The Next AI Business Is Built on Two Foundations.
Software that keeps services connected. Computing infrastructure that powers AI at scale.
A US$15 million investment opportunity in ConneXionONE Corp. (OTC: CNNN), built around DualSpan and modular GPU computing services.
Healthcare-led. Infrastructure-backed. Platform-scaled.
The next opportunity in AI starts with a human need.
Every person sees a service from their own point of view. They bring a need, an experience and a hope for what comes next. Every institution sees it through a different lens: expertise, responsibility, time and operational limits. Both want a good outcome. Too often, their information and expectations never fully meet.
That gap creates the real opportunity. When people feel understood, services can become relationships. When institutions can remember what was agreed, respond with care and complete the next step, each interaction becomes more valuable than the last.
CNNN is building two connected businesses around that insight: DualSpan, the AI service relationship platform, and modular GPU infrastructure that powers enterprise AI. We begin with a need investors in healthcare already know, then build a model that can travel across industries.
Why continuity is valuable
An AI answer can be useful today. A service relationship becomes valuable when the next visit starts with the right context: what the customer asked, what was agreed, who owns the next step and what has already been completed. For a business, that continuity can reduce repeated work and create reasons for customers to come back.
CNNN is turning this human expectation into a repeatable technology and business model. First we show how a single service journey improves. Then we explain how that experience becomes a software product, a paying institutional relationship and a broader platform business.
People want to be understood. One poor experience can end a relationship.
Independent studies point to a market shift: people expect personal attention, react financially when service fails, and want control over how their information is used. Businesses know the expectations are rising, yet often struggle to keep pace.
Expect personalized interactions
71% expect personalization; 76% are frustrated when recommendations or messages feel irrelevant.
Bad experiences linked to reduced or stopped spending
53% of reported poor experiences were followed by customers cutting or stopping spend — not 53% of all consumers.
Executives say expectations move faster than their response
Seven in ten surveyed executives say customer expectations evolve faster than their companies can adapt.
Personalization has become a basic part of good service.
McKinsey’s Next in Personalization study found that 71% of surveyed U.S. consumers expect tailored interactions, while 76% become frustrated when the experience feels irrelevant. The same research found that people want businesses to recognize them as individuals rather than treat each purchase or request as an isolated event. This raises the value of relevant context: remembering a preference, knowing what has already been explained, and making the next contact easier.
Service gaps can translate into lost spending.
Qualtrics’ 2025 Consumer Trends report, based on nearly 24,000 consumers across 23 countries, found that 53% of reported bad experiences were followed by reduced or completely stopped spending. The underlying responses included 38% reducing spending and 15% stopping it. Common causes included service-delivery problems (46%) and communication problems (45%). This connects service continuity to business economics: unanswered questions, dropped handoffs and repeated explanations can create friction precisely when a customer is deciding whether to return.
Companies can see the demand, but struggle to keep up.
PwC’s 2025 U.S. Customer Experience Survey found that 70% of business executives believe customer expectations are moving faster than their organizations can adapt. It also reported that 52% of consumers had stopped using or buying from a brand following a bad product or service experience. The opportunity is not another channel that can answer more questions. It is a practical way to help institutions coordinate knowledge, responsibilities, follow-ups and confirmed outcomes across the full service journey.
Relevant service depends on permission and trust.
Customers want recognition, but they do not want uncontrolled use of their personal information. Qualtrics found that 64% prefer buying from businesses that tailor experiences to their needs, while only 27% are comfortable with organizations using data they did not volunteer. Just 26% trusted organizations to use AI responsibly in the same report. The design response is to give people a meaningful choice over their information and to let institutions use only context they are authorized to handle — instead of gathering more data by default.
Healthcare makes the need for continuity especially clear.
The World Health Organization describes people-centred, integrated health services as care that is coordinated across prevention, diagnosis, treatment and ongoing support. In practice, a useful report is only one part of a longer journey. Individuals still need to understand what happens next; providers need clear service handoffs and dependable follow-up. DualSpan Bio is designed to connect these authorized service steps without replacing professional medical judgment.
Turn one helpful interaction into the beginning of the next.
