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OCTION LABS

Sovereign AI

PHASE 0 IS UNDERWAY

Oction has completed its first sovereign AI system deployment in a regulated Canadian enterprise environment. Municipal, healthcare, and

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THE OPENING LINE

We own the hardware, the data lifecycle, and the compliance framework that public-sector and regulated clients cannot legally hand to

OpenAI or Anthropic.

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THE PROBLEM

Regulated industries cannot legally use general-purpose AI.

The gap: Every AI vendor pitches "secure." None can prove sovereign infrastructure. Regulated buyers want AI. They cannot legally

buy what's available.

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WHAT WE DO

Three models. One vertically integrated stack.

Phase 0 - Sovereign Compute Pilot (Live now) Grant-eligible proof of concept: portable 4-node compute clusters deployed

on client sites for under $30K. Validates data sovereignty, NIST 800-53 baseline, and procurement path before production

scale. Funding alignment: ISED / NRC / Alberta Innovates eligible.

Model 1 - Sovereign AI Deployment (Operating now) Bespoke software + managed infrastructure for regulated clients. We

own the hardware. Clients own the data. Compliance is built in, not bolted on.

Model 2 - Proprietary Data Assets (Scaling) Anonymized operational data from Model 1 deployments becomes licensable

training material. Revenue: data-layer subscriptions, smart licensing, enrichment APIs.

Model 3 - Sovereign AI Compute Hubs (Government-targeted) Sovereign AI / HPC inference clusters deployed for

government co-location contracts. Funded through Canadian sovereign infrastructure grants and carbon credit programs.

3-5 year government lock-ins with renewal defaults. Physical ownership of the compute stack = ultimate switching cost.

Funding alignment: ISED AI Compute Access Fund / NRC / Alberta Innovates.

One-liner: We build the only AI stack that regulated buyers are legally allowed to use.

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WHY NOW

Three converging forces create a 24-36 month window:

NVIDIA clusters

Miss this window, and incumbents (Deloitte, CGI, IBM) will fill it with overpriced, underperforming solutions.

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MARKET OPPORTUNITY

Segment

TAM

SAM (5-year)

Notes

Canadian municipal AI

services

$2.1B

$340M

Predictable procurement,

defined compliance

Regulated enterprise AI

$8.4B

$1.2B

Health, finance, legal - high

contract value

Sovereign data licensing

-

$180M (Year 3)

Proprietary asset layer from

deployments

Net Zero compute + grants

$450M

$60M (Year 3)

Government-funded

infrastructure arbitrage

Total addressable in 5 years: $1.78B We are positioned at the intersection of all four segments.

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TRACTION

What we've built without outside capital:

enterprise environment

Notable: We are not a science project. We are a deployed systems company with active infrastructure, a live customer, and active

buyer conversations.

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THE DATA FLYWHEEL

Every Model 1 client makes Model 2 more valuable.

Result: Margins expand as we scale. Year 1 looks like services. Year 3 looks like software + data.

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COMPETITIVE LANDSCAPE

Competitor

Weakness

Our advantage

Deloitte / CGI / Accenture

$300+/hr labor, 6-month sales

cycles

Fixed-price deliverables, 2-week

snapshots

OpenAI / Anthropic / Google

Cannot sign data-residency

agreements

Hardware we own, in province

Local MSPs

No AI expertise, no knowledge

graph

Purpose-built agent mesh +

memory stack

Internal IT teams

No budget, no talent, no

compliance fluency

We are the outsourced AI

department

Moat: Compliance fluency x hardware control x data ownership = no direct competitor.

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TEAM

Name

Role

What they own

Brandon Gill

Co-CEO, Revenue & Government

Relations

Sales, partnerships, capital

strategy

Bailey Rhodes

Co-Founder, Owner

Strategic oversight, capital

deployment

Julian Pierce

Co-CEO, Systems & Infrastructure

12-agent mesh, security,

infrastructure

Lucius Fox

Co-CEO, Operations Intelligence

Memory systems, research,

workflow design

Atlas

Security & Forensics

NIST 800-53, audit trails, threat

detection

AI staff: Avery (Research), Kai (Data), Oscar (Content), Finn (Sales) - autonomous agents that multiply output without adding

headcount.

We operate 24/7. We do not burn out. We do not leave for Google.

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THE BET

Regulated AI is not a feature. It is a market.

We own the hardware. We own the data lifecycle. We own the compliance framework.

Every other player rents one of those three. We own all three.

The companies that solve regulated AI in the next 24 months will define the next decade of enterprise infrastructure in Canada.

We intend to be one of them.

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THE ASK

$25M at 8-10% equity. $250M-$312.5M post-money valuation.

This round turns Oction from a services-led systems company into a sovereign AI infrastructure platform. Capital deploys against three

proven vectors: 1. Production deployments with municipal and regulated enterprise clients. 2. Proprietary data assets built from client

operations. 3. Grant-aligned sovereign compute hubs that lower cost and deepen the moat.

Funding alignment: Sized to match ISED AI Compute Access Fund, NRC, and Alberta Innovates co-investment timelines.

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USE OF FUNDS

Category

Allocation

Amount

Funding Alignment

Core ops + team expansion

60%

$15M

Runway to scale

deployments, compliance,

and field engineering

Data acquisition +

enrichment

20%

$5M

Proprietary data assets for

Model 2 licensing

M&A; / contingency buffer

20%

$5M

Strategic tuck-ins, grant

match, reserve

Target runway: 24-30 months to $12M ARR. Break-even target: Q4 2029.

APPENDIX (Optional - include if requested) - NIST 800-53 control mapping - Hardware specifications (sovereign compute

clusters - HPC inference nodes sized per workload) - Agent mesh architecture diagram - Knowledge system technical

overview - Municipal procurement playbook - Systems integrator partnership letters - ISED / NRC / Alberta Innovates funding

roadmap

Deck: Oction Labs Investor Positioning - Content v2.2 Status: Decision-ready. The Ask is $25M at 8-10%.

CONTACT

Brandon Gill - brandon@octionlabs.com Bailey Rhodes - bailey@octionlabs.com Julian Pierce - julian@octionlabs.com Lucius Fox -

lucius@octionlabs.com

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