
Brighthouse Financial case study: Batch queries cut from nearly 30 minutes to seconds
~30 min to secondsCritical query runtime
Industries · Insurance
We’ve stabilized daily batch operations for a life insurer and rebuilt admin and reporting workflows for an insurance brokerage.

Recurring failures were fixed at the root, and Brighthouse renewed.

Batch jobs kept failing, and manual fixes stretched the internal team.

Missing indexes slowed date-based queries, and reports went out without timely data.

Insurance agents and administrators did onboarding, reporting and admin tasks by hand, on an outdated platform.

Payouts ran on insurance-agent hierarchy data that had to stay intact through a move to a new database.
48%
of insurers worldwide have at least one generative AI use case in production
Source: Celent, via actuary.info
McKinsey’s 2026 survey finds insurers among the most likely to expect their AI investment to grow.
Source: McKinsey, The State of AI
The NAIC model bulletin sets out regulators’ expectations for insurers’ AI use. Pennsylvania adopted it in 2024.
Pick the package that matches your systems today. Each can add AI, and a named Amalgam engineer approves every change.
2 to 4 weeks
Get a written plan for what to change first, before you fund a new system.
6+ weeks
Most used
Rebuild an outdated platform around the workflows your admins and insurance agents run every day.
12+ weeks
Stabilize and modernize systems already in production, then keep them supported with managed services.
Pilot recruiting · delivery contingent
Cited answers from your documents. Team pilots start after the Trust Gate, our security and quality review.

~30 min to secondsCritical query runtime

Insurance-agent data moved to PostgreSQL
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