The agent captures both structured ratings and open-ended qualitative feedback. Typical categories include food quality (taste, temperature, presentation), service quality (attentiveness, friendliness, speed), ambiance (noise level, cleanliness, decor), value for money, and likelihood to recommend. Each category can use numerical scales, multiple choice options, or free-text responses. The adaptive conversation flow means a guest who rates food poorly gets probing questions about specific dishes, while a guest who is satisfied moves quickly through the survey. This produces granular, actionable data rather than vague satisfaction scores.

The core difference is engagement format and timing. A traditional survey presents a static page of questions that feels like a task, which is why restaurant email surveys see 1-3% completion rates. A conversational AI agent asks one question at a time in a chat-like interface, adapts follow-ups based on previous answers, and can be triggered via SMS or QR code while the guest is still thinking about their meal. The result is 5-8x more responses, more detailed answers on negative experiences due to branching logic, and data that arrives in real time rather than in a spreadsheet someone checks once a month.

Yes. You configure one core feedback flow with your standard question categories, then customize per location to reflect menu differences, service styles, or specific operational focus areas. Each location is tagged automatically, so responses route to the correct manager and roll up into a group-level dashboard. A fine dining location might include questions about wine service and pacing, while a fast-casual location asks about counter speed and order accuracy. All data flows into a single reporting view where your operations team can benchmark locations against each other.

When a diner submits a low score on any category, the agent immediately sends an alert to the designated manager through email, Slack, or your preferred notification channel. The alert includes the full context of the feedback, the specific scores, and any written comments. This gives the manager a window to respond, whether that means visiting the table if the guest is still present, calling them that evening, or sending a personalized recovery offer. The speed of this response is what separates proactive guest recovery from reactive damage control on review sites.

Tars connects natively with CRMs like HubSpot, Salesforce, and Zoho CRM, and integrates with hundreds of additional tools through Zapier and webhooks. For restaurant-specific platforms like Toast, Square for Restaurants, Clover, and Lightspeed, Zapier provides pre-built connections so feedback data can flow alongside your sales and guest data. The webhook API also supports custom integrations with any platform that accepts HTTP requests, including reservation systems like OpenTable and Resy.

Tars is SOC 2 Type 2 certified and encrypts all data both in transit and at rest. Guest feedback, including any personal information like names or contact details, is stored securely and accessible only to authorized team members. For restaurants operating in the EU or serving EU customers, the platform supports GDPR-compliant data handling including consent capture and data deletion. You can also configure the agent to collect anonymous feedback when you want honest opinions without attaching them to identifiable guests.

Most guests complete the survey in 60-90 seconds. The adaptive question logic ensures satisfied guests move through quickly with simple ratings, while guests who flag specific issues get targeted follow-up questions that capture the detail your team needs to act. This is significantly shorter than traditional multi-page surveys, which is a major reason conversational agents achieve dramatically higher completion rates. The chat format also feels less demanding because guests see one question at a time rather than a wall of questions on a single page.

Tars supports multi-language conversations, which is essential for restaurants in metropolitan areas or tourist destinations where diners may prefer to give feedback in Spanish, Mandarin, French, or other languages. The agent detects language preference and conducts the entire feedback conversation accordingly. Multilingual support ensures you hear from your full guest population rather than only the English-speaking segment, which produces a more representative and accurate picture of your overall dining experience quality.