For Engineering Leaders Running AI Features That Bleed

Your AI is making things up
because it doesn’t have the right 250-token chunk in front of it when the customer asks.

We have a protocol for putting the right 250-token chunk in front of every AI agent at every moment. It is called Card Network. We migrate your knowledge into it in 4-8 weeks. Saving: usually 30-60% of your support burn within 60 days.

Book a 30-min discovery call Run a free Card-readiness audit

Timeline

Migrated in 4-8 weeks

Saving

30-60% of support burn in 60 days

Payback

Under 6 months on every Sprint

Card Network · Aura Media Studios · Patent Pending

Content Card

Self-contained knowledge unit, ~250 tokens, fits any context window.

Action Card

Triggers, forms, agent tool-calls bound to typed edges.

Decision Card

Branch points, agent context window decisions, prereq routing.

Open Protocol. Reference Resolver. Coming Open Spec.

Card Network is an open protocol that anyone can implement. We run the reference resolver and ship the open spec under PolyForm Noncommercial after the patent provisional files (target M1).

cardnetwork.dev

Sales, the article-shaped reader demo, the ROI calculator, the Magic Sprint pricing, and the whitepaper.

Application

api.cardnetwork.dev

Resolver and registry API: api.cardnetwork.dev (resolver coming with M2 launch).

Infrastructure

cardnetwork.org

Coming soon: open spec under PolyForm Noncommercial after patent provisional files (target M1). Paper, schemas, reference materials.

Open spec (coming)

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.

AI for Business / Token Efficiency

Save Your Business with the Right Knowledge Substrate

Five concrete bleed-scenarios where business data for LLMs is wrong-shaped, and Card Network reshapes it. Each one a real money problem with a real payback under six months.

A

Your AI customer-support agent burns $40K/quarter and still routes 60% of tickets to humans

Symptom

SaaS with 5K MAU runs an AI support agent on top of an 8K-token knowledge base. The agent escalates 60%+ of tickets because articles don't fit a single context window. AI-spend is $15K/mo ($45K/quarter). Human team is still 4 FTE because the AI can't deflect anything substantive.

Card Network Fix

Migrate 80-200 KB articles into 800-1500 cards (250 tokens each). Retrieval pulls 3-8 cards per query. Total context stays under 2K tokens. Deflection jumps from 40% to 75% in 60 days.

Math

Support team 4 FTE to 2 FTE = $200K/yr saved. AI-spend drops 30-50% as token count per query shrinks = $60K/yr saved. Total: $260K/yr saved.

Price + Payback

Magic Sprint Custom Medium $80K-$130K. Payback under 6 months.

"We save you a quarter-million a year on support. Pays for itself before you finish onboarding the next product manager."
B

Your sales team can't find the right battle-card so reps freelance their pricing

Symptom

B2B SaaS with 20-rep sales team. Battle-cards live in a doc tool (long), in an enablement platform (org-chart shaped), in a chat tool (ephemeral). Rep on a call can't pull "what's our differentiator vs Competitor X for 250-employee fintech ICP" in real-time. Rep guesses. Conversion sits 6-9 points below industry benchmark.

Card Network Fix

Ingest the battle-card library into Card Network with edges typed by ICP, competitor, deal-size. Sales-rep agent pulls 3-5 relevant cards in under 2 seconds during the call.

Math

Lift conversion 4-6 points = $2-3M new ARR on a $30M ARR base. Card migration $95K-$130K.

Price + Payback

Magic Sprint Custom Medium $95K-$130K.

"We turn your battle-card mess into a real-time co-pilot. Five-point conversion lift pays this back in the first quarter."
C

Your engineering team rewrites the same RAG pipeline three times because each AI tool is a different shape

Symptom

Mid-stage AI startup runs three model providers and two embedding providers. Each integration team rebuilds chunking + embeddings + retrieval. Three RAG pipelines to maintain, three eval harnesses, three drift-detection systems. Engineering spends 30-40% of their time on retrieval-layer plumbing.

Card Network Fix

One card-shaped corpus, one embedding pipeline, one retrieval layer. Every model and every agent reads the same substrate. Model-of-the-month becomes interchangeable.

Math

Reclaim 30-40% of an 8-engineer team = $1.2M/yr in eng-time recovered. Card migration $130K-$250K.

Price + Payback

Magic Sprint Custom Medium-to-Large $130K-$250K.

"Your engineers should ship features, not maintain three RAG pipelines. We give you one substrate."
D

Your AI features hit $8K/mo bills and the CFO is asking why

Symptom

Series-A SaaS bolted multiple LLMs into 5 product features in 6 months. Each feature shoves entire docs into prompts. Token-spend climbs 20-30% MoM. CFO is asking when this stabilizes; founder doesn't know.

Card Network Fix

Authoring-time pre-chunking shrinks context windows 60-80%. Same answer quality, fraction of the tokens. Plus agent-friendly caching on immutable card hash IDs.

Math

$8K/mo to $2-3K/mo after card-ification = $60-72K/yr saved. Card migration $30K-$60K.

