Insurance solutions
Energy Optimisation for Insurance
Beryl Analytics builds energy optimisation the way a software team would: version-controlled, monitored, peer-reviewed, and shipped in small slices. insurers get analytics infrastructure they can debug at 2am, not a black box they can only call us about.
Why insurance teams choose Beryl Analytics for energy optimisation
- Deep-domain models. Every energy optimisation model we build is tuned to the realities of insurers — not the synthetic benchmarks you see in vendor pitches.
- Production-ready, not throwaway. We ship pipelines, monitoring, alerting, and runbooks — the boring stuff that decides whether the system survives contact with reality.
- Operator-first design. Insights live inside the tools your team already uses, with thresholds and ownership matched to how decisions actually get made.
- Governance built in. Lineage, explainability, and access controls aren't an afterthought — they're scoped from day one and signed off with your security team.
- Outcomes measured in dollars. We track impact in revenue, cost avoided, or risk reduced — never in dashboard counts.
How we deliver energy optimisation 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 Energy Optimisation for Insurance
How long does a typical Energy Optimisation engagement take for a insurance business?
Most energy optimisation projects for insurers land a working production slice within 4-6 weeks, then harden and expand over the following 8-12 weeks. Larger insurance programmes that touch multiple business units take 4-6 months end-to-end.
What data do you need to start a Energy Optimisation project in insurance?
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 energy optimisation with our existing insurers systems?
Yes. We're tool-agnostic and have integrated with Snowflake, BigQuery, Databricks, Salesforce, SAP, Oracle, custom in-house platforms, and dozens of insurance-specific systems. Insights surface inside the tools your operators already use.
How do you measure success on a Energy Optimisation 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. Energy Optimisation engagements in insurance typically return 4-12x within the first year.
Do you work with insurance 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 energy optimisation engagements have been with regional insurers.