QIA — The context engine for agentic AI.
QIA is the core intelligence engine built by Haqikos. Rather than simply dumping embeddings into a vector database, QIA handles real-time semantic search, context compression, and automatic memory decay — streaming only the most relevant intelligence to the calling LLM.
QIA is not a standalone product you interact with directly. It is the engine layer that powers PayQIA and any future Haqikos products — the same way a model family powers multiple applications.
How QIA Works
Plug In
Install the SDK. Works in every runtime — edge functions to long-lived servers.
Ingest
Bring in any data type. QIA extracts and structures it automatically for retrieval.
Understand
Entity resolution structures ingested data into a unified, linked node cluster graph.
Retrieve
Memory, retrieval, and user profiles live in one queryable graph — not three separate systems.
Real-Time Traversal
Sub-300ms graph traversal, every request. Your agent reaches into memory at request time.
Two ways to give your agent memory.
PayQIA doesn't force a single memory philosophy. Use QIA Direct for control. Use QIA Graph when you want it handled. Most teams end up using both.
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
Seven building blocks for persistent AI context.
Fundamental components engineered to orchestrate persistent, structured context for agentic systems.
Memory & Continual Learning
LivePersistent, structured memory built as a knowledge graph. Facts update dynamically as new context is introduced.
Q-RAG (Contextual Retrieval)
LiveReal-time semantic search with reranking and profile fetching. Monitors embedding states and links semantic graphs.
Session Filesystems
LiveStructured per-session state that persists across requests. Developers get a queryable file system for agent context.
User Profiles
LivePersistent per-user identity and preference stores. Memory, retrieval, and user profiles in one queryable graph.
Connectors
LiveIngestion pipelines for Notion, Drive, GitHub, and more. PayQIA extracts and structures data automatically.
Extractors
LiveDocument, URL, and media ingestion. Entity resolution automatically structures ingested data into linked node clusters.
Qualitative Analysis
LiveAsk anything about your user's context. QIA monitors embedding states and acts on analytical queries.
We don't think benchmarks tell the full story — but we're building toward leading them.
Avg. Memory Recall
340ms
sub-300ms target
Context Reduction
92%
vs. raw context passthrough
Graph Traversal
<300ms
per request, at query time
All figures above are labeled as unverified, pre-launch, internal projections and represent internal roadmap benchmarks and tests. They are not independently verified and should not be treated as production guarantees.
Ready to give your agents persistent memory?
PayQIA — built on QIA — is live and available to developers now. Free tier available.