Reputation Guardian
Grow your reviews the honest way: know what drives your stars, answer every review without revealing a patient, and prove you never gamed a rating.
How it works
One inbox for every review
Google Business Profile, Facebook and industry sites in one place, read in Lithuanian and English.
What actually costs you stars
Aspect-based sentiment analysis splits each review into what it is about (waiting time, staff, price, result) and how it feels, so the 1-stars point at one cause you can fix.
Replies behind a confidentiality gate
Drafts in your voice are checked for patient disclosure, prohibited health claims and defensive tone. A person approves and posts every reply; nothing is posted automatically.
Ask for reviews the honest way
Requests go to every patient after a visit, timed on your appointment data, never filtered by how happy they seem. Every request is logged.
Methodology reference
The published method this build is based on, and what it did in production.
Trustpilot removed 4.5 million fake reviews in 2024, 90% of them detected automatically by machine learning, and publishes the figures every year.
Authenticity-first review detection · as reported by Trustpilot Trust Report 2025 ↗
Referenced for methodology only. AimRank is not affiliated with, endorsed by, or a customer of these companies; each links to its own public source. Sources last checked September 2026.
The Four Guarantees™ - this build
The scaffold wires all four in. The PoC proves them on your data, as of that day. Only Implementation keeps them true over time: watched, drift-checked, with recourse as the Build page defines it.
Measured value
Response time, response rate and review volume measured against your own baseline; eval gate on reply safety and aspect accuracy before ship.
Defensible
Every reply cites the review it answers and the checks it passed; every request is logged as sent without filtering, so the trail is the evidence that you played clean.
Self-improving
Aspect and sentiment drift watched; a new complaint theme or a spike of negative reviews raises an alert to a person.
Yours & everywhere
Your reviews stay in your region under access you control; full source. MCP endpoint (analyze, draft_reply, evidence) for front-desk and marketing agents.
The evidence, and what we commit to
Reference buyer: Lithuanian dental or aesthetic clinic, or a 3-site group, receiving ~40 to 80 reviews a month across Google and Facebook, where the front desk replies slowly and inconsistently.
Three ways to own it
| Tier | What you get | Price | |
|---|---|---|---|
| Scaffolding ★ | The full repo - review ingestion scaffold, aspect-sentiment baseline, the reply-safety gate (confidentiality, claims, tone), audit log, MCP server. Reference run on public review sets. | €1,490 | |
| PoC ★★RECOMMENDED | Connected to your Google Business Profile and Facebook, tuned for Lithuanian: aspect accuracy and reply safety measured on your own reviews plus a red-team set. Code + quality report yours. No guarantee at PoC. | from€6,000 | |
| Implementation ★★★ | Production: multi-location monitoring, a reply queue with human approval, request timing on your appointment data, alerts to a channel you read, monthly reputation and compliance-evidence report - the agreed number (conservative) attaches here. | from €18,000+ from €900/month assurance |
★ = engagement depth. PoC is the recommended path: quality proven on your data before production money. The PoC carries no performance guarantee by design; the agreed number (conservative) attaches at Implementation, informed by the PoC report.
What we don't promise
Ready to see your own number?
Request the build: within 48h you get a personal reply with the value sized to your volume.
No commitment · reply within 48h · your data stays in the EU