How ReplyPolish drafts and reviews customer replies

ReplyPolish separates wording assistance from business decisions. It can organize a response, adjust tone, and point out common risks, but it does not know whether a refund is approved, a parcel will arrive on a certain date, or an account change is authorized. Those facts must come from the user’s case record and remain subject to a final human review.

Last reviewed: July 27, 2026.

Default local drafting path

The default workflow runs in the browser. The page reads the customer message, intended response, facts to keep, tone, channel, language, and optional writing sample to build a rule-based draft. Generating this local draft does not send the text in those fields to the ReplyPolish server. No account is required, and the tool does not send the finished reply to the customer.

The sensitive-detail option is enabled by default for local analysis. It looks for email addresses, phone numbers, common order or ticket references, and links, and substitutes generic markers while the draft is prepared. This is a limited pattern check, not anonymization or a guarantee that private data has been removed.

Optional AI beta path

The AI beta is a separate, user-initiated second pass. It is available only after a local draft exists. When the user selects it, ReplyPolish displays a notice and sends a best-effort masked payload through the same-origin AI endpoint to Cloudflare Workers AI. The payload includes the form fields, local draft, selected language, tone, and channel. Masking is applied in the browser and checked again by the Worker before model processing.

ReplyPolish does not store the submitted message or AI output. Short-lived anonymous counters are used to enforce usage limits, and a secure device cookie helps apply those limits. The AI result is shown separately from the local draft and is never sent automatically. See the privacy page for the current storage, analytics, advertising, and quota details.

How facts are kept separate from tone

The “Facts to keep exactly” field is the place for confirmed dates, amounts, status, policy boundaries, and approved next actions. Local drafting treats those facts as source material. The optional AI instruction also tells the model not to invent policies, refunds, compensation, dates, amounts, or completed actions. Neither mechanism can prove that the user’s input is correct or guarantee that every output preserves it perfectly.

1. Copy only confirmed case facts into the facts field; keep guesses and desired outcomes out.
2. Choose tone and channel as presentation choices, not permission to change the decision.
3. Compare every name, number, date, amount, status, and promised action with the source record.
4. Remove any confident claim that is not supported by that record.

What the risk checks do

The local checks flag a limited set of signals, including guarantee-like wording, references to legal responsibility, refund or compensation language, missing intent, and detected private identifiers. They also help the user inspect length, next-step clarity, and channel fit. A flag means “review this sentence”; the absence of a flag does not mean the reply is safe, lawful, accurate, or compliant with a business policy.

Pattern checks can miss names, unusual identifiers, context-specific secrets, and sensitive details written in an unexpected format. Users should not enter passwords, full payment-card data, security codes, government IDs, medical records, or other highly sensitive information in either drafting path.

The required human review

Before sending, a person with access to the real case should verify the decision and the message. The review should cover the customer and account, policy version, dates and amounts, promised action, responsible team, update window, destination channel, and information that should stay private. High-risk billing, legal, health, safety, account-access, and compliance topics require the organization’s own qualified review.

ReplyPolish is a drafting aid, not an automatic decision system, help desk, legal adviser, or delivery mechanism. If the draft and the case record disagree, the case record and authorized human decision take priority.

How public resources are created and reviewed

Guides, checklists, and examples begin with a defined support question and a small set of facts that a reply is allowed to use. Public examples are written as synthetic teaching cases rather than presented as real customer records. Drafting or translation automation may assist the first pass, but the site operator remains responsible for publication.

1. Define the scenario, channel, locked facts, prohibited claims, and safe next action.
2. Draft the rough and improved versions without adding a policy outcome.
3. Review factual consistency, privacy exposure, tone, channel fit, and action clarity.
4. Check that the page contributes its own explanation rather than repeating a template or navigation list.
5. Run internal-link, metadata, indexability, and content-overlap checks before publication.
6. Correct material errors and update the visible review date on process and safety pages.

The detailed ownership and correction rules are in the editorial policy.

Known limitations

• Rule-based local drafts can sound repetitive, miss context, or choose an awkward phrase.
• AI output can omit a fact, add an unsupported detail, or misunderstand tone even when instructed not to.
• ReplyPolish cannot see the ticket history, current business policy, inventory, payment system, or approval chain unless the user provides relevant facts.
• Masking and risk checks cover common patterns only and cannot detect every sensitive or risky statement.
• Language and cultural nuance vary by organization, region, and channel; a fluent reviewer may still be necessary.

Transparent synthetic evaluation protocol

This section defines how a future evaluation should be run. It does not report a completed benchmark or claim a performance score.

1. Create synthetic cases across the supported languages and common scenarios. Each case records its locked facts, prohibited claims, privacy markers, expected channel, and acceptable next action before any output is generated.
2. Record the site version, test date, selected controls, and exact synthetic input. Test the local draft separately from the optional AI second pass.
3. Check whether every locked fact is preserved, whether any new date, amount, decision, or action appears, whether sensitive markers are repeated, and whether the next step fits the selected channel.
4. Use a fluent human reviewer for naturalness and cultural tone. Keep factual and privacy checks separate from subjective style ratings.
5. Retain failures, empty outputs, and awkward outputs instead of selecting only successful examples. For non-deterministic AI runs, disclose the number of attempts and do not replace the first result without recording it.
6. Publish a score only with the scenario set, criteria, version, denominator, exclusions, and failure examples needed to interpret it. A synthetic pass does not establish correctness for a real customer case.

Practical rule: preserve the source facts, use the tool to improve presentation, and require a human to approve the final message.