The inference layer companies use to embed AI in their own products — flat hourly pricing, zero data retention, and per-country compliance.
Investor Deck
Delaware (US) + São Paulo (BR)
Global OpCos
Confidential
noTOKEN.cloud
Investor Deck · 2026
The Problem · Step 1
Tokens have no value because consumption is unpredictable
The hidden engineering tax
Unpredictable consumption forces companies to keep part of their team modeling token-consumption algorithms and model-routing proxies — engineering hours diverted away from the core business.
Quality drops with it
Different models make different errors. Routing across models multiplies inconsistency: more rework, more reviews, less trust in the output.
The Meta evidence
Meta's internal data: AI pushed code changes up 220% — but new and upgraded user features rose only 36%. Technical incidents spiked 40%, and time spent firefighting them rose 70%. The second layoff round was suspended in October.
+220%
code changes with AI — but only +36% features shipped
Without controlling tokens, companies cannot embed AI in their products
Option A — assume the customer's tokens
The company pays the AI Labs for every end-customer request. Cost scales with usage it does not control, with no way to price its own product on top. Unpredictable cost becomes a direct margin risk.
Option B — resell the AI Lab's API
The company opens a sales pipeline for the AI Labs' APIs to its own customers — who see no value in unpredictable costs and refuse to buy. The pipeline stalls before it starts.
Both walls disappear with a single-model API at a predictable hourly cost
One stable endpoint, one flat price per hour, no metering, no reselling. And for peak tasks, a model-upgrade option above Claude Fable 5 — same cost structure, billed by the hour, on demand.
1
single model, single endpoint — no routing, no proxies
$3.50
flat per GPU-hour — the AI cost becomes a fixed line on the P&L
0
tokens to meter, invoice, or resell to end customers
Sources
GitHub Copilot moved to usage-based billing (June 1, 2026):The GitHub Blog
Only 27% favor outcome pricing. The value is in the 73% who don't.
Universe 1 — the 27% who favor rev share
Why do they accept it? Because they have no cash to buy tokens — which means no sufficient revenue, no alpha. They are startups or startup-like companies. And for them, the hyperscalers (Microsoft, Google, OpenAI, Anthropic) already run startup programs. Giving GPUs to this group is burning money unless you are cash-flow positive: the odds of a rev-share deal being profitable are the same as a VC hitting a unicorn with a $500k check — minimal.
Universe 2 — the 73% who refuse rev share
They refuse because they have revenue to protect — and they can pay for compute hours the same way they already pay AWS, GCP, OCI and Azure. These are established companies embedding AI into real products. This is noTOKEN's market: flat hourly, OpEx-style, no equity-like upside ceded, no outcome data ceded.
We bet 100% on the Compute model: enriched compute
Raw GPUs + AI APIs, on/off in minutes
The same way AWS sells compute: you rent the capacity, turn it on when you need it, turn it off when you don't. No metering of tokens, no per-request billing, no consumption forecasting. The customer pays for hours of capacity.
Why this beats both broken models
Token sale is broken because consumption is unpredictable. Rev share is a VC lottery because it only works for cash-starved startups. Enriched compute serves the 73% who have revenue: predictable OpEx, no data ceded, no lottery.
100%
of our bet on the compute model — enriched compute, not tokens, not rev share
min
to turn capacity on or off — like AWS, GCP, OCI and Azure
73%
of the market pays for compute hours — this is our customer
We are not the distribution (Consulting Network). We are not the data center (partner cloud supplies the metal). We are the inference layer that makes the apps work.
We don't ship open-source models. We ship super-optimized ones
One endpoint, agentic or simple tasks
Our R&D delivers models that are super-optimized, accelerated and intelligent — built to execute agentic tasks or simple ones on the same endpoint. No routing, no proxies, no model zoo to manage.
OpenAI-compatible API
The customer gets not only the option to turn reasoning on/off — but also to decide how to use the GPUs across their applications and their own customers. Full control of the capacity, like a cloud.
2
fine-tuned models in production — Qwen3.8 27B and another better than Claude Fable 5.
Operates on RTX PRO 6000 Blackwell (96GB), single-tenant or MIG
Public methodology: per-tier benchmarks, measurements before shipping.
Test before commit: sandbox environments to validate real usage; exclusivity for early entrants.
Channel identified: distribution via Consulting Network (global mega-system) + enterprise pipeline in Europe.
noTOKEN.cloud
07 · Traction
What Protects Us
The last moat is private high-value data. We compound it.
A mutual-help network, not a vendor list
We integrate our customers into a mutual-help network: one ecosystem, one community. They are not just buying an "enriched board" — they are entering a universe where AI is a basic service, like water, electricity, telephone and internet. And as the operator, we help design and deploy their architectures.
Each customer holds a piece of the moat
We know the last moat of this segment is private high-value data. Every customer using our services holds a piece of that moat. Connecting them all creates a super moat — a network of private data and architectures that no competitor can replicate by buying technology.