This is the reason for DualSpan’s two-sided design. 有方 Alby supports the individual: clarifying needs, organizing personal context and remembering what the person chooses to keep. 常在 Ays supports the institution: explaining approved services, receiving requests, assigning authorized work and tracking confirmed follow-up. Relationship Memory and governed collaboration connect the two without merging their private records. DualSpan Beyond brings this same value to non-medical service institutions, while DualSpan Bio applies it to health and healthcare relationships.
The commercial opportunity: service that remembers, follows through, and earns the next interaction.
Customer relevance can support repeat use; reliable handoffs can support institutional efficiency; permission-based memory can support trust. DualSpan is designed to turn those needs into software subscriptions, enterprise services and usage-based offerings that can be tested through real customer activity.
Primary research and methodology
- [1] McKinsey — McKinsey, “The value of getting personalization right—or wrong—is multiplying” (Nov. 2021). U.S. consumer personalization study; 71% / 76% figures from the 2021 survey (n=1,013).
- [2] Qualtrics — Qualtrics, “2025 Consumer Experience Trends” (released Oct. 2024). Global consumer study, 23,730 respondents in 23 countries. The 53% refers to reactions following reported bad experiences; 64%, 27% and 26% reflect respondents to the relevant survey items.
- [3] PwC — PwC, “2025 Customer Experience Survey” (Sept. 2025). U.S. survey of 5,511 consumers and 406 business executives; the 70% describes executive opinion, not consumer responses.
- [4] WHO — World Health Organization, “Services organization and integration,” integrated people-centred health services; used here as a service-design framework, not a DualSpan outcome measure.
Different studies, populations and survey years are shown separately. They describe market needs; DualSpan’s own impact will be measured through verified service completion, retention and paid adoption.
A result is valuable. A connected next step makes it useful.
Imagine an AI-enabled medical device delivers a report. A person needs to understand it, prepare questions and find the right professional service. The provider needs to welcome that person, handle the request and follow through. Neither side should need to begin again at every touchpoint.
有方 Alby · Personal AI
有方 Alby — Your Personal AI
Alby works for the individual. It helps a person understand needs, compare suitable services, prepare questions, organize approved personal context and remember what the person chooses. One Alby can serve the same person across both DualSpan domains while sensitive health data remains separately protected.
常在 Ays · Service Provider AI
常在 Ays — The Provider’s Digital Service Twin
Ays works for a verified service institution or qualified professional. It presents approved knowledge and services, receives requests, hands work to authorized people and follows through on confirmed outcomes. In Operations and Enterprise, Ays can coordinate internal work through Teammate and approved AI agents.
The journey is designed to continue: a person finds a service, the institution responds, an authorized handoff is completed, and confirmed outcomes inform the next interaction. In healthcare, professional decisions remain with the appropriate clinicians.
Designed to support professional care and service coordination. Clinical decisions stay with qualified professionals.
The service handoff is where lasting value begins
The patient may start with a medical report, but the next valuable step can be simple: a trusted explanation, a list of questions for the doctor, a properly authorized request to the clinic, or an agreed reminder. The individual should control personal context while the clinic continues to manage its own professional decisions and records.
This is why DualSpan connects two different AI roles rather than forcing customers and institutions into one shared chatbot. Personal understanding and institutional delivery work together through a documented request, a responsible owner and a confirmed result.
Two AI assistants. Two clear roles. One better service experience.
That is why DualSpan is built as one reusable AI Core—not a separate chatbot for every business. Alby, Ays, the ERAL framework, governed memory, Hub and controlled workflows can support different service industries while keeping each organization’s data and responsibilities separate.
One technology foundation, different responsibilities
ERAL stands for Evidence, Reasoning, Action and Learning. It uses approved information to support a decision, helps the right person or tool act within authority, and learns only from confirmed outcomes. Personal Alby and institutional Ays work across the same core while their memories, permissions and service responsibilities remain distinct.
The shared architecture also supports Teammate, an employee-facing work AI, and Professional Ays Service, where a qualified professional offers approved services through an Ays identity. The result is a foundation that can support customer service, internal operations and professional expertise without rebuilding the platform for each business.
Once a single journey can continue, the same method can serve many institutions.
