speed.
Redis for caching — when you’re moving at scale, milliseconds matter.
2. Kubernetes on DigitalOcean — All pipelines and AI agents run on Kubernetes clusters, giving us full control over scaling and fault tolerance.
Why Kubernetes? Because when you’re processing thousands of conversations, you need to scale like a beast — and we do.
Yes, we even keep some agents running on Azure RDP when Windows is absolutely necessary (looking at you, Space Live Agent).
The AI & NLP Muscle: Why Our Data is Cleaner & Smarter
Autocorrection & Entity Recognition:
Custom LLM-powered pipeline trained on crypto-specific language — no more random "Elon" meaning your buddy from Discord.
We handle speaker normalization, crypto jargon standardization, and project name disambiguation (yes, there are 5 projects called "Moon" — we know which one you mean).
Semantic Chunking & Splitting:
Powered by OpenAI's
text-embedding-3-small— the gold standard for finding meaning in messy human conversations.Dynamic thresholding — so the splits actually make sense and follow conversation flow.
Sentiment & Signal Detection:
Proprietary CryptoSentimentAnalyzer, designed specifically to read between the lines in crypto spaces — catching not just what people say, but how they say it (bullish, bearish, warning).
GraphRAG Indexing:
Think Google for Web3 conversations — using graph-based relational search, built in-house.
LightRAG + Pinecone hybrid search — for lightning-fast entity matching and retrieval.
External Data Hooks: Keeping Us Always On-Point
Pinecone Vector Store — handles hybrid (BM25 + embedding) search for precise entity linking.
AWS Transcribe + Whisper (fallback) — when we pull raw audio and need rock-solid transcriptions.
Deepgram API (in beta) for specialized audio-to-text work when Whisper doesn't cut it.
What This Means (The Why)
Faster — No manual crawling, no broken scrapers. Fully automated, scalable infrastructure that handles thousands of conversations daily.
Smarter — By combining AI + graph reasoning + embeddings, we go beyond keywords to understand the real story.
Cleaner Data — Most providers give you raw mess — we give you refined, research-ready data that works out of the box.
Battle-Tested for Crypto — Every model we use is fine-tuned for crypto markets, not general news. We know what a "rug pull" is, and we treat "FOMO" like the market signal it is.
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