Town: An AI Assistant for Your Health Data

The daily management of health involves more administration than anyone cares to admit. It’s a constant flow of information: lab results arriving in one portal, wearable data in another, practitioner emails in a third, and your own symptom notes in a fourth. The core problem is not a lack of data, but the sheer clerical effort required to connect the dots and act on the signal within the noise.

What It Actually Does

Town is a personal AI assistant, sometimes called a 'Townie', that integrates with your existing digital tools. It securely connects to services like your email, calendar, Slack, and Google Drive to read, synthesize, and draft information on your behalf. It operates on a 'human in the loop' basis—it will prepare a draft, but you must always be the one to approve and send it. For wellness applications, this clerical automation is its primary strength.

• It synthesizes disparate data streams into a single morning brief, pulling together sleep data, calendar appointments, and key practitioner emails.
• It drafts communications based on context from your connected sources, such as an email to a doctor referencing a recent lab result.
• It automates scheduling and follow-ups with practitioners or clients by cross-referencing your calendar and inbox.
• It can answer questions based on a personal corpus of documents you grant it access to, like notes or past reports stored in a specific folder.

How I Use It for Personal Wellness

I connected my Townie to Gmail, Google Calendar, and a single, specific folder in Google Drive I named ‘Health Ledger’. This folder is where I save all my health-related documentation: PDF lab reports, weekly data exports from my Oura ring, and simple text files where I log daily symptoms.

Each morning, the assistant delivers a brief. For example, it will flag an unread email from my functional medicine doctor, note that my HRV was lower than my weekly average (based on the latest Oura export in the Drive folder), and remind me of an upcoming appointment. This saves me from having to manually open three different applications and piece the context together myself.

The drafting capability is where it becomes a true time-saver. When my latest bloodwork arrived as a PDF in my inbox, I gave the instruction: 'Draft an email to Dr. Sharma summarizing the key out-of-range markers from the attached lab report and ask if I should adjust my current supplement protocol.' Town generated a clean, neutral draft that I could review, edit, and send in under a minute.

How Practitioners Can Use It

For a health coach or practitioner, the primary challenge is managing high-volume client communication. A busy functional medicine practice can receive hundreds of emails a day, many of which are complex and require referencing a client's history.

In this setting, a practitioner can set up their Townie to triage the inbox. It can be trained to identify and flag messages containing urgent keywords (e.g., 'adverse reaction', 'severe pain') for immediate human attention. For routine check-ins, the assistant can draft a reply by first pulling context from a dedicated client folder. For example: 'Draft a reply to Jane's email, first reviewing her file for her current protocol and recent lab work.' The practitioner just has to review and approve the draft.

This fits directly into the 3-Layer Method of building an AI health stack. Town automates the 'Ledger' (synthesis of existing data) and 'Protocol' (drafting actions and communications) layers, freeing up the practitioner to focus on high-value 'Research' and clinical reasoning.

• Draft initial responses to new client inquiries based on a set of template documents.
• Summarize a client's entire communication and results history in seconds before a consultation.
• Automate the process of booking follow-up appointments based on calendar availability and pre-set rules.
• Generate end-of-week summaries of common client questions or reported issues for internal team review.

Where It Falls Short

The most significant consideration is privacy. By design, you are granting a third-party AI access to your most sensitive digital spaces. While Town is built with security in mind and emphasizes human approval, the fundamental model of an AI reading your email and private files requires a high degree of trust. This will not be a suitable tool for everyone.

Second, it is an assistant, not an analyst. It is excellent for summarizing and drafting, but it cannot provide novel medical insights or perform diagnosis. It will tell you that a lab marker is high because the report says so, but it won't explain the potential biochemical reasons. That is, and must remain, the work of a qualified clinician.

Finally, the pricing may be a barrier for individuals who are not managing a significant complexity of data or communications. Its value is directly proportional to the amount of administrative work it can offset.

The Point

A tool like Town doesn't replace your judgment or your practitioner's expertise. Its value is purely in clerical automation. It buys back the hours spent on the administrative tasks of health management—the summarizing, the drafting, the scheduling. This frees up your cognitive bandwidth for the actual work: interpreting data with a professional, making decisions, and living your life. It earns its place by making the management of your health stack less of a job.