HeyContext

HeyContext

HeyContext

Role

Product Manager / UX Team Lead

Timeline

August 2025 - December 2025

Team

1 Product Manager, 2 UX Designers, 3 UX Researchers, 3 Software Engineers

Background

Business

PersistOS is a silicon valley startup with a unique approach to AI memory and personalization. They have award winning technology, but right now their product’s value is shrouded by a lackluster user experience.

Product

HeyContext is a multi-agent orchestration workspace where groups of specialized agents coordinate autonomously.

Current HeyContext Experience

Objective

Redesign the HeyContext platform to better communicate and deliver users the value that the PersistOS’s unique technological approach offers. A desired outcome would look like an increase in engagement metrics.

Site Traffic

Account Sign-ups

User Retention

Research

Platform Software

Starting with the foundation of the service, I worked with the engineers to map out the system architecture.

Thanks to enthusiastic explanations from the engineers, I was able to gain an understanding of the software’s functions. The platform contained several novel solutions that will help set us apart from competitors.

Features

Convergence

Agents are able to learn from previous experiences. Top performers communicate with other agents to share what's working.

Darwin

For tool usage, model selection and prompts are optimized through survival of the fittest.

Vector Native

Agent-to-Agent language that reduces unnecessary token usage by simplifying communication without damaging meaning.

Clustering

Utilizing vector embeddings, files and conversations are mapped in a web. Related information is connected for agents to use.

Temperament

Preferences and characteristics of a user are recognized through interaction, allowing models to interpret input and tailor responses for highest value.

Convergence

Agents are able to learn from previous experiences. Top performers communicate with other agents to share what's working.

Vector Native

Agent-to-Agent language that reduces unnecessary token usage by simplifying communication without damaging meaning.

Darwin

For tool usage, model selection and prompts are optimized through survival of the fittest.

Temperament

Preferences and characteristics of a user are recognized through interaction, allowing models to interpret input and tailor responses for highest value.

Clustering

Utilizing vector embeddings, files and conversations are mapped in a web. Related information is connected for agents to use.

Convergence

Agents are able to learn from previous experiences. Top performers communicate with other agents to share what's working.

Temperament

Preferences and characteristics of a user are recognized through interaction, allowing models to interpret input and tailor responses for highest value.

Clustering

Utilizing vector embeddings, files and conversations are mapped in a web. Related information is connected for agents to use.

Vector Native

Agent-to-Agent language that reduces unnecessary token usage by simplifying communication without damaging meaning.

Darwin

For tool usage, model selection and prompts are optimized through survival of the fittest.

Assessing Market Landscape

Focusing on the memory, project, and AI workspace attributes central to the HeyContext product, the research team identified competitors.

What Users Are Saying

The research team sought to understand popular sentiment surrounding competing services, as well as the specific focuses of HeyContext. This was done by looking at online posts and conducting interviews. Once a significant number of perspectives were coalesced, they were sorted into an affinity map.

"When I initiate a new conversation or switch between different models, I often find myself re-explaining the same background details, objectives, or context repeatedly"

"Every interaction resets to square one. You have to reiterate your technology stack, redefine your preferences, and reintroduce context that should already be evident."

"After spending hours troubleshooting a problem, you return the next day to find a generic greeting, as if we hadn't just spent an entire day discussing intricate details."

Current AI Users

Design

Defining Core Features

How can we address market demand while balancing the realistic scope of our situation? Our goal is to provide a unique value proposition and target a subsection of the AI workspace market. We're aiming to reduce the barrier for getting maximum value from AI tools. We’ll achieve this by increasing output quality and decreasing input effort.

Must Haves

To achieve our goal, the team defined the most important features our service must have. These were decided on based on the structure of competitors' services and from speaking to users directly.

Platform Integrations

Work done on other platforms is able to be seen by HeyContext. Wasting time uploading files or providing context is unnecessary.

Proactive Intervention

Utilizing integrations, agents can recognize potential tasks. With personal profiles and the ability to pull from files, completing tasks can as easy as a single button press.

Trust Through Visibility

Agent activity is transparent and communicated to the user throughout each step of tasks. Token usage is always available.

Intent Validation

Ambiguity will be removed from tasks by asking users questions after their prompts if needed. If any unanswerable questions arise during complex task completion, users will be requested to give input.

Collaborative Capability

Projects will be able to have multiple users. Taking advantage of our contextual file system, teams would not have to upload the same document multiple times individually. A single connection of a google drive folder or file upload would suffice.

Platform Integrations

Work done on other platforms is able to be seen by HeyContext. Wasting time uploading files or providing context is unnecessary.

Trust Through Visibility

Agent activity is transparent and communicated to the user throughout each step of tasks. Token usage is always available.

Collaborative Capability

Projects will be able to have multiple users. Taking advantage of our contextual file system, teams would not have to upload the same document multiple times individually. A single connection of a google drive folder or file upload would suffice.

Proactive Intervention

Utilizing integrations, agents can recognize potential tasks. With personal profiles and the ability to pull from files, completing tasks can as easy as a single button press.

Intent Validation

Ambiguity will be removed from tasks by asking users questions after their prompts if needed. If any unanswerable questions arise during complex task completion, users will be requested to give input.

Structure

The first step to achieving our goals was organizing the platform to incorporate the primary advantages of our service into the core product flow. With this in mind, the team laid out the site map.

In updated structure, I pushed the flow that mattered most to users, the chat interface, to be the central focus of the homepage experience. This also aligned ourselves with standards across the industry, allowing for new users to intuitively interact with the service.

Old Chat Flow

New Chat Flow

Interface

With each page’s details set in place, the design team moved onto the visual presentation. Building off of the previously established branding, the design team began assessing potential visual direction.

Quick Idea 1

Quick Idea 2

Design System

Consolidating our visions, the design team established a foundational design system. The system was paired with full documentation describing the use of each element for AI development tools once handed off to the engineers.

Low Fidelity Mockups

With the system in place, it was time to begin piecing together the experience's visual layout. I started this process with low-fidelity mockups.

The most notable change that was made in this rendition was cutting the constellation screen, which had served as a visual representation of file clustering. The screen had become unconsciously accepted part of the product, but as we progressed, it was now conflicting with the service's goals.

Mid Fidelity Prototype

With the basic layout in place, the design team quickly constructed a mid fidelity prototype, expanding on the previous screens and fleshing out the location of features.

With the new structure and visual identity developed, the prototype was handed off with assistive documentation for developers to use as a base of the update.

Outcome

Launching Update

After the engineering team pushed the update live, we gave a brief period for users to interact with the product. Then we began measuring how well we had addressed our objectives and where we could improve the product in the future.

We performed 14 usability tests with the revised product, and ran a community survey deployed through email to those who created accounts on the platform.

Results

41% Increase

In User Satisfaction

1k Users

First 2 Weeks

60k Visitors

First 2 Weeks

User Quotes

"I'm so happy that I don't have to describe what I am working on again and again."

"It's like a thinking companion that organizes not just tasks, but meaning. It feels almost like my proxy, and it understands who I am."

Reflection

This project pushed me across more dimensions than any previous experience. Collaborating closely with engineers, leading a team through the full UX process, and navigating the fast pace of an AI startup was demanding in the best way.

Understanding the technical constraints shaping our available decisions and positioning ourselves in the market was rewarding and enjoyable. I’m grateful to the amazing teammates I worked on this product with, and can’t wait to apply what I’ve learned in the future.