{ The memory layer for AI }
Your AIshould remember.
Give every assistant and agent a shared, persistent understanding of your users — without locking their data inside a single model. One open layer, every intelligent surface.
- Communication preferencepreferencePrefers concise async updates
- Project contextprojectNorthstar launch moved to Friday
- RelationshiprelationshipMaya leads the design review
Works with the tools already in your stack
{ How it works }
Capture once.
Recall everywhere.
A single pipeline turns raw interactions into durable, structured context your models can trust.
Capture
Ingest conversations, documents, and events. MatiAI extracts clean facts, preferences, and relationships — and resolves what changed.
Resolve
Update beliefs as new information arrives. Preserve provenance, merge contradictions, and keep a versioned memory graph per user.
Recall
Retrieve the right context at the right time with hybrid semantic, keyword, temporal, and graph signals — injected into any model.
{ MatiAI Tools }
One memory layer.
Every intelligent tool.
Capture
Turn conversations into durable context.
Extract useful facts from messages, documents, and events. Deduplicate, update, and connect memories without brittle pipelines — MatiAI keeps meaning, source, confidence, and history behind every fact.
Explore CaptureRecall
Retrieve exactly what your model needs.
Semantic, keyword, temporal, and graph-aware search — so every response gets the right context at the right time, ranked by relevance and freshness in under 100ms.
Explore RecallIntegrations
Memory for the tools you already use.
Connect models, frameworks, data sources, and productivity tools. MatiAI sits between them and keeps context flowing across every surface your product touches.
Explore IntegrationsDevelopers
Ship memory in five lines, not five months.
Clean API, typed SDKs, webhooks, namespaces, and first-class MCP support. Start free, scale to production, and deploy on our cloud, your VPC, or self-hosted.
Explore API{ Built for real products }
One layer. Every kind of intelligent experience.
Use MatiAI anywhere continuity, personalization, or long-running context matters.
AI assistants
Carry preferences and context across every conversation — so the assistant remembers who the user is and what they decided last time.
Coding agents
Remember architecture, decisions, and developer conventions across repositories, sessions, and tools.
Support agents
Give every resolution the full customer history — account context, prior tickets, and resolution notes.
{ Memory vs everything else }
Not storage.
Not RAG. Memory.
Storage keeps chunks. RAG retrieves documents. Memory evolves with the user.
Storage / Vector DB
- Stores chunks, not meaning
- No contradiction handling
- Retrieval, not continuity
- Manual pipelines
RAG
- Document retrieval only
- No evolving beliefs
- Stateless per query
- No user-owned controls
MatiAI
- Structured, evolving memory
- Conflict resolution + provenance
- Per-user, portable, deletable
- One layer for every model
{ Built for developers }
Memory in five lines,
not five months.
Ship persistent context with a clean API, typed SDKs, and first-class MCP support.
- TypeScript and Python SDKs
- Framework-agnostic REST API
- Realtime webhooks and namespaces
- Cloud, VPC, or self-hosted deployment
import { MatiAI } from "@matiai/sdk";const memory = new MatiAI({apiKey: process.env.MATIAI_KEY});await memory.remember({userId: "user_42", content: "I prefer concise answers."});const context = await memory.recall({userId: "user_42", query: "How should I reply?"});{ What teams say }
Trusted by teams shipping memory.
From early-stage agents to production support — MatiAI is the context layer behind their products.
We replaced a tangle of embedding pipelines with five lines. Recall latency dropped and our agents finally stop asking the same questions.
MatiAI gave our assistant real continuity. Users stopped repeating themselves, and CSAT climbed noticeably in a month.
The memory graph explorer changed how we debug context. We can see exactly what the model remembers and why.
{ FAQ }
Questions, answered.
Is MatiAI a vector database?
No. MatiAI is a memory layer — it stores structured, evolving context (facts, preferences, relationships) with provenance and conflict resolution, not raw embedding chunks. It can sit on top of your existing store.
How is this different from RAG?
RAG retrieves documents for a single query. MatiAI maintains a living model of each user that updates over time, so the right context is already available — continuity, not just retrieval.
Can I self-host?
Yes. Run MatiAI on our cloud, in your VPC, or fully self-hosted. Memory is portable — export to JSON anytime with no lock-in.
Which models does it support?
Any. MatiAI is model-agnostic and works with OpenAI, Anthropic, Gemini, Mistral, open-source models via Ollama, and any MCP-compatible client.
How is user data controlled?
Users can view, correct, export, and delete their memories. You set retention, decay, and deletion policies by user, namespace, or memory type.
Is there a free plan?
Yes — 10K stored memories and 25K recalls per month, free forever, with the full API and SDKs. No credit card to start.
Does it train on my data?
Never. Your memories are encrypted, tenant-isolated, and never used to improve a shared model.
How fast is recall?
Median recall is under 100ms with hybrid semantic, keyword, temporal, and graph ranking, so memory fits inside a normal model request.
{ Start remembering }
Give your AI a memory
it can trust.
Free to start. Open by design. Ready for production.