How Papr Works
Your Brain Doesn't Search.
It Predicts.
We built an architecture that does the same for AI agents.
Takes in everything
Understands meaning from any source — conversations, docs, data
Connects & synthesizes
Maps relationships and turns scattered facts into understanding
Predicts & surfaces insights
Anticipates what's needed and delivers the “so what” — not just raw data
But today's AI agents don't work this way…
The Problem
Context Supercharges Agents—
Then Kills Them at Scale
Adding context to agents works brilliantly at first. But as data grows, traditional approaches break down in four predictable ways.
Latency Explodes
200ms → 3s as context grows. Agents become too slow for real-time use.
Accuracy Plummets
Search returns noise at scale. More data means less relevant results.
Engineering Burns
Teams maintain pipelines, not features. Complex glue code everywhere.
Time Wasted
Constant retuning, never shipping. Retrieval quality becomes a full-time job.
The problem isn't that agents lack data.
It's that they lack a brain to organize it.
The Insight
Think About How You Remember
Your brain doesn't file information into folders or search through an index. It does something far more powerful.
It takes in everything
You absorb conversations, things you read, stuff people tell you—and your brain doesn't file it alphabetically. It grasps the meaning.
It connects and makes sense of it
When someone mentions a project, your brain doesn't just recall a file—it instantly links the people involved, the last meeting, the deadline, the open questions. It synthesizes separate facts into a story.
It predicts what you'll need—and surfaces insights
Before you walk into a meeting, your brain has already surfaced the relevant context. But it doesn't just hand you raw facts—it gives you the so what: what changed, what matters most, what to watch out for.
It knows what's sensitive
You instinctively know what to share with whom. Some things are for your team, some for your manager, some are private. Your brain is a gatekeeper, not just a library.
Your brain doesn't just retrieve memories. It synthesizes them into understanding and predicts what you'll need—before you consciously ask.
Papr gives AI agents these same four capabilities.
The Papr Architecture
How Papr Gives AI a Brain
Takes in everything
Universal Data Ingestion
Papr ingests both unstructured and structured data from any source. It doesn't just store fragments—it understands the meaning of every piece of information, regardless of format.
Unstructured Data
Structured Data
How it works
Ingest via API or conversation
Send any text, document, or structured data through a single endpoint
Semantic understanding
AI extracts meaning, entities, and relationships—not just keywords
Grouped & connected
Related data is automatically bundled—one query retrieves the full picture
Connects and makes sense of it
The Context Graph & Synthesis Engine
What's blocking the Acme deal?
9 fragments returned, no connections
I found several documents about Acme. There are pricing discussions and some notes. The deal seems to be in progress.
What's blocking the Acme deal?
1 graph query · 4 connected entities · 150ms
The Acme deal ($450K) has two blockers:
Papr doesn't just connect data into a graph—it synthesizes across relationships to generate summaries, surface blockers, and recommend next steps. Agents reason, not just retrieve.
“What changed since last week?”
Tracks evolution over time
“Why is this deal at risk?”
Reasons across relationships
“What patterns do these share?”
Finds hidden connections
Predicts what you'll need
The Predictive Layer
Every query starts from scratch
Context already pre-staged before the query
Predicted goals & tasks on-device
Recent memories, on-device embeddings
Cloud vector search (fallback)
Deep multi-hop reasoning (fallback)
Context pre-staged before you ask
How Papr Predicts What You'll Need
Goal-Based Seeds
Your goals and use cases are embedded and used to find semantically relevant memories
60% weightBehavioral Transitions
Markov chains learn which memories are co-accessed, predicting 2–3 steps ahead
30% weightEngagement Feedback
User feedback and engagement signals are fused via Bayesian weighting to refine confidence
confidence signalAccess Frequency
Log-normalized retrieval counts ensure frequently needed memories stay hot
10% weightThe system is self-correcting: every retrieval updates the model and feedback refines confidence. Bad predictions decay exponentially. Good ones accumulate evidence.
Accuracy at Scale
Degrades as data grows
Improves with more data
#1 on Stanford's STaRK Benchmark
90x
Faster
100x
Fewer tokens
50x
Lower cost
Learns from every interaction
Conversation Intelligence
Papr processes every conversation between users and agents in real time. Messages are analyzed in batches, generating hierarchical summaries, extracting structured context, and detecting learnings—for both the user and the agent.
User Insights
Preferences, goals, tasks, facts, and context extracted from what users say
Agent Learnings
Skills, patterns, and techniques the agent discovers—improving with every conversation
Message Processing Pipeline
Messages stream in
User & agent messages stored in real time
Batch analysis (every 15 msgs)
AI analyzes the batch for memory-worthy content
Hierarchical summaries
Short-term, medium-term, and long-term summaries generated
Structured extraction
Files, projects, tech stack, decisions, next steps
Graph + memory creation
Entities linked, insights stored, learnings captured
Every conversation becomes structured knowledge: Tasks, Resources, Projects, People, and Goals—all connected in the graph.
Controls how memory works
The Policy Engine
The Policy Engine is the control plane for how Papr processes, structures, and governs memory. It combines graph generation control, schema enforcement, and safety standards into a single configuration—the MemoryPolicy.
Auto Mode — LLM-Powered Extraction
In auto mode, the LLM freely extracts entities and relationships from your data. Use node constraints to guide what gets created, how entities are matched, and which properties are set.
Node constraints — Control creation policy (upsert vs lookup), set property values, define search behavior
Edge constraints — Control how relationships are created between entities
Schema reference — Point to a schema for default policies across all memories
Example: Auto with constraints
{
"mode": "auto",
"node_constraints": [{
"node_type": "Deal",
"create": "upsert",
"search": { "properties": ["name"] },
"set": { "stage": {auto} }
}]
}
LLM extracts Deal entities, upserts by name, and auto-fills the stage
The Result
What Context Intelligence Enables
When agents can take in everything, connect it, predict what's needed, and respect boundaries—every interaction transforms.
Customer Support
Agents recall every past ticket and preference—proactively connecting today's issue to past resolutions instead of starting from scratch.
Development
Coding agents understand the full history—the PR that introduced a function, the bug it fixed, the design discussion behind it. Ask “why?” and get a real answer.
Sales Intelligence
Call transcripts, emails, and meetings auto-connected. Deal status, buyer signals, and risks synthesized—without anyone touching a CRM.
Give Your AI Agents
Context Intelligence
An AI without context is a tool. An AI with context intelligence is a partner. Start building agents that understand your world.