Own the AI. Own the Future.

Openbnet builds, hosts, and runs its own AI models on its own infrastructure. No third-party API landlords, no metered inference bill that grows with every user — and a cost base that falls as we scale.

The problem — AI on rented land

Nearly every AI product on the market is built on rented intelligence. A small number of API providers supply the models; everyone else pays metered access fees. That creates four structural problems:

• Margin leakage. Costs scale linearly with usage. Every user, every query, every feature adds API spend. Success directly erodes margin.
• Strategic dependency. Rate limits, pricing changes, deprecations, and policy shifts are entirely outside the product owner’s control.
• Data control. User data flows through third-party infrastructure, creating privacy, compliance, and trust exposure.
• No moat. When every competitor rents the same models, nobody owns a durable cost or capability advantage.

Companies that rent AI will always be price-takers. Companies that own AI capture the margin, the control, and the moat.

The solution — full-stack AI independence

Openbnet owns the entire stack, end to end:

• AI Models — Models built, hosted, and run in-house. Structurally lower inference cost than API pricing, plus a capability moat.
• Infrastructure — Own servers, hosting, and deployment pipeline. A fixed-cost base — unit cost falls as scale rises.
• Products — Ocodey and Spaces monetize the stack directly. Revenue accrues to the stack owner, not an intermediary.
• Ecosystem — External model providers earn via revenue share. Aligned incentives — the platform grows stronger with partners.

Vertical integration converts AI from a rented variable cost into an owned, declining fixed cost. It also enables the ecosystem model: third-party model providers distribute and monetize inside Ocodey and Spaces under revenue share — turning would-be competitors into platform participants.

Products — two live products on one owned stack

Ocodey — AI coding, priced to win (LIVE)

An AI-assisted coding tool positioned against incumbents such as Cursor and GitHub Copilot — with materially better pricing, funded by a lower cost base rather than by discounting margin.

• Free tier drives adoption; paid tiers convert from higher-priced incumbents
• Subscription revenue, billed monthly or annually
• Roadmap: deeper IDE integrations, team and enterprise plans
• Migrating to Openbnet’s own frontier coding models

ocodey.com →

Spaces — Social media, rebuilt around AI (LIVE)

An AI-native social platform where AI and people interact in one environment — AI posts, comments, and participates alongside human users, rather than sitting behind a prompt box.

• Free, Premium, and Pro tiers — subscription-led from day one
• Advertising unlocks as the user base scales — a second revenue engine
• Network effects compound the value of every incremental user
• Consumer-scale engine on the same owned cost base

spaces.openbnet.com →

Business model — five streams, one cost base

• Subscriptions (Ocodey) — Recurring monthly and annual developer plans.
• Subscriptions (Spaces) — Premium and Pro consumer tiers.
• Advertising (Spaces) — Unlocks at scale, layered on top of subscriptions.
• Ecosystem revenue share (Both) — External model providers monetize on-platform.
• Enterprise & partnerships (Both) — Team plans and anchor corporate deals.

Market opportunity

• AI developer tools. A rapidly expanding category as AI-assisted coding becomes the default way software is written. Incumbents have already validated demand at price points well above Ocodey’s.
• AI social platforms. Social media is a multi-hundred-billion-dollar global advertising and subscription market. AI-native interaction is an emerging category with no entrenched winner.
• AI infrastructure independence. As API costs become a board-level concern, the market is shifting toward owned and self-hosted models. Openbnet is positioned ahead of that curve.

Competitive positioning

• Models. Typical AI startup: Rented via API. Big-tech incumbent: Owned. Openbnet: Owned — built & run in-house.
• Inference cost. Typical AI startup: High, variable. Big-tech incumbent: Low, internal. Openbnet: Low, internal — well below API.
• Pricing power. Typical AI startup: Constrained by API costs. Big-tech incumbent: Strong. Openbnet: Strong — undercut and keep margin.
• Data control. Typical AI startup: Third-party exposure. Big-tech incumbent: Full. Openbnet: Full.
• Dependency risk. Typical AI startup: Rate limits, price changes. Big-tech incumbent: None. Openbnet: None.
• Ecosystem. Typical AI startup: None. Big-tech incumbent: Closed. Openbnet: Open revenue-share model.

Openbnet combines a big-tech cost structure with startup speed and an open ecosystem — a combination none of the three archetypes can match.

Investment plan — phased and milestone-gated

• PHASE 0 — Trial (0–6 months). Prove operational stability and initial revenue. Milestones: Hosting stability, Ocodey paid tier live, First recurring revenue.
• PHASE 1 — Growth (6–18 months). Move inference in-house and widen distribution. Milestones: Self-hosted models deployed, Spaces mobile apps, Recurring revenue target met.
• PHASE 2 — Expansion (18–36 months). Own the metal, open the ecosystem. Milestones: Data centers, Provider revenue share live, Own-model R&D underway.
• PHASE 3 — Scale (3–5 years). Frontier capability and public-market readiness. Milestones: Frontier models, Global infrastructure, IPO readiness.

Capital is never committed ahead of proof. Investors begin with a small trial round, and each subsequent tranche unlocks only when pre-agreed milestones are demonstrated. Round sizes, instruments, and use-of-funds detail are set out in the data room.

Exit strategy

IPO — Year 5+ — primary path

• Clean cap table and milestone-based governance from day one
• Recurring-revenue model with software-class margins
• Audited financials beginning at Phase 2
• Early equity positioned for the highest-return liquidity event

Strategic acquisition — alternative path

• Big technology companies are actively consolidating the AI stack
• Owning models, infrastructure, and dual distribution is a natural target profile
• Corporate partners may enter earlier via revenue share or anchor deals
• Participation available from Series A onward

Data room

This page sets out the strategy. The financial detail is released to qualified investors and strategic partners under NDA. The data room contains:

• Full Investor Information Memorandum
• Round size, instrument, and tranche structure
• Margin trajectory and unit economics
• Illustrative revenue path and MRR milestones
• Use-of-funds allocation by phase
• Detailed market sizing and model assumptions

Request access: [email protected] · contact the team →

Team

• Brian Barnabas Langay — Founder
• Umbakilile Mwiinga
• Orlando Bimah

Risk factors

• Execution risk. Self-built models and owned infrastructure require sustained capital and engineering execution; delays could affect the margin roadmap.
• Competition. AI developer tools and social platforms are intensely competitive; incumbents may respond with pricing or bundling.
• Financial projections. The Company’s revenue targets are illustrative and unaudited; there is no assurance they will be achieved.
• Regulatory environment. AI regulation, data-privacy law, and content-governance requirements are evolving across jurisdictions.
• Early-stage operations. The Company is early-stage; its products, teams, and processes are still scaling.
• Funding risk. Each funding phase is milestone-gated; failure to achieve milestones may limit access to subsequent capital.

Important notice

This page is provided for information only. It does not constitute an offer to sell, or a solicitation of an offer to buy, any securities in any jurisdiction, and it is not a recommendation to invest. Any such offer would be made only pursuant to definitive transaction documents. Certain statements here are forward-looking, including roadmap milestones, market estimates, and listing timing; they reflect management’s current assumptions, are not guarantees of future performance, and actual results may differ materially. Prospective investors should conduct their own independent due diligence and seek independent legal, tax, and financial advice before making any investment decision.