# Teleprompter Script — All 7 Reels

> Copy this entire file into your teleprompter app.
> Record straight through. Pause 3 seconds between reels (edit points).
> Total recording time: ~5 minutes for all 7.

---

## REEL 1 — THE PROBLEM (35s)

Your AI coding assistant is guessing.

It reads fifty thousand tokens,
burns fifteen cents,
takes two minutes...

to answer a question
the graph already knows.

Memory. Embeddings. Then LLM.
Three layers. Cheapest first.

That's why it costs
three ten-thousandths of a dollar
instead of fifteen cents.

I'll show you how. Follow for the series.

[PAUSE 3 SECONDS]

---

## REEL 2 — MEMORY LAYER (40s)

When you ask
"what breaks if I change auth"

the first thing GraQle does
is NOT call an LLM.

It checks the graph.
Pure traversal. Zero cost.

The graph already knows:
three modules import auth.
Eleven modules break if auth changes.

And your team learned three months ago —
never skip refresh token rotation.

All of that.
Zero tokens.
Zero dollars.
Under one millisecond.

That's the memory layer.
And we haven't touched the model yet.

[PAUSE 3 SECONDS]

---

## REEL 3 — EMBEDDING LAYER (35s)

Memory told us who's connected.
Now — who's actually relevant?

Your query gets embedded.
One call. One vector.

That single vector gets compared
against every chunk in the graph —
already cached from the scan.

Auth: point nine four.
Routes: point eight seven.
Config: point three one — gone.

Eleven connected nodes.
Eight survive.
Cost: one hundred-thousandth of a cent.

Two filters done. Still no LLM.

[PAUSE 3 SECONDS]

---

## REEL 4 — AGENTS TALK (45s)

Now it gets interesting.

Eight nodes survived.
Each one becomes an agent.

Auth asks routes:
"Do you consume my JWT validation?"

Routes responds:
"Yes. Three endpoints use your decorator."

API confirms:
"I import verify-token and AuthError."

Middleware says:
"I wrap you on every request.
If you break, I break."

Two rounds. Five messages.
These aren't guesses.
They checked the code.

Total cost:
three ten-thousandths of a dollar.

For the answer that would have cost
fifteen cents.

[PAUSE 3 SECONDS]

---

## REEL 5 — VS RAG (40s)

People keep asking me —
"isn't this just RAG?"

No. And here's the difference
in one frame.

RAG: embed the query,
vector search,
stuff top-K chunks into one prompt,
one model answers alone.

GraQle: graph memory,
embed query,
activate subgraph,
eight agents negotiate,
consensus with evidence.

RAG finds what's similar.
GraQle finds what's
connected AND similar.

Routes dot py has low text similarity
to auth. But it imports auth.
RAG misses it. The graph catches it.

Fifty thousand tokens versus five hundred.
That's not optimization.
That's a different paradigm.

[PAUSE 3 SECONDS]

---

## REEL 6 — CONFIDENCE (40s)

When your AI says "I think" —
that's a hallucination with a smile.

When GraQle says ninety-two percent —
it calculated that
across five independent axes.

Relevance: ninety-five percent.
How well do the nodes match the question?

Evidence: ninety-three percent.
Is every claim backed by a code reference?

Consensus: eighty-seven percent.
Did the agents actually agree?

High confidence — you ship.
Low confidence — you investigate.
The AI tells you which.

Every answer has an evidence chain.
Every claim links to source code.

That's the difference
between "I think" and "I know."

[PAUSE 3 SECONDS]

---

## REEL 7 — FULL PICTURE / FINALE (50s)

Here's the whole model.

You ask:
what breaks if I change auth?

Layer one — Memory.
The graph checks edges, consumers, lessons.
Ten thousand nodes down to eleven.
Cost: zero.

Layer two — Embeddings.
One vector call scores relevance.
Eleven down to eight.
Cost: one hundred-thousandth of a cent.

Layer three — LLM.
Eight agents activate.
They exchange messages. Two rounds.
They reach consensus.

The answer:
auth breaks routes, API, and middleware.
Eleven modules impacted.
Ninety-two percent confidence.

Total cost:
three ten-thousandths of a dollar.
Total tokens: five hundred.
Total time: five seconds.

The same question with traditional tools?
Fifty thousand tokens.
Fifteen cents.
Two minutes.
No confidence score.

Memory first.
Embeddings second.
LLM last.

That's GraQle.
Ten seconds to install.
And your AI stops guessing.

---

> [END OF RECORDING SESSION]
> Total: ~5 minutes of raw footage → 7 reels after editing
