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AI Detector for customer service, built for support, success, and escalation teams.

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.

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Business at $29.99/mo yearly Audit log & REST API No training on your tickets
Who it is for

Built for support, success, and escalation teams.

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.

Support agents (tier 1 and tier 2)

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.

Customer success managers

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.

Escalation and trust and safety

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.

Macros vs AI

Pre-written macros are not AI. AI-drafted replies are.

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.

Macros are deterministic and brand-chosen

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.

AI-drafted replies are stochastic and drift

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 hybrid case: AI-drafted, human-edited

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.

Where to draw the line

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.

Ticket response workflow

Zendesk, Intercom, Front, Help Scout, Salesforce Service Cloud.

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.

Zendesk

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.

Intercom and Fin

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.

Front, Help Scout, Salesforce Service Cloud

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.

Honest scope

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.

Plans & pricing

Business is the CS team tier.

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.

Free
$0/forever

 

Evaluate before rolling out to the queue.
  • 3 scans / day
  • 5,000 chars per scan
  • Sentence-level highlights
  • 2 lifetime AI rewriter uses
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Starter
$7.49/month

Billed $89.88/year — Save $30

For the QA lead piloting before the team rolls in.
  • 20 scans / day
  • 20,000 AI rewriter words/mo
  • Chrome extension
  • Email support
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Pro
$14.99/month

Billed $179.88/year — Save $60

Senior agents and CSMs on the team. Single-seat workflow.
  • Unlimited scans
  • 50,000 AI rewriter words/mo
  • 10,000 chars per scan
  • 90-day scan history
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Yearly billing saves 25%. View full pricing →

Trust and retention

A reply that looks AI hurts NPS and pushes churn.

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.

NPS is the leading indicator

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.

Churn risk on escalation tickets

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.

CSAT on routine queues

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.

The compounding effect

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.

Voice consistency

One brand voice across ten or more agents.

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.

The drift is structural, not individual

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.

Style guide and detector pass-through line up

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.

Editing for voice, not for the number

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.

Shared scan history across the queue

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.

Empathy & escalation

Sensitive tickets need a higher floor.

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.

Outage and incident communications

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.

Refund and billing disputes

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.

Account termination and offboarding

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.

Trust and safety tickets

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.

FAQ

Customer service teams frequently ask.

Why do customer service teams need an AI detector?
Modern support stacks layer Zendesk or Intercom AI suggestions on top of ten or more human agents. Without a scan step, customers start receiving replies that read identical across agents and across tickets, and NPS dips by the next quarterly survey. An AI detector gives the support lead one number to enforce on AI-assisted replies, especially on sensitive tickets where a templated tone reads as the company not caring.
What is the difference between a macro and AI-drafted text?
A pre-written macro is a fixed template the agent inserts and customises. The detector ignores macros because they are deterministic text that the brand chose deliberately. AI-drafted text is generated per ticket by Zendesk AI, Intercom Fin, a Copilot-style assistant, or ChatGPT in another tab. The detector targets that second category, which is the part that drifts away from brand voice and reads templated to the customer.
Does TextSight integrate with Zendesk, Intercom, Front, or Help Scout?
Today the integration path is REST API on the Business tier. Wire a scan into Zendesk, Intercom, Front, Help Scout, or Salesforce Service Cloud via webhook on ticket reply draft. Native plugins for Zendesk and Intercom are on the 2026 roadmap. The Chrome extension covers the case where an agent drafts in the helpdesk web UI and wants a quick scan before clicking send.
How do you keep brand voice consistent across ten or more support agents?
Each agent develops a personal phrasing style. When all of them lean on the same AI assistant, distinct AI patterns layer on top of personal patterns and the brand sounds generic across replies. Sentence-level highlights flag the AI passages so the agent can rewrite the generic lines and keep their personal voice underneath. Shared scan history on Business gives the support lead a rolling view of which agents and which ticket types are drifting.
What about AI-drafted replies that an agent then edited by hand?
This is the most common modern pattern and the hardest case for any detector. The classifier is calibrated to read these mixed replies as partially AI rather than fully human, which is the honest answer. An Authenticity Score in the 60 to 75 band typically means the AI structure survived the edit even if the words were rewritten. Below 60 means the draft is essentially AI; above 80 means the human edit was substantive.
How should we handle empathy and escalation tickets specifically?
Sensitive ticket types like outages, refund disputes, and account terminations are where a templated tone hurts the most. Set a higher Authenticity Score floor of 80 or 85 on these queues specifically and treat any AI-drafted reply as a draft that needs substantive human rewriting. The cost of a flat reply on an escalation ticket is a churned customer; the cost of an extra ninety seconds of editing is negligible.
Does the Business tier include an audit log and shared history?
Yes. Business at $39.99 a month standard, or $29.99 a month on yearly, includes five team seats with shared scan history, an audit log of which agent scanned which ticket reply with timestamps, REST API access, and white-label PDFs. The audit log is useful for QA reviews where the support lead wants to demonstrate consistent quality control across agents and across ticket categories.
Does TextSight train on our ticket data or share it with anyone?
No on both. Scans are private to your workspace and we do not share team content with anyone. Text submitted for scanning is never used to train the classifier or any other model. This applies the same way on Free, Starter, Pro, and Business. Customer ticket data confidentiality is honoured by default across the tiers.
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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.

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Brand voice across reps · Audit log & REST API · White-label PDFs · No training on your tickets