Live products + open architecture

Persistent memory and cited knowledge
for AI agents.

Card Network turns scattered business knowledge into durable, source-aware context that models and agents can retrieve, cite, govern, and act on.

Explore products See live pricingSee the proof

Storage / cited knowledge / implementation / enterprise runtime

Content Card

A durable knowledge unit with provenance and a stable address.

Action Card

A typed connection from context to tools, workflows, and checkpoints.

Decision Card

A governed branch point that makes agent behavior auditable.

Commercial surface, runtime, and open architecture.

Card Network separates the customer experience, the working MCP runtime, and the evolving specification so each surface has a clear role.

cardnetwork.dev

Products, live pricing, the Knowledge Sprint, proof, audit, onboarding, and enterprise qualification.

Commercial application

mcp.cardnetwork.dev

The active MCP runtime and discovery target for Card Network tools and agent integrations.

Runtime

Card Network architecture

Typed cards, provenance, retrieval, actions, governance, and deployment patterns that can support private and enterprise systems.

Specification and licensing

Why Business Data for LLMs Breaks

Current knowledge bases force a choice between human readability and machine efficiency. AI agents pay the bill: longer context windows, hallucinated answers, runaway token spend.

Documents Are Monolithic

Long documents get chunked at runtime, losing context and relationships. AI agents can't tell where one concept ends and another begins, so retrieval pulls 8K tokens to answer a 250-token question.

Links Are Untyped

Hyperlinks say there's a connection, but not what kind. Is it a sequence, a reference, a dependency, a branch? Agent navigation becomes guesswork and the knowledge graph never composes.

Agent Context Windows Get Wasted

LLMs have limited context windows. Stuffing 10,000 tokens of a document when the agent needs 250 tokens of relevant info is the difference between a useful answer and a $15K-a-month AI bill nobody can explain.

Business outcomes

Fix the knowledge problems that make agents expensive and unreliable.

Start with the smallest live offer that solves the immediate problem, then expand into implementation, actions, gateways, or runtime licensing as the system proves value.

A

Your support agent retrieves too much and still escalates the hard questions

Current failure

Long articles and runtime chunking force the agent to load large context windows without reliable provenance.

Card Network fix

Card Knowledge converts the support corpus into atomic, cited units that can be retrieved by topic, product, policy, and relationship.

Expected outcome

Smaller context windows, more grounded answers, and a measurable path to higher deflection.

Commercial path

Start with Card Knowledge. Use the Knowledge Sprint when the corpus needs implementation and evaluation.

B

Your sales team cannot retrieve the right proof, positioning, or objection response during the call

Current failure

Battle cards, pricing notes, and customer evidence are spread across documents, chat, and enablement tools.

Card Network fix

A cited Card Network corpus connects competitors, industries, objections, proof, and approved messaging.

Expected outcome

Reps and sales agents retrieve the smallest approved answer set instead of improvising from stale documents.

Commercial path

Start with Card Knowledge or scope a Context Gateway for multiple source systems.

C

Every agent and model integration rebuilds retrieval from scratch

Current failure

Teams maintain separate chunking, embeddings, permissions, evaluation, and context assembly for each surface.

Card Network fix

Card Network provides one persistent, typed substrate that multiple models, agents, and workflows can share.

Expected outcome

Less retrieval plumbing, more consistent answers, and a governed migration path between models and vendors.

Commercial path

Book the Knowledge Sprint for one domain or request enterprise runtime licensing for a broader platform deployment.

Model the opportunityChoose a live offer

The C.A.R.D. Framework

"Context isn't a card. Context is a DECK."

C

ontext

Layered, composable from multiple cards. Context is built dynamically from what's relevant, not pasted in from a giant doc.

A

tomic

Each card is self-contained with a predictable schema. Small enough for any context window, complete enough to stand alone.

R

etrievable

Spatial and semantic addressing. Find cards not just by keyword, but by their relationships and place in the network.

D

eck

The managed working set in context. Agents build decks to solve problems, resizing and reshuffling as they reason.

Universal Card Addressing

Every card lives in a deck. Every deck has an owner. Resolve any card from any deck, anywhere in the network.

"Draw from any deck. Your agent requests card:@[stripe.com/docs/quickstart], the resolver finds the deck, deals the card."
URI Format
card:@namespace/deck/local-id#version
Examples:
// Domain-based namespacecard:@[stripe.com/api-errors/rate-limit]
// Vanity namespacecard:@mike/docs/intro
// Versioned cardcard:@[cardnetwork.dev/spec/quickstart]#v1.0

How Authoring-Time Pre-Chunking Works

Cards as atomic units. Typed edges as relationships. Compositions as navigable structures. The substrate AI agents actually retrieve against.

