Aviation solutions
Recommendation Engines for Aviation
Whether you're modernising a legacy data stack or building greenfield, Beryl Analytics's recommendation engines practice gives aviation operators the same calibre of analytics engineering you'd find in the world's top product companies.
Why aviation teams choose Beryl Analytics for recommendation engines
- Senior practitioners. No bait-and-switch — the architects you meet in scoping are the engineers who ship the system. We don't farm work to juniors.
- APAC time zone, APAC context. We understand aviation operators regulations, data residency expectations, and the procurement cycles your team actually navigates.
- Honest scope. If a recommendation engines use case isn't ready for ML yet, we'll tell you. Half our highest-impact engagements start by killing initiatives that wouldn't have worked.
- Tool-agnostic. Snowflake, BigQuery, Databricks, Postgres, S3 — we work with what you already run.
- Speed without recklessness. First production slice in 4-6 weeks. Hardened over the next 8-12. No 18-month black-box programmes.
How we deliver recommendation engines engagements
- 01
Discovery (week 1-2)
We meet your operators, map data sources, and pressure-test the business case. Half the value is sometimes in killing the wrong initiative and reframing the right one.
- 02
Pilot build (week 3-6)
One vertical slice end-to-end: ingest, model, dashboard, monitoring. Real data, real users, measurable result before we expand.
- 03
Productionise (week 7-12)
Hardening, governance, lineage, runbooks, observability. Pair-programmed with your team so they own it by handover.
- 04
Scale & evolve
Expansion into adjacent use cases, retraining cadence, model performance reviews, and a roadmap that compounds.
Frequently asked questions about Recommendation Engines for Aviation
How long does a typical Recommendation Engines engagement take for a aviation business?
Most recommendation engines projects for aviation operators land a working production slice within 4-6 weeks, then harden and expand over the following 8-12 weeks. Larger aviation 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 aviation?
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 aviation operators systems?
Yes. We're tool-agnostic and have integrated with Snowflake, BigQuery, Databricks, Salesforce, SAP, Oracle, custom in-house platforms, and dozens of aviation-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 aviation typically return 4-12x within the first year.
Do you work with aviation 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 aviation operators.