Central reservations and guest care
Resolve routine contacts end to end, and give agents a copilot for the rest.
What this fixes
Group-level contact centres handle repetitive booking and loyalty questions across properties with inconsistent answers.
An automation layer across voice, chat, WhatsApp and email that answers the repetitive contacts on its own and assists a human on the complex ones. It reads the same systems your agents read, so an answer is grounded in the customer's actual record rather than a generic script. Arabic and English are handled natively, including dialect on the inbound side.
Multi-property booking
Loyalty and folio queries
Pre-arrival and upsell
Complaint handling
Where it earns its place
How we build it
Foundation check
Before any buildWe inspect the systems, the data behind them and the quality of the records this capability will depend on, then write down what has to be fixed before anything is built.
Narrow first release
First releaseOne workflow, one team, real traffic, measured against the baseline we captured before we started.
Controls and evaluation
Runs in parallelGuardrails, approval gates, evaluation sets, logging and the evidence pack your auditor and your regulator will ask for.
Scale and hand over
After sign-offAdditional workflows, sites or languages, then runbooks and training so your team can operate it without us.
What has to be right first
- A single customer identity across channels, or the bot answers the wrong person's question.
- Knowledge that is current and owned, an out-of-date policy page becomes a confidently wrong answer.
- Write access to the systems of record, otherwise the customer is told to call back.
- Call and transcript history for evaluation baselines.
Guardrails and KPIs
A human owns every consequential decision
Agents draft, route and execute reversible steps. Anything that moves money, changes a record of consequence or affects a person's rights stops at a named human approver with the reasoning attached.
Every action is logged and replayable
Inputs, retrieved context, model version, output and the human decision are written to an immutable log, so an auditor can reconstruct any single interaction months later.
Data stays where your regulator requires
In-country, in-region or on-premise deployment, with the model portfolio right-sized to the workload instead of routing everything to a frontier endpoint abroad.
Accuracy is monitored, not assumed
A held-out evaluation set runs on every model or prompt change, with drift alerts and a documented rollback path when quality moves.
KPIs it moves
- • First-contact resolution
- • Average handle time
- • Containment rate
- • Transfer and repeat-contact rate
- • CSAT and complaint volume
Integrations
Proof in this sector
Proof in this sector
Anonymised clientsData quality management, BI dashboards and guest-facing AI assistants
Global hotel groupDemand forecasting and revenue-management support
Resort group in the GulfGuest service assistant across web, app and messaging
Hotel group in Europe