Financial institutions adopt AI at the speed their vendors and examiners allow.
Banks, insurers, and advisory firms run on regulated data they do not entirely control. What can be deployed, and in what order, depends on who holds the system of record and what the firm can defend under review.
Adoption arrived ahead of capability.
Individual employees are already using these tools to draft, summarize, and search. Firm-level capability is a different thing: systems connected to the record of the business, with controls a supervisor can inspect. The distance between the two is where the risk currently sits, and closing it is an engineering and governance question.
Nothing we recommend pays us a commission.
We resell no software and hold no platform partnerships, so the architecture we propose answers to your institution and your regulator. Strategy, data engineering, and integration exist in one team, which matters here because every recommendation eventually has to be written down, tested, and explained to someone whose job is to doubt it.
The system of record usually belongs to someone else.
A core platform, a carrier's policy administration system, or a custodian typically holds the data the institution runs on, and each one sets its own terms for access. That constraint decides what is buildable this year. It also means the work often begins by establishing an owned data layer alongside the vendor system, so the institution stops waiting for a roadmap it does not control.
Each of these answers to a different regulator and a different system of record.
Banking
Community and regional institutions with owned data, vendor-controlled access, and an examination culture that decides what ships.
View subsector02Commercial Insurance
Carriers, MGAs, and brokers where submission volume outpaces the underwriting bench that has to price it.
View subsector03Wealth & RIA
Advisory firms and registered investment advisers carrying supervision duties for tools their advisors adopted independently.
View subsectorThree ways we work across this sector
Modernization strategy
Decide where to invest, what to prioritize, what should happen first, and what evidence should change the plan.
Explore service ↗02Data engineering
Make data quality, ownership, lineage, and availability explicit before decisions or operating systems depend on them.
Explore service ↗03AI engineering
Use AI where it earns its place, from commodity capabilities and orchestration to proprietary engineering where organization-specific value justifies it.
Explore service ↗See where AI belongs in a regulated operation
A short assessment maps the data, the controls, and the review path before anything gets built.