Architecture Overview

Beever Atlas is built on a dual-memory architecture that combines semantic search with knowledge graphs to transform your team conversations into a persistent, queryable knowledge base. Unlike traditional chat search tools that only find messages, Beever Atlas understands relationships, tracks decisions, and auto-generates wiki pages.

Why This Matters

Your team's most valuable knowledge is trapped in scattered conversations across Slack, Discord, and Teams. Traditional search tools can find messages, but they can't answer questions like:

• "Who decided to use JWT and why?"
• "What blocks the authentication migration?"
• "How did our deployment strategy evolve over the past year?"
• "What projects is Alice working on?"

Beever Atlas solves this by ingesting your conversations, extracting facts and relationships, and organizing them into two complementary memory systems that work together to provide context-rich answers.

System Architecture

Technology Stack

Key Architecture Principles

Dual-Memory Design

Beever Atlas uses two complementary memory systems:

• Semantic Memory (Weaviate): Handles ~80% of queries — factual lookups, topical questions, and content retrieval. Uses a 3-tier hierarchy (Summary → Topic Clusters → Atomic Facts) for cost-optimized retrieval.

• Graph Memory (Neo4j): Handles ~20% of queries — relational questions, temporal evolution, and multi-hop traversals. Captures entities (Person, Decision, Project, Technology) and their relationships.

These systems are not redundant — they each solve problems the other cannot. Weaviate can't traverse "Person → works on → Project → has decision → blocked by → Constraint." Neo4j can't do fuzzy semantic search across thousands of facts.

Agent-Based Processing

The ingestion and query pipelines are orchestrated by Google ADK agents — specialized AI agents that handle specific stages:

• Ingestion Pipeline: 6-stage pipeline (Sync → Extract → Validate → Store → Cluster → Wiki) with parallel execution where possible
• Query Router: Decomposes complex questions, routes to appropriate memory systems, and merges results
• Quality Gates: Confidence-scored filtering prevents low-quality data from entering your knowledge base

Cost-Optimized Retrieval

Beever Atlas is designed for cost efficiency at scale:

• Tier 0 + Tier 1 reads = FREE: Pre-generated channel summaries and topic clusters require no LLM calls
• Tier 2 search = CHEAP: ~$0.001 per query (embeddings only)
• LLM synthesis = PAID: ~$0.02 per query (only when needed)
• Average query cost: ~$0.01 (5x cheaper than competitors)

Graceful Degradation

The system is built to handle component failures:

• Circuit breakers for each external dependency (Weaviate, Neo4j, Gemini, Jina, Tavily)
• Fallback models: If Gemini is unavailable, routes to Claude via LiteLLM
• Partial degradation: If Neo4j is down, semantic search still works; if embeddings fail, BM25-only search continues
• Outbox pattern: Cross-store writes are retryable and reconciled automatically

Data Flow

Ingestion Flow

1. Sync: Fetch messages from Slack/Discord/Teams via platform adapters
2. Extract: LLM extracts facts and entities in parallel
3. Validate: Quality gates filter low-confidence extractions
4. Store: Write to Weaviate (facts) and Neo4j (entities) via outbox pattern
5. Cluster: Group related facts into topic clusters
6. Wiki: Generate structured wiki pages from clusters

Query Flow

1. Decompose: Complex questions split into focused sub-queries
2. Understand: LLM classifies query type (semantic/graph/both)
3. Route: Execute searches in appropriate memory systems
4. Merge: Combine results, apply temporal decay, deduplicate
5. Respond: Generate grounded answer with citations

What Makes Beever Atlas Different

Next Steps

• Learn about the LLM Wiki Pattern that powers persistent knowledge
• Understand the Dual Memory Architecture in detail
• See how the Ingestion Pipeline transforms messages into knowledge
• Explore how the Query Router decides the best way to answer questions