Managing inventory across multiple channels is a massive operational headache. AI is the logical tool for syncing data across Shopify, Amazon, and others, but it often brings a sense of unease for merchants. If an automated system makes a mistake, it doesn't just look bad—it threatens account standing and cash flow.

I designed ChatBlu to solve these trust issues. My goal was to build a brand that signaled stability and a product that made complex data management clear and friendly. I led the creative direction and product strategy, designing a web application that bridges the gap between conversational AI and traditional data management. This work was a core part of the vision that helped ChatBlu secure $500M in pre-product funding and numerous new partnerships.

AI has a recognizable look: it’s usually purple, sparkly, and dark. While trendy, it doesn't necessarily say dependable to someone managing a warehouse. I moved away from these trends to focus on the reality of the logistics industry.

Using the name as a starting point, I leaned into the theme of the deep ocean. A world of integrations and data that can be overwhelming without a clear map.

I grounded the brand in a mascot: the crab. Its pincers act as a metaphor for the product’s ability to grip and manage disparate data sources simultaneously. I paired this with a bright blue palette and high-contrast typography to establish immediate credibility with enterprise users.

The ChatBlu platform handles thousands of SKUs across dozens of integrations. Combining the speed of AI with the precision required for data management meant every automated action had to be both visible and reversible.

The primary bottleneck for merchants is data collision. The fear that a price change on Shopify might break an Amazon listing. I solved this by designing a multi-step onboarding process that forces the user to define a source of truth for every field.

Precise Mapping: When users add new channels, they map holistic fields and specific products. The UI surfaces conflicts immediately, offering clear resolution choices rather than making automated guesses.

Managing Density: I built a product table that handles massive data structures without overwhelming the user. By using a layered hierarchy and progressive disclosure, power users can drill into granular API configurations directly within a cell without losing their place in the catalog.

Trusting AI to update a product catalog feels risky. While chat interfaces are great for context, they aren't great at showing the big picture. I needed to show users exactly what was being changed so they could feel confident in the automation.

To solve this, I moved the chat out of a standard bubble and into a side-by-side workspace. The chat acts as an overlay with its own separate contextual canvas to show product edits or supplier orders in real-time.

Side-by-Side Validation: With the chat on the left and a structured form on the right, users can watch the AI perform edits. It makes the invisible work of the backend visible and verifiable.

The Audit Trail: Anytime ChatBlu touches data, the field is marked with a special label. Users can interact with this label to see previous values and alternative AI recommendations right in context. This ensures total reversibility and eliminates the fear of the system making unwanted errors.

The UI is built to be modular. It is simple enough for a beginner launching their first shop on TikTok, but deep enough for an enterprise warehouse manager to configure complex API field mappings. I focused on progressive disclosure—keeping advanced tools out of the way until a power user actually needs them.

I was responsible for the brand architecture, product strategy, and the final UX and UI. The product vision I delivered helped secure $500M in pre-product funding, new partnerships and established a new standard for how AI can be integrated into the supply chain industry. Early users were able to onboard and begin managing catalogs in Q1 2026.