DualSpan Bio
DualSpan Bio helps healthcare and wellness organizations deliver services that continue beyond an appointment. Individuals can organize their health journey with Alby; institutions use Ays and Hub to support inquiries, appointment steps, handoffs and follow-up.
Our commercial path combines standardized Hub subscriptions with tailored semi-turnkey and full-turnkey enterprise deployments. Each enterprise project has a defined scope, implementation milestones and ongoing support plan.
A practical healthcare journey creates three business entry points
DualSpan Bio starts by helping individuals carry forward useful context between visits. Institutions then gain an AI service front desk with clearer handoffs and follow-up. Larger health organizations can expand into governed workflow, integration, dedicated deployments and managed support.
The deeper enterprise opportunity is not another medical diagnostic device. It is the coordination layer around existing medical technology and the service organization. A clinical decision stays with the appropriate professional; the platform helps make the authorized service steps easier to understand, assign and complete.
DualSpan Bio provides a demanding, high-value entry market. What works in professionally governed services can strengthen the common platform while health information remains protected.
DualSpan Beyond — AI services for non-medical organizations
A relationship platform for retail, travel, hospitality, education, fitness, professional services and other non-medical businesses.
DualSpan Beyond begins with distribution partners that already serve communities of customers or members. Rather than asking every new service provider to find users from zero, the partner introduces the platform through existing customer relationships.
One channel can start a network. Every active institution can help it grow.
Customers and institutions pay DualSpan Beyond directly for eligible platform services. DualSpan Beyond then shares an agreed portion of collected, eligible revenue with the referring platform partner. As institutions share their own service links and QR codes, the network can reach customers beyond the first partner.
A direct-billing partner model for non-medical markets
DualSpan Beyond focuses on non-medical organizations and professional service businesses. These include retail, hospitality, tourism, education, fitness, beauty, consulting, property services and other everyday sectors. It does not replace DualSpan Bio in regulated healthcare workflows.
Its first commercial channel is an existing platform with members or customers. The platform introduces DualSpan Beyond; institutions and eligible individual users subscribe and pay DualSpan Beyond directly; the channel partner earns a share of eligible collected revenue. This keeps the customer contract and billing relationship with the operating business while rewarding the partner for real distribution work.
Every institution then becomes a potential second point of distribution. Its own customers may meet Ays through a service QR code or web link, choose to activate Alby, and continue into other non-medical services. Growth becomes progressively less dependent on any single acquisition partner.
DualSpan Beyond takes that shared capability into many service industries. Distribution begins with platforms that already have users or members; users and institutions pay DualSpan Beyond directly, and the platform partner receives agreed revenue sharing on eligible receipts.
Every good service makes the next one easier.
A partner introduces people and institutions. Alby helps people discover a service. Ays helps the institution respond. With permission, a useful service record makes future interactions smoother. Better experiences support renewals, referrals and more institutional participation.
This loop creates several ways to earn: Hub subscriptions, AI add-ons, enterprise projects, and continued support. It also generates a practical scorecard: active users, paid institutions, completed services, retention and real cash collected.
A measurable commercial loop
The loop begins with a real need, not an advertising impression. A person arrives, the institution responds, the service reaches an accountable result, and authorized context makes the next visit easier. Repeat service can support renewal; renewal supports stronger provider participation; more participating institutions create new entry points.
The commercial scorecard follows this order: activated people, verified institutions, qualified service requests, acknowledged results, retained paying accounts and collected revenue. Each stage can be improved independently, so the network is managed as a real operating system rather than a claim about viral growth.
Start simple. Upgrade when the value grows.
Hub Free helps organizations create an AI-ready service entry point. Hub Dual AI adds managed AI reception and ongoing engagement. Hub Operations adds more structured work execution. Full-turnkey and semi-turnkey enterprise packages address deeper deployment, integration and governance needs.
| DUALSPAN | COMMERCIAL MODEL |
|---|---|
| Hub Free | Free entry point |
| Hub Dual AI | US$99 / month · paid-pilot price |
| Hub Operations | US$299 / month · modeling assumption |
| Semi-turnkey | US$100K–300K / project |
| Full-turnkey | US$300K–900K / project |
The planning model uses US$99 per month for Hub Dual AI, the approved paid-pilot price for DualSpan Beyond. US$299 per month for Hub Operations is an internal planning assumption. Enterprise project fees and compute service rates below are fundraising-model assumptions, to be set through customer contracts.
