Use Google Docs with an AI Browser for Support Triage

If you use Google Docs and you regularly need to triage and respond to support, the bottleneck is usually the same: Google Docs holds part of the context, but support triage also needs signals that live outside it - on the public web, in LinkedIn, in news, in other connected apps. Strawberry is built to combine the Google Docs context with the rest of the browser, and run the full workflow as a companion you can re-trigger every week.

This page describes specifically how Strawberry handles support triage when Google Docs is one of the inputs. It names the Google Docs surfaces involved, the signals the workflow actually needs, an example prompt you can paste, and what a good output looks like.

The job a support engineer, founder doing support, CS lead is trying to do

The goal of support triage is to categorise inbound tickets, surface the urgent ones, and draft accurate replies grounded in product source-of-truth. The success metric is concrete: first-response time under 2 hours, accurate-categorisation rate above 95%, draft-edits-before-send under 20%. That definition matters because it shapes what Google Docs needs to contribute to the workflow.

What signals support triage actually needs

For each signal below, here is whether Google Docs can contribute directly or whether Strawberry has to find it via the browser:

• Ticket category (billing, bug, feature request, account, security) - Google Docs does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Sentiment (positive, neutral, frustrated, churn-risk) - Google Docs does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Product state (subscription tier, recent activity, feature flag) - Google Docs does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• History (has this user reported the same before) - Google Docs does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• GitHub/Linear status if it's a bug - Google Docs does not contain this directly. Strawberry uses the browser plus public sources to fetch it.
• Team-mate replies already in the thread - Google Docs does not contain this directly. Strawberry uses the browser plus public sources to fetch it.

What Strawberry can do inside Google Docs

Strawberry can draft new Docs from research or update existing Docs with structured outputs (briefs, recaps, reports).

Google Docs surfaces Strawberry uses for this workflow: headings, comments, suggesting mode, tables, linked Sheets/Slides.

How Strawberry runs support triage with Google Docs

1. Strawberry opens the Google Docs headings that contains the relevant context.
2. The companion pulls related context from Google Docs (comments, history, attached files) where it exists.
3. For the parts Google Docs does not store, Strawberry uses the browser - web search, LinkedIn, news, the prospect's website.
4. Strawberry synthesises the output in the shape this workflow needs: A draft reply per ticket, plus a category label and priority - human reviews before send.
5. A human reviews before any external action (send, update, post). Then the approved output is saved back to Google Docs or your system of record.

Example Strawberry prompt

Paste this in a new Strawberry chat with Google Docs connected. Adjust the specifics to your actual ICP, role, or topic.

Read this Google Docs headings and any linked context.
Then run a full support triage workflow on it. Use the browser to fill any gaps not in Google Docs.
Return the output in the shape we use for support triage: A draft reply per ticket, plus a category label and priority - human reviews before send.
Do not send anything externally. Save the draft to me to review.

What a good support triage output looks like

Here is what a finished output for support triage should look like in practice. The specifics will change for your use case, but the shape should look similar:

• Ticket #1962 - Marcus Rosenberg (marcus@clubstill.com)
• Category: billing - plan-state mismatch
• Priority: P1 (paying user, $118 charge vs Intern credits)
• Verified: Stripe shows Intern, charge log shows $118 Part-Time amount, credits granted at Intern rate
• Draft reply: confirm Intern is active, apologise for the rate mismatch, grant 22k credit balance to match Part-Time tier for current cycle, no refund promised

Why Google Docs for this, and where to use a different tool

Google Docs is strong for this workflow because Strawberry can draft new Docs from research or update existing Docs with structured outputs (briefs, recaps, reports).

Where Google Docs falls short complex tables can lose formatting; comments are write-once and don't always trigger collaborator notifications.

Consider also a CRM for the relationship layer.

Common mistakes when running support triage

• Auto-replying with 'we'll look into it' without doing the work
• Ignoring teammate replies already in the thread
• Guessing about product behaviour instead of checking GitHub or source code
• Automated security-report replies (always a major mistake - escalate to a human only)

Connecting Google Docs to Strawberry

Bundled in Google OAuth scope. Once connected, the companion can read the surfaces above without re-authenticating, and any write action still requires explicit human approval the first time the workflow runs.

Caveats

Do not let any AI agent send emails, update CRM records, or change shared systems without a clear approval step. Strawberry is strongest when the workflow combines browser context with connected-app context and a human review for sensitive actions.

When Google Docs + Strawberry is the right combo for support triage

Google Docs is the long-form output and shared reference doc. Strawberry can draft new Docs from research or update existing Docs with structured outputs (briefs, recaps, reports). For support triage specifically, that means the agent already has headings, comments, suggesting mode, tables, linked Sheets/Slides as starting context - you do not need to brief it from scratch.

When it is NOT a fit

• You need a single number, not a synthesised brief. A SQL query against your warehouse is faster.
• The decision is happening in the next 60 seconds. The agent is fast but it is not instant; for hard real-time use, do it manually.
• The Google Docs data you would feed in is stale or wrong. Garbage in, confident garbage out.

Three mistakes to avoid

1. auto-replying with 'we'll look into it' without doing the work
2. ignoring teammate replies already in the thread
3. guessing about product behaviour instead of checking GitHub or source code

Honest tradeoff

complex tables can lose formatting; comments are write-once and don't always trigger collaborator notifications. If you are running this at scale (10+ briefs per day), batch the inputs and let Strawberry process them as a routine instead of one-by-one prompts - cheaper per brief and the output stays consistent.

What a real output looks like

Ticket #1962 - Marcus Rosenberg (marcus@clubstill.com),Category: billing - plan-state mismatch,Priority: P1 (paying user, $118 charge vs Intern credits),Verified: Stripe shows Intern, charge log shows $118 Part-Time amount, credits granted at Intern rate,Draft reply: confirm Intern is active, apologise for the rate mismatch, grant 22k credit balance to match Part-Time tier for current cycle, no refund promised