Banking and financial services
Helping application teams build without waiting on cloud specialists
How a Fortune 100 bank is helping application teams use cloud expertise in their everyday work while keeping security and compliance requirements in place.
A Fortune 100 bank
The bank operates at exceptional scale, with many teams depending on cloud infrastructure to deliver business value. Teaching every team the full depth of its cloud estate, regulatory obligations, and internal standards is impractical. Routing every change through a small group of specialists creates long waits. The bank needs expertise to reach people where they work, while its security, governance, and compliance controls remain intact as AI evolves.
The customer journey
How we're working together.
Business ideas waiting on specialist capacity.
Cloud expertise available across the organization.
Connect intent to expertise
The expertise existed. Getting it to every team was the challenge.
The customer's specialists understood the estate and its requirements. The difficulty was making that knowledge available to the exceptional number of teams that needed it. Every request that depended on a specialist's time added another constraint to delivery.
Our work together focused on bringing that expertise into developer workflows. Cloud context, approved patterns, and organizational requirements could guide the developers and agents doing the implementation. A business idea no longer had to begin with learning the entire cloud estate.
Keep the AI strategy flexible
The AI strategy needed room to evolve.
The team also faced a moving target. Agents and models were evolving quickly, and a decision made today could not become a constraint on tomorrow's delivery. The same was true of the clouds and infrastructure languages its teams used.
We approached the governance layer as something the customer could carry across those choices. Infracodebase makes organizational context and requirements available independently of the agent or model doing the work, giving teams a way to evolve their AI strategy while keeping organizational controls intact.
Make AI useful within the controls
Trust needed evidence behind it.
Generating a plausible change was not enough. The customer needed to check the actual infrastructure against its requirements and explain the decisions later, including when an auditor asked what changed and how it had been reviewed.
We brought deterministic verification into that delivery model so findings and evidence could accompany the code into review. Rules management gave the requirements a lifecycle too. As the business and its obligations changed, the team could evolve the rules guiding development and verification.
Put specialist judgment where it matters
The ambition was to change the time it took to deliver.
These pieces served a business goal. The customer wanted more teams to move valuable work into production without every initiative depending on the same scarce cloud resources. The ambition is to compress a ten-month path into ten weeks.
Bringing requirements into development and evidence into review addresses the time lost discovering constraints, reconstructing context, and correcting avoidable gaps. Specialists keep the approval decisions that need their judgment. Their expertise can reach much further than their individual availability.
The business value
What this means for the team.
The goal is to deliver business capabilities significantly sooner with less dependence on scarce cloud specialists. Expertise becomes available across the organization, AI choices remain flexible, and governance is supported by explicit requirements and verification evidence throughout delivery.
Keep AI choices open
Let teams evolve their agents and models while retaining a consistent layer of organizational context, security, and compliance.
Unlock delivery capacity
Make specialist context available to more developers, helping business initiatives progress without every step becoming a bespoke cloud request.
Shorten time to governance
Reduce late discovery of requirements and repeated review cycles. Give reviewers a concrete change and deterministic verification evidence.
Explore the workflows
Build landing zones and application infrastructure
Establish a new cloud foundation and build the infrastructure your applications and AI agents need.
Explore use caseLet developers use your approved cloud patterns
Make approved infrastructure patterns available through the tools and agents developers already use.
Explore use caseStart with a real infrastructure challenge.
Bring a workflow, an existing stack, or a new cloud project. We can walk through where Infracodebase fits and what your team would review.