Enterprise and GPU numbers are planning ranges, priced for the actual scope, contract term, GPU configuration, reserved capacity and SLA. The revenue model later uses selected points inside these ranges to show the sales needed for the Year 1 target—not standard customer quotes.
Customers can grow into the platform
The standard path starts with a free service presence and grows into paid Alby–Ays continuity. When an institution needs structured employee work, the Operations path adds Ays coordination, Teammate collaboration, governed Agents and AI Work Credits. Larger organizations can choose a dedicated Full-turnkey or Semi-turnkey enterprise engagement.
Enterprise pricing is a range because one site with a few workflows and a multi-entity deployment with healthcare-grade governance have different implementation costs. The quotation process starts with customer outcomes, defines integration and acceptance milestones, and then prices delivery and ongoing support.
One relationship platform. More than a dozen ways to earn.
As customers move from first contact to frequent service, the same platform can earn subscription income, paid AI usage, enterprise implementation fees and ongoing support. Each charge corresponds to a specific customer benefit. We can grow account value without requiring every customer to buy every feature.
| Revenue stream | What the customer buys | Indicative price |
|---|---|---|
| Institution plans | ||
| Hub Free | Free service entry; AI reception and basic institutional visibility | Free / adoption channel |
| Hub Dual AI | Alby × Ays continuity and institution services | US$99 / month |
| Hub Operations | AI teamwork, controlled workflow, Policy & SOP capabilities | US$299 / month |
| Personal AI & usage packs | ||
| Alby Memory & AI Pack | Individual memory capacity and AI credits | US$9.99 / pack |
| Relationship Memory Expansion | Institutional searchable memory +1M tokens | US$4.99 / month |
| Relationship Memory Expansion Plus | Institutional searchable memory +3M tokens | US$12.99 / month |
| Hub Creative Credits | 25 credits for AI-generated creative images | US$9.99 / pack |
| Ays Voice Add-on | 120 extra minutes of institutional admin voice | US$9.99 / 120 min |
| AI Work Credit Pool | Enterprise AI work usage beyond included pool | US$29 / 3,000 credits |
| Knowledge Capacity Pack | Additional institutional knowledge index / sources | By approved offer |
| AI Answer Capacity Pack | Institution purchases capacity for authorized paid answers | By approved offer |
| Enterprise & professional solutions | ||
| Semi-turnkey Enterprise | Fast configured deployment, integrations, process mapping | US$100K–300K / project |
| Full-turnkey Enterprise | Dedicated enterprise rollout, governed integrations and delivery | US$300K–900K / project |
| Enterprise managed support | Ongoing enterprise operations and service support | US$3K / month |
| Business Assessment & SOP implementation | Business understanding, policy mapping, approved workflows | Contract scope |
| Professional Ays services | Licensed expert knowledge and professional service experience | Contract / usage-based |
| Compute infrastructure | ||
| Dedicated GPU capacity | Reserved modular GPU capacity for external clients | US$80K–200K / month / contracted client |
| Distributed AI Compute Management | Scheduling, metering, routing and managed third-party capacity | US$10K–50K / month / agreement |
Enterprise and GPU numbers are planning ranges, priced for the actual scope, contract term, GPU configuration, reserved capacity and SLA. The revenue model later uses selected points inside these ranges to show the sales needed for the Year 1 target—not standard customer quotes.
Enterprise and GPU numbers are planning ranges, priced for the actual scope, contract term, GPU configuration, reserved capacity and SLA. The revenue model later uses selected points inside these ranges to show the sales needed for the Year 1 target—not standard customer quotes.
More revenue without charging for the same value twice
Subscription income is supported by institution plans. Consumption income comes from optional memory expansion, creative image credits, voice minutes, AI work credits and approved knowledge or answer capacity. Enterprise customers add configuration, integration and ongoing support. Outside the software business, CNNN can earn GPU capacity and distributed compute management fees.
For Professional Ays Service, the platform earns from the institution’s authorized Hub service, resource usage and agreed enterprise work. The professional and institution arrange their own customer-service fees directly; DualSpan does not automatically take a commission on that underlying professional transaction. This keeps the economics clear and protects confidence in the platform.
