Design innovations
beyond standard LLM structure.
Many, timestamped before public release.
Major LLM companies still cannot
understand their users with depth.
The industry has invested heavily in three approaches to closing the user-understanding gap. Each is bounded by an inherent ceiling.
In-session inference
Reasoning about the user from conversation context. Useful, but shallow — and the state resets when the session ends.
Opt-in memory
Storing user-stated facts across sessions. Better, but most users do not volunteer information at depth unprompted.
Behavioral & third-party signals
Inferring from usage, or buying from data providers. Limited by ambiguity and by tightening regulation around consent.
Do not infer. Ask. And reward.
Inference-based
MZN consent-first
Six sources. Six methods.
Each feeds the others.
Pulino — Identity & Connection
Users answer personal questions (occupation, income range, vehicle, housing, interests, lifestyle) as a prerequisite to earning income on the platform. The system identifies relevant business categories: a painter is matched to paint retailers, tool shops, contractors. Connected via Follow campaigns and Board. Each explicit answer establishes structured user context that inference-based approaches cannot reach. 168K users.
Board — Comprehension Validation
A business creates a campaign → the system shows it only to matching Pulino profiles → the user sees the product, answers 4 questions in 20 seconds → correct answers earn a reward. The system validates comprehension, not just exposure. High accuracy on professional-paint questions indicates genuine domain expertise; low accuracy indicates the user is not the audience for that category.
Radar — Purchase Funnel
“I need white construction paint” → nearby sellers respond → in-person purchase → cashback. A complete funnel: Intent + Discovery + Transaction. Search engines have intent. Social platforms have social signals. Major platforms do not combine all three at the unit-economics level this design targets.
Taste Analyzer & Style Finder
Three sources combined: natural behavior, explicit preference selection in Style Finder (entertaining for the user, deep signal for the system), and Board response patterns. Taste ≠ interest. “I like sports” is an interest. “I prefer minimal clothing aesthetics” is taste. Progressive. Cross-context. No comparable consent-based equivalent exists at this depth.
Analytics — Invisible Layer
Asks nothing directly. All modules feed into it; a unified profile is produced. Output: “User #9343: male, 35, painter, Peugeot, renter, follows football, sporty taste, high accuracy in tools category, follows brand X, Radar 3x/week.” Anonymous — the business sees characteristics, never name or phone number.
Zoyan — Companion Intelligence
Smart ring. 24/7. 4 personalities. Voice-first. Designed before ChatGPT entered the public conversation. After accumulated trust through Mazzaneh: wakes the user, suggests outfits, manages schedule, takes meeting minutes, records doctor advice, finds nearby items via Radar, processes payment via Pulino. Users share life events naturally: “looking for a new job,” “expecting a baby.” The ring provides physical presence no chatbot interface matches. Passive data: location, activity, health — with consent.
Data positioning compared.
This is not an exhaustive list of what each provider does. It is a positioning view of where consent-first attribute data sits in the broader landscape.
| Data type | Search providers | Social platforms | LLM providers | Mobile platforms | MZN |
|---|---|---|---|---|---|
| Verified occupation (consent) | Inferred | Self-reported | In-session inference | Not collected | Pulino — explicit |
| Income range (consent) | Inferred | Inferred | Not present | Not present | Pulino — explicit |
| Taste profile (consent) | Behavioral inference | Behavioral inference | Limited | Limited | Taste — progressive |
| Verified comprehension | Click signals only | Click signals only | Not collected | Not collected | Board — validated |
| Complete purchase funnel | Intent only | Social signals only | Not present | Not present | Radar — full funnel |
| User compensation | No | No | No | No | Direct payment |
| GDPR posture | Retrofit | Retrofit | Evolving | Evolving | Native by design |
← Scroll to compare across providers →
Beyond standard LLM structure.
Each design grounded in the 9-layer LLM diagnostic map (L0–L8).
Patterns timestamped
before public release.
Over the past several years, multiple design principles documented and timestamped in the HUAI portfolio have subsequently surfaced, in similar form, in capabilities released across the industry. Independent and convergent emergence of similar solutions is a recognized pattern in technology — and is among the strongest possible validations that a problem was correctly identified and that the solution space contains natural attractors.
What this confirms:
All design records are blockchain-timestamped through standard verification methods. They exist as a record of independent priority. Detailed evidence is held under NDA and is shared only in formal partnership conversations.
Why disassembly does
not produce equivalent value.
Interlocked modules
Pulino without Board produces profiles without validation. Board without Pulino produces ads without targeting. Both without Radar produces knowledge without purchase intent. All without Taste produces identity without preference depth. Everything without Zoyan produces data without delivery. Each module has value only in the context of the others. Replicating one module in isolation produces a fraction of the system value.
Hidden mechanics
A significant portion of effectiveness comes from undisclosed analysis methods. The Pulino question sequence is intentional. Board-to-Pulino feedback logic is proprietary. The conversion of advertising response patterns into profile refinement, without asking additional personal questions, is independently defensible IP. Less than half of the architecture is publicly disclosed. Approximately 40% remains offline.
“Like a puzzle where every piece interlocks. Build them out of sequence and the picture is completely different. The complete blueprint exists in only one mind.”