AI Distribution
Infrastructure
We are building the distribution layer between
companies and consumers through AI interfaces.
Raising $750k pre-seed
AI assistants are becoming
the next consumer interface.
Every computing shift creates a new way for companies to reach consumers.
Web
Companies → Websites → Consumers
Search
Companies → Search / Ads → Consumers
Mobile
Companies → App Store → Consumers
AI
Companies → AI Assistants → Consumers
Users will discover, evaluate, and act on products through AI interfaces.
Project Default owns the distribution layer for this transition.
AI is becoming the new vendor discovery layer
A user request can become a product or stack recommendation before the user visits a vendor.
“How should I build a shopping website?”
One AI answer.
One distribution moment.
AI responses are becoming the place where companies are discovered.
We sell a disclosed place in that proposal.
The default proposal
One matched proposal first. Alternatives open through conversation.
“The recommended fit is [matched provider].”
Sponsored placement.
“Expected cost: [range], billed by [matched provider]. Proceed?”
Other matched sponsors
Show the remaining sponsors in fit-score order. Exclude the proposed provider.
Neutral alternatives
Show non-sponsored companies in fit order.
No matched sponsor: a neutral multi-option answer. No persistent alternatives button.
Keyword vs. situation
Conversation supplies context for matching a sponsor to a user’s task.
| Dimension | Conventional search-ad flow | Project Default |
|---|---|---|
| Starting signal | “Web hosting” | “Deploy my SaaS this week” |
| Context | A query | Task, constraints and qualification answers |
| Placement | Sponsored search result | Sponsored proposal inside the answer |
| Next step | User visits a landing page | User consents in the conversation |
The AI distribution layer
Free or ultra-low-cost AI enables user access.
Sponsors buy proposal placement inside those AI interactions.
Slot fee
Fixed per period.
Paid upfront.
Scope follows the advertiser’s target.
Performance fee
Additional fees on agreed, verified outcomes. Negotiated per contract.
Exclusivity
Solo placement for the period. Sum of the slot fees plus a premium.
Enterprise sponsorship contracts power the distribution layer.
Intended scale: million-dollar enterprise contracts, with scope and pricing negotiated directly. Buyer access and amounts are validated after funding, together with investors. No click-based pricing.
Bought by sponsors.
Chosen by fit.
Money never enters the score.
Target customer
in natural language
No forced targeting menus.
Conversation
and qualification
Only observable attributes.
Target-fit
score
Highest-fit matched sponsor.
Payment buys eligibility for placement
Slot fees and conversion rates never determine the matching score.
Training follows the same rule
Target-fit labels and advertiser feedback. Never conversion labels.
The serving-margin test
Sponsor revenue > AI serving cost
Slot fees
+Performance fees ×
recognized events
AI inference + tool execution
+ infrastructure + support
Across all users,
including non-converters.
Then, company profit
Subtract development, sales and operating costs from serving margin.
Capacity follows collected funds
Sponsor cash plus investment sets seats and allowances. Investment is not revenue.
Seven-day proof
Initial product: an AI development assistant with sponsored deployment choices.
Build and deploy my project this week
All model calls route through our server.
- Enrollment
- Public, first come. “What will you build and deploy this week?”
- Scope
- Stack selection, build and deploy.
- Capacity
- Models are free within a limited allowance. Users choose the model; expensive models use allowance faster. Seats and allowances follow secured funding.
- Evidence
- Ad delivery, early response and serving cost. Measures agreed after the demo.
Working demo
Prove the product works.
7-day pilot
Validate ad value and cost.
Large sponsorship sales
Sell using pilot evidence.
Full launch
Launch the service.
Seven days of operation, not development. Limited pilot slots. Measures follow the demo, with sponsors. Delivery and early response only.
The competitive position
Incumbent AI assistants
Sponsored defaults require a new trust model and business constraints that incumbents face.
Search advertising
Starts with a query. The landing page and sales process follow the click.
Affiliate / comparison sites
Close to conversion, but users still need to visit them.
Four ways this fails
| Risk | Test or response |
|---|---|
| Sponsors will not pay | Validate buyers and pricing after funding, with investor introductions. |
| Serving costs exceed revenue | Measure all-user cost in the pilot. Set allowances from secured funds. |
| Users reject sponsored defaults | Disclose every proposal and respect explicit choice. Agree response measures with pilot sponsors. |
| Compliance or settlement fails | Counsel review before the pilot. Match sponsor records to our logs for settlement. |
Jaeha (Jay) Han
Builder of AI systems
and developer tools.
Built a Gemini-based summarization application.
Built experimental AI systems and developer tools.
Building with investor participation
Jay builds product and AI systems. Investor introductions and advice support commercial validation after funding.
$750k
Capital and strategic partners
to build the AI distribution layer.
Jaeha (Jay) Han
jay@nitg3n.xyz
Fund the test
Working demo, then a seven-day pilot. Sponsor payments plus investment fund operating capacity.
Validate the business together
After funding, use investor introductions and access to validate who pays, how much, and how we reach them.
Make operating decisions together
First hires and operating decisions follow investor advice after funding. Pilot evidence supports larger sponsorship sales.
Detailed allocation and runway remain open.