From a service assistant to a company-wide AI workforce.
Semi-turnkey gives an enterprise a faster deployment on the shared platform, with agreed workflows, identity and governance settings. Full-turnkey provides a broader engagement that may include dedicated environments, custom integration, policy and SOP mapping, testing and managed support.
From customer request to verified completion
The enterprise value grows through implementation fees, approved integrations, continuing support and expansion. DualSpan’s Policy & SOP Engine provides a reusable path from business assessment to controlled workflows, approvals and audit-ready results.
The employee-facing layer completes the enterprise picture
Institutional Ays represents the organization. Teammate helps an employee work within that organization’s verified role. An Agent Team performs permitted tasks using approved tools. A manager keeps the authority to approve, reject and verify important outcomes. This gives the organization one coordinated workflow instead of several disconnected AI assistants.
AI Business Assessment maps the business problem first. The Policy & SOP Engine then structures approved rules, decision criteria, tasks, human approvals, acceptance checks and an audit trail. Operations uses institution subscription plus an organization-wide AI Work Pool, rather than a fixed charge for every employee’s Teammate identity. Enterprise engagements extend this approach into more complex systems.
Professional Ays Service — give expertise a lasting service presence.
A consultant, teacher, coach, creator or qualified healthcare professional has knowledge people value between formal appointments. Professional Ays Service lets an eligible expert offer approved knowledge and service guidance through an AI Digital Service Twin, managed through the existing Hub platform.
From professional knowledge to an ongoing AI service
Professional Ays Service uses the established Ays and Hub infrastructure rather than requiring a new standalone app or a per-professional network license. The institution or professional pays for the eligible platform services and actual AI resources. Their own end-customer service prices and professional revenue sharing are arranged outside DualSpan.
Own critical capacity. Sell trusted compute services.
CNNN plans to build modular/container GPU infrastructure for customers that need dedicated AI capacity. Customers can contract for reserved compute, deployment, uptime and managed operations. CNNN will size purchases to signed demand, power availability and project-level returns.
The distributed AI compute management platform will manage scheduling, workload routing, usage metering, monitoring and service quality across owned and eligible third-party GPU nodes. This creates another way to serve customers without relying only on hardware ownership.
Capacity is a service before it is a piece of hardware
CNNN can sell reserved GPU capacity, deployment and managed service levels, while the distributed compute platform can schedule and meter workloads across eligible owned and third-party nodes. Customers buy reliable capacity and accountable operations, not just server racks.
Hardware orders follow signed demand, available power, cooling design and a full project return model. This keeps GPU spending connected to actual revenue potential and gives the management team a clear way to decide when to build a new module.
Own the steady workload. Rent the peaks. Measure every GPU-hour.
With dedicated customer agreements and growing demand from DualSpan Bio and DualSpan Beyond, CNNN can create a stable base workload for owned GPUs. The distributed compute management platform can route suitable work to this base and use qualified external capacity for peaks. This gives CNNN more control over unit costs and service quality as volume grows.
Contracted capacity and customer credit guide the first hardware order.
Route steady, supported inference to owned capacity; use cloud and APIs where they offer better economics.
Measure useful GPU-hours, tokens delivered, energy, cooling, support and hardware depreciation.
One infrastructure layer serves external customers and DualSpan.
External customers pay CNNN for compute and managed services. DualSpan Bio and DualSpan Beyond may use group compute when workloads, privacy rules and total costs support that choice. External reserved capacity always comes first under customer contracts.
One shared infrastructure, three economically different uses
External healthcare and enterprise clients provide contracted compute revenue. DualSpan Bio uses eligible compute to support its professional service stack. DualSpan Beyond uses eligible compute for non-medical AI reception, memory, agents and other service workloads. The same management system can measure each stream separately.
CNNN records outside sales as external revenue. Charges between CNNN and wholly owned subsidiaries support internal unit-cost reporting and are eliminated in consolidated financial statements. This allows investors to see the real combined business without counting the same unit of compute twice.
Internal usage is metered to show the real cost of AI software. In CNNN consolidated financial statements, internal compute fees between the parent and wholly owned subsidiaries are eliminated. Investors see external compute revenue and software revenue once, with costs assigned to the right businesses.
