CognIX AI
A live AI assistant experience for professionals who need support while the conversation is still happening.
The product challenge was attention, not output volume: make suggestions, references, and prompts fast enough to scan without pulling the user away from the live moment.
Role
Senior Engineer - built the web frontend and mobile app, shaped the product interface, integrated API workflows, and optimized the real-time user experience.
Domain
AI knowledge assistance
Proof Level
Live public product
Live Product
Live public product
The public site establishes the product category, while the case study explains the attention-sensitive web and mobile surfaces behind the assistant.
Interface Evidence
Conversation support without distraction
Public visual evidence is included for this project. Product details are framed around the interaction model rather than private usage data.
Live Attention
AI support for the moment a conversation is happening.
This page should read as a real-time interface problem: how to offer useful prompts while protecting the user's focus.
Moment of Need
Professionals often need the right reference or next question during a live conversation, not after the moment has passed.
Attention Context
The product sits between note-taking and decision support, so the interface needed to feel calm, fast, and useful during active listening or speaking.
Web and Mobile Ownership
Senior Engineer - built the web frontend and mobile app, shaped the product interface, integrated API workflows, and optimized the real-time user experience.
Assistant Response
I helped shape web and mobile experiences that make AI suggestions quick to scan, context-aware, and easy to ignore when the user needs to stay focused.
Suggestion Model
The product experience is organized around live context, suggestion cards, reference surfacing, and account-based flows across web and mobile.
Assistant Surface
The touchpoints that had to stay fast and calm.
Feature scope is grouped around live context, suggestion review, references, and consistent web/mobile flows.
01Real-time AI suggestion interface
02Context-aware reference and concept surfacing
03Web frontend experience
04Mobile application experience
05Private, secure user-facing product flows
Attention Choices
Restraint as the main product decision.
The tradeoffs favor scan speed, calm hierarchy, and platform consistency over dense AI output.
Design for live attention
The interface needed to support users while they are speaking or listening, so suggestions had to be quick to scan, context-aware, and visually calm.
Tradeoff: This favors focused information hierarchy over dense AI output or overly complex controls.
Share product thinking across web and mobile
Building both the web frontend and mobile app required consistent flows, reusable interaction patterns, and careful API boundaries.
Tradeoff: Platform-specific details still needed to be handled deliberately so each experience felt native enough for its context.
Delivery Notes
What shipped and what still needs signal.
The outcome focuses on delivered product foundations while keeping adoption and performance claims for verified data.
Delivery Evidence
Delivered web and mobile product foundations for a live AI assistant focused on professional conversation support.
AI Interface Lesson
Real-time AI interfaces need restraint. The product has to support attention instead of competing with it.
Next Product Signals
Continue strengthening real-time feedback states, mobile polish, and measurable product outcomes as usage data becomes available.
Related work
Nearby product problems.
Other work with a related domain, workflow, or product category.