Role
Tech + R&D
Obligation (table stakes) — gets us at the table
Compliance, country by country
Barrier for later entrants (LGPD, GDPR) — accumulated protection
Customer network + private data
Super moat — compounds with every customer connected
noTOKEN.cloud
08 · What Protects Us
Competition
No major competitor sits in our quadrant
Provider
Single-tenant + ZDR
LGPD/BR
Measured optimization
Flat/hour
R&D — super-optimized models
Community network
What's missing
GPU clouds (RunPod, Lambda, CoreWeave…)
✓
✗
✗
△
✗
✗
Metal only — no product, no compliance
Managed inference (Fireworks, Together…)
△
✗
✓
✗
△
✗
Per-token, multi-tenant
Together AI
✓
△
✓
✓
△
✗
Flagship datacenter GPUs only (H100–GB200 NVL72) — different customer
Baseten
✓
△
△
✓
△
✗
Flagship datacenter GPUs only (T4–B200) — different customer
Exoscale / Regolo
✓
✗
✗
△
✗
✗
EU focus; no measured optimization
noTOKEN
✓
✓
✓
✓
✓
✓
All six, unified
GPU clouds lack the product. Managed inference lacks the pricing. Nobody has the community. The flat + zero-retention + compliance + optimized + R&D + community quadrant is empty — and only the last two compound into a moat.
The closest competitors target a completely different customer
Together AI and Baseten sell flagship datacenter GPUs (H100, H200, B200, GB200 NVL72) to AI labs and frontier-model teams — the capex arms race we refuse. Neither lists RTX PRO 6000 or Qwen3.8-27B on their pricing pages. They compete for the 27% (AI builders chasing frontier scale); we serve the 73% (companies embedding AI into real products). No overlap — the enriched-compute quadrant is ours alone.
Baseten GPU catalog (T4, A100, H100 MIG, H100, H200, B200 — no RTX PRO 6000):baseten.co/pricing
RunPod RTX PRO 6000 (raw metal, from $1.69/hr):runpod.io
GPU market median $2.37/hr across 52 providers:GetDeploying
noTOKEN.cloud
09 · Competition
Corporate Structure
Global HoldCo + per-country OpCos
1
entity exists today — the Delaware company
2018
operating since — 8 years of track record
3+
OpCos to be constituted with the raise (BR, GE, future)
HoldCo — Delaware LIVE · since 2018
All equity lives here · a single sellable asset · operating today
OpCoBR
São Paulo · LGPD · planned
OpCoGE
Germany · GDPR · planned
OpCoXX
Future countries · planned
Today, only the Delaware company exists — operating since 2018. The per-country OpCos are constituted as each market opens: each is 100% owned by the HoldCo, equity and operational control are separated, and regional compliance (LGPD/GDPR) lives in each country's OpCo — which is what becomes accumulated protection.
noTOKEN.cloud
10 · Corporate Structure
Funding to Date
Already funded — $310k raised via SAFE
$310k
Raised so far (SAFE)
$5M
Latest valuation (SAFE round)
20.3%
Held by 5 angel investors · 79.7% founder
Angel investors (5x SAFEs)
20.3%
$310k
Founder
79.7%
core
Angels entered between Dec 2024 and June 2026 — 5 SAFEs signed over 18 months, showing sustained conviction as the product went to production. The latest round valued the company at $5M. The next valuation is open — to be priced in this round. The capital structure is simple: angels + founder, with no strategic equity outstanding.
noTOKEN.cloud
11 · Funding to Date
The Investment
What we are raising — and our defense
US$ 1M
Cash flow needed to reach the next milestone
$5M
Latest valuation — next valuation open
SAFE
$310k raised · 20.3% to 5 angels
✗ No flagship-GPU arms race
No B200, no B300, no NVLink, no water-cooled GPUs. No exotic supply chains, no waiting lists — we refuse the capital-expenditure race.
✗ No electricity-hungry datacenters
Low power footprint by design — the energy bill never scales with the arms race.
✓ Exclusively RTX PRO 6000 (96 GB VRAM) — or smaller
MIG and MaxQ versions accepted. Commodity, available, efficient hardware — single-tenant, on/off in minutes.
✓ Optimized for the moat
Low consumption, high speed, high quality, fast deploy (minutes), adaptability — and high moat. Every optimization compounds into the super moat.
Use of proceeds
Scale the enterprise community · constitute the OpCos (GE, US) · deepen the engine · per-country compliance.
What investors receive
Equity in the HoldCo (Delaware) · access to an infrastructure layer with a network + compliance moat · flat recurring revenue per app.
We bet 100% on the compute model — enriched compute (raw GPUs + AI APIs, on/off in minutes) — serving the 73% who already pay AWS, GCP, OCI and Azure. Every customer connected deepens the community and adds a piece of the super moat of private data.
noTOKEN.cloud
12 · The Investment
Who Presents
Carlos Martins
M.Sc. in Nuclear Technology → special nuclear military classification
3× certified analyst for 15 years
Member of Brazil's national market regulation committee (SEC equivalent) — only 8 seats in the country
EB2-NIW green card (2018) · Mensa #386
Author of Manual dos Supersinais da Análise Técnica (2010: 1st ed.; 2020: 2nd ed.)
Author of Ramsey Statistics and Infiniteons Theory (Vol I & II)
Post-grad: Market Operator, Accounting & AI
Member of Mensa International
M.Sc. Nuclear Eng. · 20-year trading-tech builder · EB2-NIW green card (2018) · Mensa #386 · Residency: Orlando, FL, USA
