How Beever Atlas Works

Beever Atlas transforms team conversations into an intelligent knowledge base through a multi-stage pipeline and dual-memory architecture.

The LLM Wiki Pattern

Traditional wikis require manual curation. Search tools only retrieve existing content. Beever Atlas combines both approaches:

1. Continuous ingestion of team messages
2. LLM-powered extraction of facts and entities
3. Automatic organization into topic clusters
4. Persistent wiki pages that stay up-to-date
5. Natural language queries with cited sources

The result: A living knowledge base that grows with your team.

Architecture Overview

Ingestion Pipeline

Atlas uses a 6-stage pipeline built on Google ADK's SequentialAgent framework:

Stage 1: Preprocessor

Normalizes messages from different platforms:

• Convert Slack mrkdwn to Markdown
• Assemble threaded conversations
• Extract media attachments (images, PDFs via Gemini Vision)
• Filter bot messages and system notifications

Stage 2: Fact Extractor

Uses Gemini 2.5 Flash to extract atomic facts:

• Quality gate: Score ≥ 0.5
• Maximum 2 facts per message
• Preserves temporal context and attribution

Example fact: "On 2024-03-15, Alice decided to use RS256 for JWT signatures due to better security properties."

Stage 3: Entity Extractor

Identifies entities and relationships:

• Entities: People, decisions, projects, technologies
• Quality gate: Confidence ≥ 0.6
• Alias deduplication (e.g., "Alice" and "Alice Smith")
• Temporal validity tracking

Stage 4: Embedder

Generates embeddings using Jina v4:

• 2048-dimensional vectors
• Named vectors for different content types
• Multimodal support (text, images)

Stage 5: Cross-Batch Validator

Ensures consistency across batches:

• Resolve entity aliases globally
• Validate relationship consistency
• Detect and merge duplicate entities

Stage 6: Persister

Writes to all three stores atomically:

• Weaviate: Facts with embeddings
• Neo4j: Entities and relationships
• MongoDB: State and wiki cache

Uses the outbox pattern for cross-store consistency.

Dual-Memory Architecture

Semantic Memory (Weaviate)

Stores atomic facts in a 3-tier hierarchy:

1. Summaries: Channel-level overviews
2. Topics: Clustered groups of related facts
3. Facts: Individual extracted knowledge

Query method: Hybrid BM25 + vector search with Jina embeddings

Answers questions like:

• "What was discussed about JWT authentication?"
• "Show me the database migration topic"
• "What are the recent decisions about the API?"

Performance: < 200ms latency

Handles ~80% of queries.

Graph Memory (Neo4j)

Stores entities and relationships:

• Entity types: Person, Decision, Project, Technology
• Relationships: DECIDED_BY, WORKS_ON, USES, MENTIONED_IN
• Temporal evolution: Track changes over time

Query method: Cypher graph traversals

Answers questions like:

• "Who decided on RS256 for JWT signing?"
• "What projects is Alice working on?"
• "What technologies are used in Project X?"

Performance: 200ms – 1s latency

Handles ~20% of queries (relationship-heavy queries).

Bidirectional Linking

Every fact in Weaviate stores graph_entity_ids, and every entity in Neo4j has a MENTIONED_IN edge to its source facts. This enables hybrid queries that traverse both stores.

Query Router

When you ask a question, Atlas's LLM-powered router analyzes the query type:

1. Semantic queries → Weaviate (fast vector search)
2. Relationship queries → Neo4j (graph traversal)
3. Hybrid queries → Both stores, merged results

The router optimizes for:

• Cost: Minimize LLM calls
• Latency: Use faster semantic route when possible
• Accuracy: Graph route for complex relationships

Wiki Generation

After ingestion, Atlas builds a structured wiki:

1. Consolidation: Facts are clustered using cosine similarity (no LLM cost)
2. Topic Summarization: LLM generates summaries for each cluster
3. Wiki Compilation: Queries both stores to generate 10+ page types
4. Caching: Full wiki stored in MongoDB for instant retrieval

A wiki_dirty flag triggers regeneration when new data arrives.

Wiki Page Types

• Overview: Channel summary and key topics
• Topics: Hierarchical topic pages with sub-topics
• People: Team members and their contributions
• Decisions: Track decisions with context and outcomes
• Tech Stack: Technologies and tools used
• Projects: Active projects and status
• Recent Activity: Timeline of recent discussions
• FAQ: Frequently asked questions and answers
• Glossary: Domain-specific terms and definitions
• Resources: Links and references

Streaming Q&A

When you ask a question via the web UI or API, Atlas streams the response:

1. Thinking: Agent reasoning trace
2. Tool calls: Each store query as it happens
3. Response: Answer tokens streamed in real-time
4. Citations: Source messages with permalinks
5. Metadata: Route used, cost, confidence score

What's Next?

• Quick Start — Try Atlas in 5 minutes with mock mode
• Installation — Set up your own instance
• Platform Setup — Connect your real team data