Illustrative first-year GPU economics: phased deployment
| Measure | US$M / basis |
|---|---|
| Total modular GPU capital allocation | 4.500 maximum budget |
| Hardware assumed in service in Year 1 | 2.500 initial deployment |
| Remaining reserved capital | 2.000 for demand-led expansion |
| Annual depreciation of initial deployment | 0.833 (36-month planning life) |
| Power, cooling, hosting, network, delivery and support | 0.637 direct-cost allowance |
| GPU and distributed management direct cost | 1.470 total |
| External GPU and managed-service revenue | 3.000 contracted-service target |
| GPU and management gross profit / margin | 1.530 / 51.0% |
This reconciles the GPU cost assumption in the Group’s Year 1 plan. The US$4.5M allocation is a staged capital budget—not an assumption that every asset is operating and depreciating in Year 1. A 36-month accounting life, the actual in-service date, installed specifications, operating expenses, customer contracts and any financing costs will be verified before investment. The 51% segment margin and 69% group margin remain performance targets.
A software platform that can scale. Infrastructure that can earn.
CNNN is building a business with two related sources of growth. DualSpan sells software and enterprise AI services. Modular GPU infrastructure sells dedicated capacity and management to organizations with real computing needs.
The same computing foundation can also support DualSpan Bio and DualSpan Beyond. This creates a clear connection between customer demand, operating assets and AI service delivery.
Why two engines are better than one disconnected portfolio
The AI software engine is designed for recurring accounts, paid resource use and enterprise implementation. The infrastructure engine supports dedicated AI capacity and ongoing managed services. Each has its own customer economics and investment profile, while common model routing, monitoring and deployment know-how can benefit both.
The important advantage is an operating connection: software use helps forecast compute needs, compute cost data helps price AI features, and enterprise contracts can bring both a workflow project and a capacity requirement. The company can then invest based on evidence rather than treating each line as a separate speculative bet.
CNNN owns the core. Two wholly owned subsidiaries focus on customers.
CNNN is the investment target and the planned owner of DualSpan’s core intellectual property. The proposed structure places DualSpan Bio Inc. and DualSpan Beyond Inc. under CNNN as wholly owned operating subsidiaries. They share one AI Core while serving distinct markets.
Target operating structure · DualSpan Bio Inc. and DualSpan Beyond Inc. planned for incorporation.
A clean group structure supports clearer execution
CNNN owns the shared core IP under the approved corporate plan and coordinates capital and computing infrastructure. The proposed wholly owned DualSpan Bio Inc. focuses on healthcare-related customers. The proposed wholly owned DualSpan Beyond Inc. focuses on non-medical institutions and professionals.
This structure assigns clear customer responsibility while preserving the efficiency of one core platform. Group management can track subscription retention, enterprise delivery performance, GPU utilization and returns separately, then review results at CNNN level. The operating subsidiaries will be established and contracted through the appropriate corporate process.
CNNN can supply shared technology and compute services at the group level. Each business has its own customer contracts, costs and performance scorecard. Intercompany compute charges support cost control and are removed in consolidated accounts.
Target a 68–70% blended gross margin as the business scales.
A strong AI business needs more than revenue growth. The plan targets a consolidated gross margin close to 69%, supported by software subscriptions, repeatable enterprise delivery and a mix of contracted compute and asset-light distributed management. The goal is to earn over US$1 million before tax in the first 12 months of full commercial operations.
Year 1 contract values are scenario inputs within the ranges above: five full-turnkey contracts modeled at US$450K each, ten semi-turnkey contracts at US$150K each, and reserved GPU clients at US$125K per month. Real contracts will have their own negotiated scope and price.
