Scan AI-drafted ticket replies before they reach the customer. Brand voice consistency across ten or more agents, sentence-level highlights to flag the AI passages, audit log on Business, REST API for Zendesk, Intercom, Front, Help Scout, and Salesforce Service Cloud. Built for support, success, and escalation teams that care about customer trust and NPS. Free to try. No card.
SaaS support desks, ecommerce CX teams, customer success orgs, and the escalation queue inside any in-house service function. Ten or more agents, an AI assistant in the helpdesk, hundreds of replies a day across email, chat, and in-app messaging, and an NPS number the leadership team watches every quarter.
Customer service teams answer to leaders who read NPS, CSAT, and reply samples. The VP of CX pulls a random ticket on Monday morning. The COO reads escalation threads before quarterly reviews. The CEO opens a complaint on social and traces it back to a templated reply. Every ticket is a signal in the eyes of leadership and the customer, and a flat AI tone reads as the company not caring rather than the agent being efficient.
The front line drafting most replies. Some use the helpdesk AI suggestion, some draft in ChatGPT in another tab, some still type from scratch. The mix is healthy. What hurts is when the AI-assisted replies go out without an edit pass and the customer feels the difference within two tickets.
Longer-form replies, account check-ins, renewal conversations. Templated AI prose in these threads reads as the CSM not knowing the account, which is the perception that costs renewals. A quick scan before sending catches the lines that drifted into stock phrasing.
The most sensitive ticket types. Outage updates, refund disputes, account terminations, abuse reports. A flat reply on an escalation ticket reads as the company on autopilot, which is the failure mode that drives churn the hardest. The scan is the gate before sensitive replies go out.
The detector targets the part of your support stack that varies per ticket and reads templated to the customer. Macros do not, even if they look identical. AI-generated replies do, even when an agent edits them. Understanding the difference is the first move toward a useful workflow.
A macro is a fixed reply the agent inserts and customises. Your team wrote it, your brand approved it, the customer reading it knows it is a template. The detector ignores macros because there is no AI signal in them and no drift risk. If a macro reads flat, that is a copywriting question, not an AI question.
Zendesk AI suggestions, Intercom Fin replies, Help Scout AI drafts, or ChatGPT in another tab. The model regenerates per ticket and varies across agents and across days. That variation is exactly what brand voice fights against. The detector picks up the model's structural fingerprint even after a light human edit.
The most common modern pattern. An agent accepts the AI draft, rewrites two or three lines, sends. The detector reads these mixed replies as partially AI rather than fully human, which is the honest call. Scores in the 60 to 75 band mean the AI structure survived the edit; below 60 the draft is essentially AI; above 80 the edit was substantive.
For routine tier 1 tickets an Authenticity Score floor of 70 is enough. For success and renewal threads push the floor to 75. For escalation, outage, refund, and account-termination tickets target 80 or 85. The floor varies by queue, the scan workflow stays the same.
Five-second addition per reply on the agent side. The integration path is REST API on the Business tier via webhook on ticket reply draft. Native plugins for the major helpdesks are on the 2026 roadmap; the API plus the Chrome extension cover the workflow today.
Wire the scan into the Zendesk ticket events stream via REST API webhook. Trigger on ticket comment draft created or status change. Pass the draft reply to TextSight, get the score and sentence highlights back in about three seconds, surface the result in a side-panel app or a Slack notification for the QA lead. The native Zendesk app is on the 2026 roadmap.
Same pattern as Zendesk. The Intercom Conversations API plus webhook on admin reply gives the trigger. For Fin-drafted replies specifically, the scan is the gate that catches generic Fin output before a human agent forwards it without an edit pass.
All three support REST integrations via webhook on reply draft. The glue code is a few lines per helpdesk. The Chrome extension covers the smaller-scale case where an agent drafts in the helpdesk web UI and clicks the extension before pasting the reply into the customer thread.
Chrome extension and REST API ship today. Native Zendesk, Intercom, Front, and Help Scout plugins are planned for 2026. Most support teams start with the Chrome extension on the QA lead's browser, layer in the API once the workflow proves out across a quarter, and switch to the native plugin when it ships.
