4. System Architecture

Echo’s architecture is purpose-built for modular scalability, multilingual intelligence, and real-time performance. It operates across four critical layers: Data Ingestion, RAG Retrieval Pipeline, Speech & Voice Generation, and the Integration Layer.

4.1 Data Ingestion & Structuring

The intelligence of Echo begins at ingestion, where raw project data is transformed into structured, query-ready knowledge.

• Accepted Formats → Whitepapers, FAQs, founder notes, community threads, chat logs.

• Preprocessing → Stopword filtering, phrase normalization, token-based chunking (512–1024), document splitting.

• Contextual Tagging → Each chunk is annotated with metadata (topic, section, version, relevance rank).

• Multilingual Embedding → Encoded with LaBSE / E5-Mistral to unify all content (English, Urdu, Hindi, Arabic, etc.) into a single cross-lingual vector index.

In Action: This means a query in Arabic can fetch the right context from English FAQs and respond instantly — without language barriers.

4.2 Retrieval-Augmented Generation (RAG) Core

The RAG engine is the control brain of Echo, ensuring responses are fact-locked and hallucination-free.

• Vector Indexing → FAISS / Weaviate for millisecond-scale similarity search.

• Top-K Retrieval → Semantic scoring (dense + hybrid) to fetch the most relevant knowledge chunks.

• Prompt Fusion → Injects retrieved passages directly into the model prompt before generation.

• Guardrails → Token-level filters ensure zero hallucination, restricting outputs strictly to project-approved data.

In Action: Tokenomics, roadmaps, disclaimers — Echo never improvises. It only speaks what the project’s dataset allows.

4.3 Speech Language Model (SLM) + Voice Generation

After context-rich text is produced, Echo optimizes and vocalizes it through a speech-first pipeline.

• SLM Optimization → Polishes responses for clarity, pauses, emphasis, and tonal balance.

• TTS Models → Powered by OpenVoice, Bark, or equivalent multilingual TTS engines.

• Custom Voice Profiles → Per-project voice settings (tone, pitch, accent, speed) for brand alignment.

• Multilingual Output → Responds in the same language as the query (or a different one, if set by the user.)

In Action: Ask in Hindi, get an answer in Hindi voice — or request it in English with an American or Arabic accent.

4.4 Integration & Real-Time Output

The final layer delivers sub-second voice output to user platforms through secure APIs.

• Platform Routing → Native support for Telegram Voice, X Spaces (bridge), Web, and upcoming SIP endpoints.

• API Interfaces → REST + WebSocket APIs for seamless app, dashboard, or bot integration.

• Session Context Buffering → Maintains query history for coherence across multi-turn conversations.

• Audio Engine → Streams optimized audio formats (Opus, AAC, etc.) per platform codec.

In Action: Echo becomes a live AMA co-host, community mod, or 24/7 support desk — engaging in context-aware, natural voice conversations.