The context cloud
for AI agents.
PayQIA gives your agents state-of-the-art memory, RAG, user profiles, connectors, and extractors, all built in. Extremely low latency. Encryption roadmap in progress.
Works with the frameworks you already use
The Memory OS Paradigm
Discover why PayQIA is not just another app, but the central logic layer for all your accounts.
AI forgets everything. Every session starts from zero.
Every chatbot, agent, and AI tool loses context the moment a session ends. Developers rebuild memory logic from scratch, again and again, with no shared standard.
One memory layer. Every AI, connected.
PayQIA is the central memory and context layer that plugs into any LLM or agent — storing, indexing, and retrieving context automatically, so intelligence persists across sessions, tools, and time.
QIA doesn't just store data. It understands.
Rather than simply dumping embeddings into a vector DB, QIA handles real-time semantic search, context compression, and automatic decay, streaming only the most relevant intelligence to the calling LLM.
"Recalled user's preferred coding style from 3 sessions ago in 340ms (projected)."
"Compressed 40K tokens of history into a 2K-token relevant summary — 99% context reduction (projected)."
The Product Catalog
Seven fundamental building blocks engineered to orchestrate persistent, structured context for agentic systems.
Memory & Continual Learning
Persistent, structured memory built as a knowledge graph, powered by QIA's context engine.
Facts update dynamically as new context is introduced.
Knowledge graphs link related entities automatically.
Smart decay models prune stale or irrelevant information over time.
Meet QIA. The Core Memory Engine.
Ask anything about your user's context. QIA monitors embedding states, links semantic graphs, and acts on your command.
Select Scenario Demo:
QIA Active Session
Active Node Connected
AI Competencies
QIA trains on multiple specialized indexing engines to handle complete semantic retrieval natively.
How It Works
Follow our 5-step flow to wire up persistent memory and structured retrieval in your agentic stack.
Three lines. That's all it takes to give your agent a memory.
Initialize, ingest interactions, and query user-profile graph memory in less than 30 seconds.
Plug In
Install the SDK and initialize the PayQIA client. Works in every runtime. Agents can even provision their own API key from the CLI — no dashboard or email required (roadmap target).
Ingest
Bring in any type of data. PayQIA extracts and structures it automatically for retrieval.
Understand
Entity resolution automatically structures your ingested data into a unified, linked node cluster graph.
Retrieve
Memory, retrieval, and user profiles live in one queryable graph — not three separate systems stitched together.
Real-Time Traversal
Sub-300ms graph traversal, every request. Your agent reaches into memory at request time, not training time.
Two Ways to Remember
PayQIA does not force a single memory philosophy. Choose the memory mode that fits your specific agentic use case.
QIA Direct
"You decide what's remembered."
A precise, explicit memory API. Store, update, and retrieve exactly what you choose — a write-and-query log built for developers who want full control over what their agent remembers and when.
- Explicit add(), search(), and update() SDK calls — no automatic ingestion
- Full control over what gets stored and surfaced inside the context window
- Ideal for chat personalization, user preference tracking, and long-term state
- Predictable, lightweight, and low-latency for simple, targeted use cases
QIA Graph
"PayQIA builds the memory for you."
A full context engine. Feed it anything — URLs, PDFs, files, conversations — and QIA Graph automatically extracts, links, and injects the right context, without manual API calls for every fact.
- Automatic ingestion across content types (documents, links, media, chat history)
- Knowledge-graph architecture — memories connect, merge, and evolve autonomously
- Ideal for knowledge bases, second-brain apps, and context-heavy agents
- Context surfaces automatically at query time — no manual retrieval logic needed
"Use QIA Direct when you want control. Use QIA Graph when you want it handled. Most teams end up using both."
Benchmarks
We don't think benchmarks tell the full story — but we're building toward leading them anyway.
End-to-End Context Sync
Query latency mapping semantic retrieval, reranking, and profile fetching (Unverified, pre-launch).
| Capability | Legacy RAG | PayQIA |
|---|---|---|
| Memory Graph & Entity Resolution | ||
| Persistent User Profiles | Partial | |
| Document Retrieval & Extractors | ||
| Automatic Connectors Sync | ||
| Sub-300ms End-to-End Latency | Partial | |
| Self-Hostable Deployment Options |
Use Cases
Real-world capabilities built on top of PayQIA's persistent semantic memory infrastructure.
Customer Support Agents
Remember every customer's history, preferences, and past issues — so support conversation threads never start from zero.
Coding Agents
Auto-capture developer coding preferences, patterns, and project context as they work, feeding the right historical memory back automatically.
Knowledge Bases
Self-improving context structures that are always synced. Automatically index Notion workspaces, Google Drive, and GitHub repos.
Autonomous Agent Swarms
Sync state, execution history, and sub-agent memory paths across distributed networks with sub-30ms retrieval (projected).
Deployment Options
Run PayQIA's memory infrastructure anywhere you want — from local machines to enterprise clouds.
On Premises
Self-host on your own infrastructure. Direct control over physical servers, storage, and networking layers.
Your Cloud
Deploy inside your own AWS, GCP, or Azure account. Bring Your Own Cloud (BYOC) setup from day one.
Local Workstation
Run the full memory stack locally on a workstation for offline development, local debugging, or sensitive work.
Security & compliance roadmap: SOC 2 Type II (in progress), GDPR-aligned data handling, self-hosted deployment option.
Open Source Core
Self-hostable & fully transparent. Read the codebase, run it locally, and contribute to The Second Memo repository on GitHub.
Open Source Core
Self-hostable & fully transparent. Read the codebase, run it locally, and contribute to The Second Memo repository on GitHub.
Simple, Honest Pricing
Pay only for what you use. No surprise bills, no upgrade walls.
Free
Forever free
Start building and prototyping your memory integration immediately.
- Up to 10,000 monthly API calls
- 50MB total document storage limit
- Community forum support channel
- Standard core SDK capabilities
Pro
Pay for what you consume
Ideal for growing projects, production apps, and custom agent networks.
- Unlimited monthly API calls
- Pay-per-token indexing pricing
- Standard webhook data connectors
- Email support service SLA
Scale
Tailored usage plans
For high-volume production setups requiring priority throughput and caps.
- Custom monthly spending caps
- Dedicated indexing cluster capacity
- Priority email & Slack support
- Self-hosted deployment capabilities
Enterprise
Dedicated contracts
For security-sensitive workloads demanding full isolation and air-gaps.
- Dedicated air-gapped server nodes
- Audit logs, usage observability, and team-level governance controls (roadmap target)
- Custom legal and SLA agreements
- 24/7 dedicated support team access
- SOC 2 compliance roadmap setup (see Haqikos Trust)
Pricing FAQ
PayQIA Roadmap
Follow our path from static vector embeddings to fully autonomous self-organizing knowledge graphs.
Core Vector DB
- HNSW Indexing
- Semantic Search
- TypeScript SDK
Graph Extraction
- Dynamic JSON Parsing
- Entity Linking
- Knowledge Graphs
Agentic Sync
- Notion Sync
- Slack Integration
- Google Drive Parsing
Edge Caching
- Global Replication
- Sub-10ms Latency
- Local Fallback
Autonomous Swarms
- Shared Agent Memory
- Swarm Coordination
- Predictive Fetching
The Future of AI Memory
Starts Here.
Join developers already building on the waitlist. Be first when we launch global intelligence memory APIs.
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