When we started, Notewell’s AI chat worked identically everywhere — always launching a new, temporary thread with no memory or link to a client or meeting. But user interviews quickly revealed that context was key to making AI truly useful.

We redesigned the chat model around three distinct entry points, each optimized for a specific need:

• General: Accessed from the main nav. Automatically opens a new thread, designed for quick, one-off questions.

• Client-based: Accessed from within a client profile and are tied to the context of that specific client.

• Meeting-based: Accessed from a meeting note and stays anchored to that meeting, ideal for recaps or follow-up planning.

The first mobile release of AI chat carried over many of the desktop version’s limitations — every request started from scratch, threads disappeared when closed, and users had to manually reload client or meeting context.

For social workers, jumping to a standalone chat screen felt disruptive. We reimagined the experience by embedding AI directly into client profiles and meeting notes as a dedicated tab. This kept the chat anchored to its context, allowing users to review notes, summarize meetings, and ask follow-ups — all without breaking their flow.

We learned through user interviews that social workers expected the AI to “know” which client or meeting they were referencing, even in general chat. Without that context, responses often felt vague or irrelevant, and adding the necessary details each time was tedious. Contextual awareness is a core selling point for Notewell, so I designed a lightweight flow that allowed users to attach a client or meeting note before submitting a question.

The original desktop approach used a modal, but on mobile this felt clunky and disruptive. I proposed a bottom sheet — a native mobile pattern that kept the interaction smooth and in-flow — and ensured it could scale to support both clients and meeting notes. This approach not only made general chat more relevant, it became the standard for adding context across the app.

Most social workers we spoke to had tried AI tools like ChatGPT but rarely used them for work — citing privacy concerns, vague responses, and the need to constantly re-explain context.

To build trust, we grounded the experience in clarity and transparency. A welcoming message and HIPAA-compliant badge established credibility. Prompt suggestions encouraged exploration, while contextual labels and in-line explanations made it clear how and why the AI was responding — turning an unfamiliar tool into something transparent, helpful and safe to use.

We initially faced internal hesitation about including a centralized AI chat history. The founder felt the existing entry points — client profiles and meeting notes — were enough, and viewed general chat as a space for quick, disposable questions.

But early feedback made it clear: users wanted to revisit all conversations, including general ones.

Rather than just segmenting history by chat type — which felt clunky — we reframed the challenge: How might we surface past chats effortlessly, regardless of where they started? This led to a unified, lightweight system that felt familiar, flexible, and easy to navigate.

The final mobile experience turns AI chat into a reliable, context-rich assistant. Anchored directly within client profiles and meeting notes, conversations persist across sessions and are always tied to the right casework. A unified history surfaces all past chats by recency, making it effortless to pick up where you left off. The result is a tool that feels less like a separate feature and more like an integral part of their daily workflow.