Atomic Cards

Each card is self-contained, ~250 tokens. Perfect for small context windows, easy to cache, works offline. Pre-chunked at authoring time so retrieval is deterministic.

contentactiondecision

Typed Edges

Relationships have meaning. Stack for sequences, link for references, branch for conditionals, depends for prerequisites. Graph traversal without LLM inference.

stacklinkbranch

Smart Compositions

Build stacks for tutorials, networks for knowledge bases, trees for decisions. Navigate visually or traverse programmatically. RAG knowledge graphs that actually compose.

stacknetworktree

Built for the Agentic Web

Card Network bridges human navigation and AI consumption, optimized for edge computing and token efficiency. The retrieval substrate your agents already know how to use.

AI-Native Substrate

~250 tokens per card fits in any LLM context window. Typed edges enable graph traversal without inference. Your second brain for agents.

Mobile-First Reading

Cards sized like phone screens. Swipe through decks, tap to navigate, pinch to zoom out to network view. Works on every device.

Edge-Ready Retrieval

Works on Raspberry Pi, mobile devices, IoT. Minimal compute, aggressive caching, offline-capable. Data retrieval where the agent runs.

Open Protocol

Dual-license: open spec under PolyForm Noncommercial after patent provisional files. JSON Schema defined. Build your own tools, integrate anywhere, no lock-in.

Token-Efficient by Design

Instead of 10,000 tokens of document, draw exactly the 750 tokens (3 cards) you need to answer the question. AI bills drop 60-80% in 60 days.

Wild Cards

Branch points and choices built-in. Query by edge type. Find all dependencies, follow all sequences. Real graph queries on real business data.

Build Your Hand

Agents don't just retrieve context, they build a hand. Budget tokens precisely, know exactly how many cards fit. Token economics is the product.

Face-Up vs Face-Down

Public cards are face-up. Private, encrypted cards are face-down. Multi-tenant retrieval substrate that respects the audit boundary by default.

Implementation capabilities

The commercial offers are backed by an operating architecture.

Each capability below connects to a working surface, an active offer, or a concrete qualification path.

1

Authoring-time cardification

Knowledge is split at semantic boundaries before retrieval, producing stable units that agents can cite and reuse.

Evidence

The whitepaper, article demo, and live proof surface explain and demonstrate the retrieval model.

Read the whitepaper
2

Typed knowledge relationships

Cards connect through explicit relationships instead of forcing the model to infer structure from untyped links.

Evidence

The graph and card views expose the relationship model directly.

See the graph
3

Cited organizational retrieval

Agent answers remain connected to source cards, provenance, and the organizational systems that produced them.

Evidence

Card Knowledge is the live subscription path for connected and cited organizational knowledge.

Explore products
4

Agent and MCP integration

The runtime exposes Card Network capabilities through MCP and agent-ready discovery surfaces.

Evidence

The active MCP runtime is discoverable at mcp.cardnetwork.dev and through the site manifest.

Open MCP discovery
5

Context economics

The opportunity model connects retrieval discipline to support capacity and AI operating cost.

Evidence

The calculator maps the opportunity to the currently active commercial catalog.

Run the calculator
6

Fixed-scope implementation

One valuable knowledge domain can move from source material to a working cited endpoint through a bounded implementation.

Evidence

The $7,500 CNA Knowledge Sprint is active in Stripe and includes ingestion, cardification, retrieval, and rollout planning.

View the Knowledge Sprint
7

Private and enterprise deployment

Larger programs can add gateways, actions, governance, private runtime, embedded use, and commercial licensing.

Evidence

The licensing intake path captures deployment model, integrations, users, tenants, and commercial requirements.

Request licensing

Live Stripe pricing

Start with the layer you need now.

Subscribe to persistent storage or connected knowledge, or buy a fixed-scope implementation.

Card Storage

$29/month

Persistent agent memory with 5,000 cards, API and web capture, and version history.

Subscribe to Storage

Card Knowledge

$99/month

Cited organizational answers across connected knowledge sources with unlimited cards.

Subscribe to Knowledge

CNA Knowledge Sprint

$7,500one time

A fixed-scope implementation that turns one valuable knowledge domain into a working cited endpoint.

Book the Sprint
Compare all commercial paths →

Put Card Network into production.

Buy a live product, book the fixed-scope Knowledge Sprint, or request a private and enterprise deployment.

Explore productsSee pricingRequest licensing

Developer discovery

MCP manifestAgent-readable service discovery.llms.txtCommercial and technical crawler entry points.

Commercial intake

Build with Card Network

Share the knowledge sources, agents, and workflow that matter first. We will route you to Storage, Knowledge, the Sprint, or a custom enterprise path.

Book a 30-minute call

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