AI Integration Project
Case Study . 2025
Building a Living, Sci-Fi-Native AI Layer for the Web
01
What’s Ultron Orbit?
and it’s vision?
An always on, multimodal AI layer that turns any website into a living, responsive intelligence.
In active development rolling out first on DISEEC’s own website, followed by an open-source release.
Replace static search bars and rigid navigation with an AI “web keeper” thatsees, listens, understands, and actsacross the entire browsing experience.
Legacy websites were built aroundforms, filters, and a sad little search box. You type. You hope. You dig through pages. You maybe give up.
ULTRON Orbit asks a different question:
What if the website behaved like an AI copilot? Listening, watching, and ready to act before you even ask?
— It understands your intent, not just your keywords.
— It understands your intent, not just your keywords.
— It understands your intent, not just your keywords.
— It understands your intent, not just your keywords.
02
Search Bars Are Dead
Traditional web UX assumes users can perfectly describe their needs in a search and will sift through pages to find it.
Terrible at nuance: “I want something like this shoe my friend is wearing…”
Doesn't understand frustration, confusion, intent.
So....
We wanted something radically different: A layer thatlearns, adapts, and collaborateswith the user in real time.
Then...
We wanted something radically different: A layer thatlearns, adapts, and collaborateswith the user in real time.
tadaaa
We wanted something radically different: A layer thatlearns, adapts, and collaborateswith the user in real time.
In reality, traditional search is clunky and misses the subtle cues that reveal a user's true intent. We envisioned a dynamic layer that evolves with the user, offering real-time assistance.
03
The Concept: ULTRON Orbit — The Website’s AI Keeper
It observes user interactions, tracking cursor movements, scroll behavior, and dwell time to identify patterns like hesitation over CTAs, loops between pages, and abandoned forms. These micro-patterns provide valuable insights into user behavior.
It understands user intent by recognizing when someone is lost, curious, or stuck. By inferring goals such as shopping, researching, comparing, briefing, or exploring, it gains a deeper understanding of user needs.
It acts by adjusting UI elements, simplifying navigation, and highlighting next steps. It proactively offers help, suggestions, summaries, and shortcuts. It can also run actions like filling forms, searching the site, preparing carts, and triggering workflows.
Find me this shoe
I see you’re stuck
It already know why I’m here
It observes user interactions, tracking cursor movements, scroll behavior, and dwell time to identify patterns like hesitation over CTAs, loops between pages, and abandoned forms. These micro-patterns provide valuable insights into user behavior.
It understands user intent by recognizing when someone is lost, curious, or stuck. By inferring goals such as shopping, researching, comparing, briefing, or exploring, it gains a deeper understanding of user needs.
It acts by adjusting UI elements, simplifying navigation, and highlighting next steps. It proactively offers help, suggestions, summaries, and shortcuts. It can also run actions like filling forms, searching the site, preparing carts, and triggering workflows.
Seach
Assistance
Consulting
Summarizing
Prediction
Guidance
Navigation
Analyzing
04
Build Phases
Phase 1 — Internal R&D
completed
During R&D, the team explored "true AI for the web," mapping functions like search, assistance, and analysis. Prototypes included frustration detection, product similarity, and conversational navigation.
active
Phase 2 — DISEEC Website Integration
In Progress
During R&D, the team explored "true AI for the web," mapping functions like search, assistance, and analysis. Prototypes included frustration detection, product similarity, and conversational navigation.
Phase 3 — Open Source & Browser-Level Experiments
planned
During R&D, the team explored "true AI for the web," mapping functions like search, assistance, and analysis. Prototypes included frustration detection, product similarity, and conversational navigation.
Because we bring more than visuals. We bring outcomes.
