Review and testimonial mining

Review and testimonial mining is an automation that reads the praise you already collect, reviews on G2 and Google, support tickets, call transcripts, survey responses, and social DMs, then extracts the strongest quotes, groups them by theme, and drafts testimonials and case study snippets from the real words. Each quote passes through a consent step and a human review before it goes public. The point is not to write praise. It is to find the proof your customers already gave you and get it onto your site, decks, and ads, where it changes whether a prospect trusts you.

Manual collection means someone remembers to email a happy customer, waits for a reply, and formats whatever comes back, so it happens a few times a year at best. This works the other way around. It continuously reads the praise already sitting in your reviews, tickets, and call transcripts, surfaces the quotes worth using, and drafts them for you, so the bottleneck stops being someone's spare time. You still approve every quote and still get the customer's permission. The difference is that the finding and formatting is done for you, from words the customer already offered.

No. It only surfaces words a customer actually wrote or said, pulled straight from a real review, ticket, transcript, or survey, and it links each one back to its source so you can verify it. It does not invent quotes, does not reword them into something the customer did not say, and does not stitch phrases together to imply a stronger claim. That rule is the core of the setup. A fabricated testimonial is a legal and trust problem, so the system is built to draft only from real language and to flag anything it is unsure about for a person to check.

Nothing goes public until the customer has agreed and a person has signed off. The system tracks consent as its own record: who approved which quote, on which channel, and when, so you always know a published testimonial is cleared. Where a customer has not yet given permission, the quote stays in an internal draft and can trigger a simple permission request rather than being used. This consent log is the part that takes the most care to set up, because it is what keeps you on the right side of both the law and your customer relationships.

On the source side, review sites like G2, Google, Trustpilot, and Capterra, support tools like Zendesk and Intercom, call platforms like Gong and Fireflies, survey tools like Typeform, and social DMs. On the delivery side, your website CMS, sales decks, ad libraries, and your CRM such as Attio, so approved quotes land where they build trust. Most tools with an API can be added. The audit maps your exact stack and picks the sources where your real praise actually lives.

Usually two to three weeks. The first days go to connecting your sources, setting the rules for what counts as a strong, specific quote, and building the consent log and approval gate. Then the system runs against your real praise so the drafts and the permission flow get tested before anything is published. The consent and approval steps are why this takes a little longer than a pure read-only automation, and they are exactly what makes the output safe to put on your site.

Two parts. Tooling runs as a modest monthly cost for the connectors and model usage, typically a per-source range that scales with how many places you pull praise from. Implementation is a fixed scope, quoted once the audit maps your sources, consent workflow, and where approved quotes get published, so you are pricing a defined build rather than an open-ended retainer. The audit itself is where the scope and price get set against every other opportunity in your business, so you are not guessing at effort up front.