Business at $39.99 a month standard, $29.99 a month on yearly, is the right fit for support, success, and escalation teams running ten or more agents through a shared workspace. Five seats, audit log, REST API, white-label PDFs. Full details on the pricing page.
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Customers in 2026 spot a templated reply within the first two sentences. The pattern they read is not the model itself; it is the assumption that the company is on autopilot. Once that assumption sets, NPS dips on the next survey and the renewal conversation gets harder by a measurable margin.
Support teams that rolled out helpdesk AI suggestions without a quality gate in 2025 saw an NPS dip of three to seven points inside a quarter. The dip showed up in verbatim comments well before it showed up in the number. Customers wrote that replies felt copied or generic without using the word AI. The scan workflow gives the support lead the diagnostic before the survey results land.
The single highest-risk reply category. A flat templated reply on a refund dispute, an account-termination notice, or an outage update reads as the company not caring. Customers who churn from a botched escalation rarely come back. The cost of an extra ninety seconds of editing is negligible against the cost of the lost account.
Tier 1 ticket CSAT scores correlate with reply quality in a measurable way. The teams that scan their AI-assisted replies for the routine queue typically hold CSAT steady or lift it by one to three points across a quarter. The teams that do not see the slow erosion as AI assistance scales without a quality gate.
One templated reply is forgivable. Twenty in a quarter from the same agent or the same queue is a brand pattern. Customers compare notes on review sites, in Slack communities, and on social. The pattern compounds, and the fix is the scan gate at the agent level, not a corporate-voice memo after the dip.
Support voice fragments faster than most CX leads expect. Ten agents leaning on a mix of Zendesk AI, Intercom Fin, and ChatGPT produce replies that drift toward a neutral templated register inside a single sprint. The scan is the diagnostic that catches the drift before the VP of CX hears about it from the CEO.
Coaching catches individual replies. It rarely catches the aggregate drift across two thousand tickets in a month, which is why brand voice audits keep surfacing the same finding sprint after sprint. The scan surfaces the structural drift by showing sentence-level highlights across the whole queue, not just inside one ticket.
A style guide that asks for specific product vocabulary instead of generic SaaS-support language is also asking for prose that reads less templated. Agents honouring the brand vocabulary lift the detector score as a side effect, and the brand stays distinct across the inbox, the in-app messenger, and the public review thread.
The Authenticity Score is the diagnostic, not the goal. Rewriting purely to lift the number flattens the voice. Use the sentence highlights to find the specific lines that drift into stock phrasing, rewrite those, and let the headline score land where it lands. The brand voice survives the workflow.
The Business tier gives the team one workspace with shared scan history across agents and ticket categories. The support lead pulls a rolling thirty-day view per queue (tier 1, tier 2, success, escalation) and sees averages instead of anecdotes. A queue drifting toward lower scores gets attention before the next NPS survey lands.
Not every queue carries the same weight. The routine password-reset reply can sit at an Authenticity Score of 70 without anyone noticing. The escalation reply cannot. Setting a different floor per queue is the difference between a workflow that scales and a workflow that costs accounts.
The customer is already frustrated and the reply will be screenshotted and posted on social before it lands in the inbox. Target an Authenticity Score floor of 85 on incident comms specifically. The first sentence carries most of the emotional weight; rewrite anything flagged at the sentence level before sending. The cost of taking an extra two minutes is negligible against the public-facing reputational risk.
Money tickets compound when the reply reads as the company on autopilot. A templated tone here reads as the company not engaging with the dispute, which is the perception that pushes the customer to the chargeback path. Target 80 on the dispute queue and treat AI-drafted replies as a draft, not a finished reply.
The last touch the customer has with the brand. A flat reply at offboarding kills the chance of a future return and seeds negative review sites. Target 80 here as well, and require a human rewrite pass on any AI-drafted draft regardless of the initial score.
Abuse reports, account hijack claims, fraud alerts. The customer needs to read confidence and human attention, not templated reassurance. Target 85 on trust-and-safety replies specifically and route every AI-drafted draft through a senior agent before sending.
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See pricing →Free to try. No card. Business at $29.99 a month on yearly for support, success, and escalation teams running ten or more agents through a shared workspace.