Context Engine
Intelligence That Compounds
Most AI systems retrieve text. Pyrana's Context Engine builds a living knowledge graph that gives your agents a working memory, one that compounds over time and across every use case.
The Problem
RAG Gives Agents Amnesia
Traditional retrieval-augmented generation treats knowledge as disposable. Documents go in, chunks come out, and every session starts from zero. There's no memory, no provenance, and no way to build on what was learned before.
For enterprise AI, that's not good enough. Your agents need knowledge they can trace, trust, and build on, a working memory that gets smarter with every interaction.
Better context beats more context
Traditional RAG
Context Engine
How It Works
Extract. Store. Retrieve. Learn.
A continuous cycle where knowledge flows from your documents into a living graph, and agents write back what they learn, creating a compounding knowledge asset.
Extract
Documents become knowledge
Source documents, conversations, and system data are processed through domain-specific extraction lenses. Each lens targets a different type of knowledge (facts, requirements, procedures, definitions) producing structured, atomic Context Units (CxUs) with full source traceability.
Store
A knowledge graph, not a document store
CxUs are stored as content-addressed, immutable records and connected via typed relationships in a knowledge graph. Every unit carries its provenance chain, from source document to chunk to extraction, so you always know where knowledge came from and why it was trusted.
Retrieve
Connected context, not keyword matches
At runtime, agents retrieve context through a hybrid of graph traversal and vector similarity, fused and ranked for relevance. This means agents receive rich, connected, traceable context, not just retrieved text, grounded in your actual business knowledge.
Learn
Knowledge that compounds
As agents operate, they write back new knowledge: findings, decisions, and derived insights as new CxUs in the graph. Over time, your knowledge base grows richer, more connected, and more valuable. Every workflow makes the next one smarter.
Every cycle makes the next one smarter
Context Units (CxUs)
Atomic, Immutable, Traceable Knowledge
The Context Unit is the fundamental building block. Each CxU is a single, verifiable claim grounded in source material, giving AI agents provenance, not just recall.
Claim
"Pure steam in the SIP cycle shall be maintained at 2.5 bar(g) during sterilization when load temp ≥ 121°C"
Supporting Quotes (up to 5)
SIP cycle operating pressure: 2.5 bar(g) ± 0.1..." , line 142
Minimum load temperature of 121°C required..." , line 208
Validated lethality target F0 ≥ 12..." , line 215
Metadata
v1.0 | created 2025-12-11 | supersedes: 1220e3b0c4...
Content-Addressed
Every CxU has a deterministic ID derived from its content, not a database index. Identical knowledge always produces the same ID.
Fully Traceable
Every fact is linked to its source document, version, and the supporting quotes that ground it. Provenance is built in, not bolted on.
Immutable by Design
When knowledge changes, a new CxU is created with a new ID. Old versions are preserved. Knowledge can't silently change beneath your agents.
Composable
CxUs assemble into Sets, curated bundles scoped by topic, business unit, or use case. Agents receive exactly the context they need, nothing more.
Model-Agnostic
Knowledge lives in the context layer, not in model weights. Switch models, add new ones, or upgrade. Your knowledge graph stays intact.
Graph-Connected
CxUs are linked through typed relationships in a knowledge graph, enabling agents to traverse and reason across connected concepts.
Why It Matters
A Compounding Knowledge Asset
Traditional AI tools start from scratch every time. The Context Engine creates a permanent, growing knowledge graph that captures every rule, process, and piece of domain expertise your business accumulates.
This reusable knowledge graph makes every new agent and workflow smarter, reducing cost and time for the next initiative. Your AI becomes more capable with every use case you deploy.
Extract
from source docs via the Context Engine
Assemble
into Sets, curated bundles scoped by topic
Retrieve
at runtime via hybrid search and pack into prompts
Audit
end-to-end: every AI response logs which CxU IDs were used
Give Your Agents a Memory They Can Build On
See how the Context Engine turns your business knowledge into a compounding advantage for every AI workflow you deploy.