Hospitality solutions
Recommendation Engines for Hospitality
Whether you're modernising a legacy data stack or building greenfield, Beryl Analytics's recommendation engines practice gives hospitality groups the same calibre of analytics engineering you'd find in the world's top product companies.
Why hospitality teams choose Beryl Analytics for recommendation engines
- Built for compounding value. Each recommendation engines engagement leaves hospitality groups with infrastructure that accelerates the next one — shared feature stores, reusable pipelines, documented data contracts.
- Real handover. We pair your team into the build from day one. By go-live, they own the system. We're optional from then on.
- Practical AI. We've shipped LLM-augmented analytics where they help, and stayed with simpler models where they outperform. Hype is not a strategy.
- Audit-friendly. Every model decision is traceable. Compliance and risk teams stop blocking — they start enabling.
- Track record. 1,000+ models in production. Across heavy-industry, regulated, and consumer domains.
How we deliver recommendation engines engagements
- 01
Frame the decision
Before we touch a model, we agree what decision the output will change, who owns that decision, and what counts as success in dollars or risk reduced.
- 02
Land a working slice
A narrow but complete production system: source-to-decision in 4-6 weeks, monitored, owned, and measurable. Then we expand from real evidence.
- 03
Embed the operating model
Retraining cadence, alerting thresholds, escalation runbooks, and clear ownership. The system stops being "the analytics project" and becomes part of how the business runs.
- 04
Compound the wins
Reuse the foundation across the next use case. Each engagement makes the next cheaper, faster, and lower-risk.
Frequently asked questions about Recommendation Engines for Hospitality
How long does a typical Recommendation Engines engagement take for a hospitality business?
Most recommendation engines projects for hospitality groups land a working production slice within 4-6 weeks, then harden and expand over the following 8-12 weeks. Larger hospitality programmes that touch multiple business units take 4-6 months end-to-end.
What data do you need to start a Recommendation Engines project in hospitality?
Minimum viable inputs are 12-18 months of historical transactional or operational data, basic entity reference tables, and access to the systems that will consume the output. We can work with messy data — cleaning is part of the engagement.
Can Beryl Analytics integrate recommendation engines with our existing hospitality groups systems?
Yes. We're tool-agnostic and have integrated with Snowflake, BigQuery, Databricks, Salesforce, SAP, Oracle, custom in-house platforms, and dozens of hospitality-specific systems. Insights surface inside the tools your operators already use.
How do you measure success on a Recommendation Engines engagement?
Before we model anything, we agree the business decision the output will change and the dollar metric we're targeting — revenue lifted, cost avoided, or risk reduced. Recommendation Engines engagements in hospitality typically return 4-12x within the first year.
Do you work with hospitality businesses outside major NZ and AU cities?
Yes. We deliver remotely across New Zealand and Australia and visit on-site for discovery, key workshops, and go-live. Distance is not a blocker — many of our highest-impact recommendation engines engagements have been with regional hospitality groups.