The company building the memory layer for agentic AI.
Haqikos builds QIA — a context and intelligence engine — and ships it as PayQIA, the Memory OS for AI agents. Every session starts from zero. We're fixing that.
One stack. Three layers.
Haqikos is the research company. QIA is the engine it builds. PayQIA is the product that ships it to developers. Each layer has a distinct job.
Haqikos
Research, safety, and engineering. The parent company that builds QIA and defines how AI memory systems should behave.
QIA
The core context and intelligence engine. Handles semantic search, context compression, automatic decay, and knowledge-graph construction.
PayQIA
The Memory OS for AI agents. Built on QIA — persistent memory, Q-RAG, session filesystems, user profiles, connectors, and extractors.
QIA doesn't just store data. It understands.
Rather than 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.
Persistent Memory
Facts, preferences, and context survive across sessions, tools, and time. Intelligence compounds instead of resetting.
340ms
avg recall latency
Q-RAG Retrieval
Real-time semantic search with reranking and profile fetching. Only the most relevant context is streamed to the calling LLM.
92%
context reduction
unverified, internalKnowledge Graph
Automatic entity resolution links related memories. Smart decay models prune stale information over time.
QIA Graph
auto-ingestion mode
Zero-Knowledge Encryption
Memory infrastructure that cannot read your data. Built for regulated industries: finance, healthcare, government.
On-Prem
deployment option
PayQIA — The Memory OS for AI
PayQIA gives every AI agent, app, and assistant persistent, searchable memory. State-of-the-art memory, RAG, user profiles, connectors, and extractors — all built in. Extremely low latency. Zero-knowledge encryption.
Scoped research. Honest labels.
We publish research directions, not product wishlistss. Every finding is labeled by its verification status.
Context Compression & Retrieval Efficiency
Active ResearchReducing what LLMs need to process without losing the signal. Up to 92% context reduction in internal tests.
Knowledge Graph Entity Resolution
Active ResearchHow memories should link, merge, and decay in a persistent graph — the architecture behind QIA Graph.
Multi-Agent Shared Memory
Early StageSwarm coordination via a shared context layer. The design thinking behind our 2027+ roadmap.
Memory-System Safety
Active ResearchPrivacy-preserving retrieval, tenant isolation, and right-to-forget guarantees for production memory infrastructure.
Research & Releases
PayQIA launches NEWQIA Vector Memory as default
QIA's vector memory layer is now the default retrieval backend for all PayQIA deployments, replacing the legacy embedding store.
Context Compression Benchmarks: Internal Report v1
Our lab publishes initial findings on context reduction techniques — 92% compression ratio in internal tests. Labeled as unverified, pre-launch.
Haqikos establishes engineering base in New Delhi
Expanding our core team to accelerate QIA's memory infrastructure and PayQIA's developer platform.