| Year 1 sales goals | US$M |
|---|---|
| 700 Dual AI accounts × US$99 × 7 paid months | 0.485 |
| 150 Operations accounts × US$299 × 6 paid months | 0.269 |
| 30,000 individual AI packs × US$9.99 | 0.300 |
| Usage-based and other eligible add-ons | 0.225 |
| 5 full-turnkey projects × US$450K | 2.250 |
| 10 semi-turnkey projects × US$150K | 1.500 |
| 15 managed service contracts × US$3K × 8 months | 0.360 |
| 2 reserved GPU clients × US$125K × 10 months | 2.500 |
| 2 platform management contracts × US$25K × 10 months | 0.500 |
Year 1 revenue mix · USD millions
| Revenue category | Year 1 revenue | Target gross margin | Target direct costs |
|---|---|---|---|
| Subscriptions & add-ons | US$1.279M | 82% | US$0.230M |
| Enterprise rollout & support | US$4.110M | 78% | US$0.900M |
| External GPU & management | US$3.000M | 51% | US$1.470M |
| Consolidated target | US$8.389M | 69.0% | US$2.600M |
Illustrative line margins: 82% subscriptions and packs; 78% enterprise deployments and support; 51% external compute and management. These are operating targets, subject to cost-based validation.
| Year 1 operating target | USD millions | US$M |
|---|---|
| Standard subscriptions & add-ons | 1.279 |
| Enterprise deployment & support | 4.110 |
| External compute & management | 3.000 |
| Total external revenue | 8.389 |
| Direct service costs incl. GPU depreciation | -2.600 |
| Gross profit | 5.789 |
| Blended gross margin | 69.0% |
| Sales, engineering & corporate expenses | -4.550 |
| Financing / other expense | -0.160 |
| Pre-tax earnings | 1.079 |
A 69% blended margin is a management hurdle, not a current achieved margin. Enterprise delivery must stay repeatable; GPU procurement is staged against signed demand, utilization and real depreciation costs. If a project fails this margin test, pricing, scope or capacity deployment must change before scale.
A 69% blended margin is a management hurdle, not a current achieved margin. Enterprise delivery must stay repeatable; GPU procurement is staged against signed demand, utilization and real depreciation costs. If a project fails this margin test, pricing, scope or capacity deployment must change before scale.
Protect the 68–70% blended gross-margin design
Software subscriptions should contribute relatively high gross margins because the same core services can support more institutions. Enterprise delivery benefits from reusable workflow templates and defined change control. GPU leasing has a different cost curve, so each equipment purchase must be supported by contracted capacity, depreciation and operating costs.
Our first-year planning case assumes US$2.5M of GPU equipment in service, with the rest of the US$4.5M compute allocation reserved for demand-led expansion. The scenario combines an approximately 51% GPU and managed-compute segment margin with higher-margin software and enterprise services to reach a 69% blended target. The margin is a management operating hurdle reviewed at contract level.
Target scenario, not historical results. Revenue requires customer contracts, delivered projects and recognized service periods; hardware is sized to contracted capacity.
Growth we can measure — revenue, margin and cash discipline.
The plan starts with contract-led enterprise projects and dedicated GPU services, then expands the share of recurring software and managed services. We target revenue of US$8.39M in Year 1, US$11.60M in Year 2 and US$15.50M in Year 3, with pre-tax earnings growing from US$1.08M to US$2.65M.
Financial targets are translated into sales work
Year 1 revenue is not a single top-down growth percentage. It is built from monthly paying institution accounts, personal and institutional usage packs, a defined number of Full-turnkey and Semi-turnkey projects, managed support, two dedicated compute customers and distributed management contracts.
The team can measure whether the forecast is on track every month: new paying accounts, signed enterprise value, recognized delivery milestones, contracted compute capacity, direct AI costs, collection days and operating cash. That visibility makes the US$1M+ first-year pre-tax target an execution plan rather than a slogan.
Each phase has clear operating gates. More enterprise projects require delivery capacity. GPU expansion requires booked capacity and reliable power. Subscription growth requires real conversion and renewal. These measures turn the forecast into a working management plan.
Put every investment dollar to work.
CNNN seeks US$15M in strategic equity financing. The proposed allocation funds the shared AI Core and product commercialization, modular GPU projects, audit and reporting readiness, market development, and working capital. Milestone-based closings can match investment to measurable delivery.
Capital follows the proof points
The proposed US$15M financing creates a shared technology foundation, funds commercial rollout, strengthens reporting and governance, and supports phased GPU capacity. Product milestones, contract receipts and customer acceptance determine how quickly each budget expands.