Price + Payback

Magic Sprint Custom Small $30K-$60K.

"We cut your AI bill 60-70% in 60 days. CFO loves you. You can finally green-light the next AI feature."
E

Your sales-call recordings are gold and you can't use them

Symptom

Series-B SaaS records 200+ calls/month. Playback exists, search does not. No way to ask "across all my Q3 calls, which 5-token objections came up most often when we pitched Tier 2 to fintechs?" The data is there; the question is unanswerable.

Card Network Fix

Call-transcript ingester chunks each transcript at semantic boundaries into cards typed by speaker, sentiment, topic, objection, outcome. The agent retrieves cross-call patterns in seconds.

Math

Product, sales, and customer-success teams get a real-time research engine. Usually drives 2-3 product-roadmap pivots a quarter that compound. Most CFOs buy this on "we already paid for the data, it's malpractice not to use it."

Price + Payback

Magic Sprint Custom Medium-to-Large $95K-$250K.

"You're sitting on 2,400 hours of recorded customer truth and your sales VP can't query it. We turn that pile into a real-time research engine."
Calculate Your Savings

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 namespace card:@[stripe.com/api-errors/rate-limit]
// Vanity namespace card:@mike/docs/intro
// Versioned card card:@[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.

content action decision

Typed Edges

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

stack link branch

Smart Compositions

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

stack network tree

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.

AI Intelligence in Business Ops / Agent Context Window

Seven Things We Know

Card Network is more than an idea. Each pillar below ships with a public artifact behind it. When a customer asks "do you actually know this?", we point at the deliverable.

1

Authoring-Time Pre-Chunking Theory

We know how to chunk content at semantic boundaries that align with how AI agents actually retrieve, not how humans read.

Proof Artifact

Composition types spec, patent provisional draft, and the 4,227-word whitepaper.

Read whitepaper
2

Edge-Typed Knowledge Graphs at Scale

We know how to express relationships between content units (stack, link, embed, branch, sync, depends, produces) and traverse them efficiently for AI retrieval.

Proof Artifact

Edge schema and 12-system internal deployment map. Magic Graph LIVE demo.

See graph view
3

Multi-Tenant Retrieval Substrate Operations

We operate card-typed retrieval across many tenants without their data crossing, and we can prove it under audit.

Proof Artifact

Per-tenant isolation pattern + Magic Audit security audit substrate. Anyone can verify their data is not bleeding.

See audit substrate
4

Agent-Substrate Integration Patterns

We know how to wire Card Network into the agent runtime so cards become native context, not glue code.

Proof Artifact

MCP server, TypeScript SDK, sample integration code (coming M2).

See integration patterns
5

Token Economics: Making AI Affordable

We know exactly how token-spend scales with chunk strategy and retrieval discipline. We can model the savings before we sign.

Proof Artifact

ROI calculator with methodology + worked examples on three customer profiles.

Run the calculator
6

Migration Playbook: Port a Corpus in 4-8 Weeks

We have a repeatable, time-bounded process for taking a customer's existing knowledge corpus and turning it into card-shape.

Proof Artifact

Week-by-week SOW template covering discovery, edge-mapping, chunking, validation, deploy.

Scope a Magic Sprint
7

Compliance + IP Defensibility

Patent + trademark + dual-license posture. A customer who builds on Card Network is not exposed to retroactive licensing risk.

Proof Artifact

Patent provisional (filed M1), trademarks (filed M2), dual-license model: PolyForm Noncommercial spec + per-org Commercial tiers.

Read license FAQ

Start with Credits. Scale When Ready.

Your API key is your wallet. Your wallet builds your hand.

Anonymous

Trial Credits

No signup required

✓ 100 draws / hr
✓ Auto-dealt key
✓ Draw from public decks
Start Drawing
Popular

Claimed

Free Tier Credits

Email signup required

✓ 10,000 draws / hr
✓ See hand history
✓ Claim your namespace
Claim Key

Funded

Pay-per-Draw

For power users

✓ Unlimited draws
✓ AI Search and RAG
✓ Publish decks and earn
Fund Wallet

"The house doesn't always win. Publish a deck, earn when others draw."

Ready to stop bleeding tokens?

Book a 30-minute discovery call. We map the ROI math, agree the timeline (4-8 weeks), name a price band, and walk away with a signed SOW or skip. Three doors, one substrate.

Book a 30-min discovery call Calculate ROI Run a free audit
GET /resolve (resolver coming with M2 launch)
# Draw a card, no signup required
curl "https://api.cardnetwork.dev/resolve?uri=card:@mike/docs/intro"
# Response includes your session key
X-Session-Key: cn_anon_xyz789
# Keep this key to build your hand across requests

Three doors / pick one

Get the Card Network Migration Playbook

Drop your email. We send the Migration Playbook (the 4-to-8-week SOW template covering discovery, edge-mapping, chunking, validation, and deploy), schedule a 30-minute discovery call with Mike, and run a free Card-readiness audit on your AI workload.

Book a 30-min call instead

No spam. We use your email to send the Migration Playbook + whitepaper, schedule the call, and follow up on the audit. That is it.