Enterprise AI for Financial Services

Get AI from pilot to production with the controls and processes that regulators expect.

Trusted by financial institutions

ANZProspaClearViewIAGMorrison SecuritiesScotPacABL CorpEY
The challenge

Most AI investment has not translated into production outcomes

Organisations have run proofs of concept, hired data teams, and tested use cases. But getting AI into production inside a regulated environment remains hard. The barrier is usually not the technology. It is the combination of regulatory requirements, data readiness, infrastructure limitations, and the processes needed to run AI reliably and at scale.

67%

Have adopted AI

AU financial services firms

Broadridge, 2025

75%

Stuck in pilot

Have not scaled AI to production

Deloitte, 2026

~50%

Governance gap

Licensees lack AI fairness policies

ASIC REP 798, 2024

44%

Skills shortage

Cite AI talent as top barrier

Deloitte, 2026

4 integrated capabilities

Our services

Four services that take AI from concept to production in regulated environments.

01

AI Implementation

Production AI deployment integrated with your existing infrastructure and regulatory requirements.

Model evaluation Core banking integration Production hardening
02

AI Governance & Policy

Acceptable use policies, risk classification, and approval workflows aligned with regulatory requirements.

Risk frameworks Regulatory alignment Board-ready documentation
03

Team Onboarding & Training

Role-based training programmes and prompt engineering workshops for your teams.

Role-based programmes Prompt engineering Change management
04

Logging, Audit & Visibility

Immutable audit trail capturing every AI interaction for regulatory review.

Immutable audit logs Real-time monitoring Compliance reporting
4-phase process

Our approach

A structured process from assessment to ongoing operations.

01
2 to 4 weeks

Assess

AI maturity, data readiness, regulatory alignment, team capability

02
2 to 4 weeks

Design

Solution architecture, controls and processes, change management programme

03
4 to 8 weeks

Implement

Production deployment, platform integration, monitoring pipelines

04
Ongoing

Operate

Managed support, compliance reporting, continuous improvement

Results

Case studies

3 differentiators

Why Adaca

Built for regulatory standards

Regulatory compliance built into every layer from day one.

Engineers who stay

4.5+ year average tenure means deep codebase knowledge and regulatory context that does not walk out the door.

AI practitioners, not advisers

We build production AI products including Lovelace, Primrose, Solido, and Lineer.

Explore more solutions

Our engineering

Our capabilities

Three ways we deliver AI for enterprise teams, from readiness assessment to full organisational change.