The company can pace GPU investment against take-or-pay capacity arrangements and keep software growth capital available for product quality, customer success and international expansion. This balance matters because a profitable infrastructure project should strengthen—not consume—the broader AI software opportunity.
Hardware purchasing follows contract quality and project economics. The alternative capital structure combines US$10M of equity with up to US$5M of separately arranged equipment financing. Equity, loans and contracted revenue are always shown as distinct sources of value.
A clear path from launch to scale and Nasdaq readiness.
The first six months focus on reporting remediation, platform launch readiness, signed enterprise work and the first bankable compute contracts. The next twelve months deepen paid adoption, deliver enterprise implementations and expand reserved compute. By month 24, the plan aims for a stronger recurring revenue base and a more diverse customer network.
Reporting repair, product gates, pilot clients, first compute agreements
Paid subscriptions, first enterprise deliveries, compute deployment
Renewals, channel expansion, more enterprise implementation
Broader recurring revenue, measured infrastructure expansion
A roadmap investors can monitor
The first stage proves the software and customer journey, with current reporting, product acceptance and signed commercial pilots. The middle stage expands paying institutions, governed enterprise workflows, professional services and reserved GPU capacity. The following stage strengthens retention, recurring revenue and multi-market replication.
Capital-markets work runs beside commercial work: current reports, audited financial statements, capitalization, governance and applicable Nasdaq eligibility conditions. The goal is to be ready to initiate the appropriate Nasdaq application process before the end of 2027 as the company meets the relevant requirements.
In parallel, CNNN is targeting the requirements needed to initiate the applicable Nasdaq listing process by December 31, 2027. The workstream covers current financial reporting, audited statements, share structure, governance, capital standards and market readiness.
Chris Chang · Founder, Chairman & CEO
Chris Chang is the Founder, Chairman and Chief Executive Officer of ConneXionONE Corp. He has built his career across international technology ventures, including telecommunications and network services, cloud solutions, digital infrastructure, business development and cross-border corporate transactions.
Chris Chang
Founder · Chairman · Chief Executive Officer · CNNN
His experience combines the work of opening new markets, bringing technology into commercial use, building partnerships and managing investment-related business integration. His provided executive profile records three successful business exits across technology-related ventures.
At CNNN, he brings those disciplines together: shaping DualSpan’s long-term product and commercial strategy, developing strategic relationships, allocating capital across AI software and computing infrastructure, and leading the company’s Nasdaq readiness plan. His focus is turning promising technologies into services customers use, revenue models companies can operate, and businesses that can grow across markets.
Leadership that joins technology to business execution
Chris Chang’s experience spans international telecom and network services, cloud and digital infrastructure, strategic business development and cross-border investment integration. His executive profile also records three successful exits in technology-related ventures. This combination informs an approach that values customer adoption and disciplined capital allocation alongside new technology.
For CNNN, his remit brings together product direction, licensing and IP stewardship, commercial partnerships, investment structuring and the Nasdaq preparation program. The management objective is to turn DualSpan and compute infrastructure into businesses with clear ownership, measurable delivery and a repeatable route to customers.
Help build the next chapter of CNNN.
CNNN invites strategic investors to participate in a US$15M growth financing built around two complementary businesses: DualSpan AI software and modular AI computing services. Healthcare is our first deep market. Existing-platform partnerships accelerate distribution. Enterprise delivery and dedicated compute contracts create revenue opportunities with clear customers and measurable results.
Our ambition is straightforward: grow a profitable AI business, strengthen CNNN’s capital structure and pursue the next stage of Nasdaq readiness. We welcome investors who see value in building real services, real customer relationships and lasting business assets.
ConneXionONE Corp. · US$15M Strategic Financing
Let us build the next stage of AI services together.
Chris@DualSpan.bioAn invitation built around measurable progress
A strategic investment in CNNN supports two connected engines: a shared AI service platform for healthcare and non-medical organizations, and a modular computing business serving real enterprise workloads. The same investment can build operating depth in healthcare while opening a wider market for repeatable software and managed compute.
We invite investors to evaluate the technology, commercial pipeline, planned corporate structure and milestone-based use of capital together. Each signed customer, completed workflow and profitable capacity contract is designed to bring CNNN closer to a stronger operating business and the next stage of its capital-markets plan.