Memory System
Turn scattered data intousable memory.
Constella ingests context from your integrations, extracts the useful parts from files and conversations, and builds a unified memory layer that brings back the right context on demand.
01
Ingest
Pulls in data from connected integrations and synced sources continuously.
02
Understand
Converts messy inputs into clean, usable text. Extracts and chunks documents.
03
Recall
Assembles the most relevant context from multiple layers into one answer.
Ingestion
Ingests context from your connected tools.
Slack, Notion, Google Drive, Obsidian, Gmail, and more — your connected integrations continuously sync into one memory layer. No manual uploads. No copy-paste. The system pulls in what matters from the tools you already use.
Understanding
Extracts the useful parts from files, notes, and conversations.
Documents aren't just stored — their usable text is extracted and made recallable. For long documents, the system preserves the full source while breaking content into smaller searchable passages. Raw becomes retrievable.
Structure
Organizes information into a memory layer the AI can actually use.
Raw inputs become organized passages, metadata, categories, and connections. The system builds multiple views of the same information — source content, semantic recall, and relationship context — so retrieval is coherent, not fragmented.
Recall
Assembles the right context before answering.
Intent-aware query analysis determines what entities, categories, and search paths matter — before anything is retrieved. Responses stay tied to original records instead of free-floating summaries. Source-grounded recall, not hallucination.
Under the hood
Built on real infrastructure.
Graph-Backed Retrieval
Retrieves both relevant content and the relationships around it — people, projects, topics, events.
Hybrid Search
Combines semantic search, keyword recall, and structured memory in a single query.
Query Analysis
Analyzes intent to determine entities, categories, and search paths before retrieving.
Retrieval-Grade Embeddings
A dedicated GPU-backed embedding pipeline tuned for recall quality.
Long-Context Indexing
Large files become smaller recallable sections while preserving the parent document.
Auditable Assembly
Tracks what context was retrieved and used. Source-grounded, not free-floating.
Build with memory that works.
Relationship-aware, source-grounded recall built from integrations, files, and connected memory.
