Every diner, every platform, one weekly read
Reviews sat on five platforms, in three languages, with no unified view. FireAI review intelligence (SPARK) brings every review into one dashboard, tags each for sentiment, theme, and dish mentions, scores six experience pillars weekly, and drafts a guardrailed reply for each.
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- review platforms unified
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- experience pillars scored weekly
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- hospitality KPIs tracked weekly
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- languages read side by side
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An F&B chain in Vietnam adopted FireAI review intelligence to unify reviews from five platforms in three languages into one dashboard. Every review is tagged for sentiment, theme, and dish mentions, six experience pillars are scored weekly, and a guardrailed reply is drafted for each. This is an early-stage deployment; the figures below are Q1 targets.
Five platforms, three languages, no single read on the diner
Reviews on five platforms all mattered for discoverability, ranking, and reputation, but they sat in five different places, in three different languages, with no unified view. The general manager had no way to see whether food, service, ambience, or wait time was the real driver of a slipping rating.
By the time a problem showed up in the headline number, it had often been brewing for weeks. Tourist reviews and local reviews were telling different stories and nobody was reconciling them. For a restaurant whose ranking is partly a function of review-response activity, the cost of inaction was direct: lower discoverability, slower bookings, and a reputation that drifted before anyone could correct it.
- Reviews scattered across five platforms in three languages, with no unified view
- No way to tell whether food, service, ambience, or wait time was driving a rating change
- Problems surfaced in the headline rating only after brewing for weeks
- Tourist and local reviews told different stories that nobody reconciled
- Ranking depends partly on review-response activity, but responses were slow and ad hoc
What they tried before
The review workflow was manual, and the general manager was deciding on a partial sample of the customer voice.
A weekly manual check
A team member checked each platform roughly once a week, eyeballed recent reviews, and forwarded the worst ones to the kitchen or service lead by message.
No aggregation or scoring
There was no sentiment scoring and no structured way to track whether the same complaint appeared across platforms, so most reviews were never read carefully, only the very recent or the very angry ones.
Patterns went unseen
A wait-time complaint on one platform was usually echoed on another, but nobody connected them, so recurring issues stayed invisible until the rating moved.
One read on every diner, across every platform
FireAI review intelligence was deployed in layers: multi-platform aggregation, LLM enrichment, a weekly experience fingerprint, and a guardrailed response assistant.
Every review across every platform now lands in one unified view, with the original language preserved alongside the translation. The same review-intelligence pattern can scale from one outlet to the group's other brands without re-platforming.
Multi-platform aggregation in any language
Reviews from all five platforms land in one view, original language alongside translation, so tourist and local feedback are finally read side by side.
LLM-driven enrichment
Every review is tagged for sentiment, theme (food, service, ambience, cleanliness, wait time, value), dish mentions, and escalation severity. Multiple tags per review mean a single complaint about cold food at dinner is attributed to both kitchen and shift at once.
A weekly experience fingerprint
Six experience pillars, food quality, service, ambience, cleanliness, wait time, and value for money, are scored 0 to 100 every week, producing a radar fingerprint comparable across time and, once sister brands onboard, across the whole portfolio.
A guardrailed response assistant
The dashboard opens to a queue of drafted, on-brand replies, with hard guardrails preventing promises of refunds or legal commitments. The team approves, edits, or declines each reply in one click. Nothing is auto-posted.
Why they chose FireAI
- The connector and AI infrastructure were already in place, so deployment was measured in weeks, not months
- The same review-intelligence pattern has been delivered before on FireAI's platform
- A platform was needed, not a point tool; response agents, campaign suggestions, and the group's other brands are all on the roadmap
Q1 targets, set at the start of the engagement
This is an early-stage deployment. The figures below are Q1 targets defined with the general manager; measured outcomes will replace them at the 90-day review.
Review coverage moves from zero unified platforms to five. Every menu item with review mentions is tracked weekly, hero dishes flagged for marketing and problem dishes flagged for the kitchen, and the six experience-pillar scores establish a baseline within the first 30 days with a weekly trend after. Because the figures above are targets rather than measured results, they will be revisited and replaced at the 90-day review.
How the rollout went
Live in stages, under two weeks per outletSetup ran in measured stages on top of FireAI's existing connector library and LLM pipeline, so no new infrastructure had to be built for this deployment.
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Week one: connectors and schema
Schema definition and the first platform connectors went live, with reviews ingesting into the unified view, original language alongside translation.
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Week two: LLM enrichment
Sentiment, themes, and dish mentions went live, structured against the restaurant's own menu taxonomy.
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Week three: pillars and replies
The experience-pillar scoring and the response assistant were deployed, with the remaining connectors sequenced alongside access provisioning.
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A single onboarding session
One working session with the general manager mapped menu items to the dish taxonomy and confirmed the response tone for the assistant.
Key takeaways
- A single view across every review platform, in any language the customer wrote in
- An experience fingerprint scored weekly on the six dimensions diners actually judge a restaurant on
- A drafted, guardrailed reply for every review, so the GM responds in minutes rather than hours
- A framework that scales from one outlet to a multi-brand group without re-platforming
Who should consider FireAI?
F&B groups running two or more brands or five or more outlets, single restaurants whose reviews live across three or more platforms, and hospitality operators whose ranking and review presence are meaningful revenue drivers, especially across multiple languages.
Frequently asked questions
Get one read on every diner, across every platform
If your reviews live across several platforms and languages with no unified view, FireAI review intelligence can bring them into one dashboard, score your experience pillars weekly, and draft a guardrailed reply for each. Book a demo on your own data.
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