An assistant that asks the right thing, in the right place.
HAI is the core of Everythink — a 3D conversational engine that runs typed-question workflows with the semantics of your business, carrying context across network, community and room. In production since 2016. Not a generic chatbot, and not a 2026 promise.
What the engine looks like in your branded app.
A 3D assistant asks typed questions, a no-code directed graph routes every answer, and a panel turns the conversation into a structured context layer. The typed-question engine and the conditional graph are in production; the avatar layer is Partial — shown honestly below.
Illustrative — real product screenshots coming soon.
Not a chatbot. A workflow of typed questions with your business meaning.
Most assistants generate free text and hope it lands. HAI runs a directed graph of typed questions — text, audio, video, image, geolocation, selection and more — so every answer is captured with structure, and the context follows the topology of your organization.
- ✓Attention — an assistant that asks the right thing, in the right place, instead of guessing from an open prompt.
- ✓Interest — 13 typed question types carry the semantics of your business, with context memory across network, community and room.
- ✓Desire — every conversation builds a context layer your other modules consume: matchmaking connects, the panel reports, the right promotion reaches the right person.
- ✓Action — book a session and put a real workflow in front of your own use case. ✅
The work it does, the pain it kills, the gain you keep.
HAI was built to attend, qualify and structure — not to improvise. Here is the value canvas, with each gain backed by the engine, not by adjectives.
Jobs
Attend 24/7. Qualify leads. Capture structured data. Guide processes end to end — onboarding, claims, certifications — without dropping the thread.
Pains
Chatbots that hallucinate or never learned the business. Forms nobody completes. Unstructured data that’s impossible to exploit downstream.
Gains
Pre-loaded answers with the correct semantics mean zero hallucination in critical flows. Data is born structured. Formalized academically in paper P-A1, with theorems for routing and context reconstructibility.
See paper P-A1 →Honest by design
“Zero hallucination” here is a precise claim about critical flows: in those flows the assistant elicits pre-defined, typed answers rather than generating free text, so there is nothing to hallucinate. The routing and context-reconstructibility guarantees are written down as theorems in P-A1 — not assumed. We label every capability below with its real state, and we never upgrade a Partial item to Production.
Feature, advantage, benefit — with the real honesty state.
Two capabilities are in production today. The 3D avatars row is Partial — we show it as Partial, exactly as it is.
| Feature | Advantage | Benefit |
|---|---|---|
| 13 typed question types ✅ | Multimodal elicitation | You capture voice, photo and location — not only text |
| Workflow as a directed graph ✅ | Conditional flows with no code | The assistant adapts to every answer it receives |
| 3D avatars with localized voice ⚠️ | Identity and closeness | Your brand speaks in the tone of your audience |
3D avatars: Partial today
Localized-voice 3D avatars are Partial — the avatar and voice layer is real but not yet production-complete across every channel and language. The typed-question engine and the conditional graph underneath it are fully in production. We’ll show you exactly where the avatar layer stands in a live session, rather than overstate it here.
Context that travels across network, community and room.
A conversation isn’t a flat queue. HAI keeps a context layer at each level of the topology, so the next module always inherits what the assistant already learned.
- ✓Network context ✅ — your brand and your identity span every location; a returning person is recognized across the whole network.
- ✓Community context ✅ — each branch is a real polygon on the map with its own panel; the assistant routes to the right team before it answers.
- ✓Room context ✅ — orders, claims and certifications each land in their functional area, with the conversation that produced them attached.
- ✓Context memory, formalized ✅ — routing and reconstructibility of context are proven, not promised, in paper P-A1.
Feeds Matchmaking
Every structured answer becomes a signal. The matchmaking engine turns those signals into the right connections inside your community — same context layer, different module.
Matchmaking →
Runs on your Whitelabel Network
HAI ships inside your own branded app — web, iOS and Android — with one identity across every branch. The engine is the assistant; the network is the body it lives in.
Whitelabel Network →Try the engine on your own use case.
One session: a real typed-question workflow, the network → community → room context model, and an honest look at where the 3D avatar layer stands today.