Financial Services
Control AI costs. Govern AI risk. Run it all inside your own cloud.
Engineering teams at banks and insurers are spending millions on AI coding tools with no ROI visibility. Every AI-assisted decision needs an audit trail. And your data can’t leave your perimeter.
AI spend is growing. Visibility isn’t.
Hundreds of developers at your bank or insurer are using Cursor, GitHub Copilot, and Claude to modernize core systems. Token bills are growing fast — and in most cases, nobody can tell you which teams are getting ROI and which are burning budget on tools that haven’t changed their output. The engineering investment is real. The visibility isn’t.
Meanwhile, your CISO and model risk team are asking a different set of questions: who approved these tools, what data are they touching, and where’s the audit trail? And your analysts and bankers are using AI tools that never went through procurement — on client portfolios, on regulated data, on decisions that examiners are starting to ask about. Every other platform that could help you answer these questions processes your data through their cloud. For a financial institution, that’s a non-starter.
By the Numbers
Engineering teams at major financial institutions are spending 3–5x more on AI coding tools than their original budgets — with no platform to show what it returned.
The firms that govern AI spend and output from the same platform are the ones that keep deploying. The rest stall at the board meeting.
Your data never leaves your infrastructure.
Olakai deploys inside your cloud tenancy or on your own hardware — AWS, Azure, GCP, or on-premises. No code, no prompt data, no AI interaction logs are processed outside your perimeter. For financial institutions operating under SR 11-7, data residency mandates, and internal security policy, this isn’t a feature — it’s the prerequisite. Most AI governance vendors are SaaS-only. That disqualifies them before the conversation starts.
Two lenses, one platform
Olakai is one platform, one data model, one set of controls. Olakai Agentic and Olakai Assistive are the two lenses you use to look at it — they share data, share context, and share Kai. What you see in one lens is immediately visible in the other.
Olakai Agentic
Measure AI coding tool ROI and govern every autonomous agent decision, in one lens. Token spend by provider, team, and developer. Run-rate forecasting before the invoice arrives. Full audit trails for KYC automation, fraud triage, and credit workflows — generated automatically, not assembled by hand the week before. All inside your own infrastructure.
Olakai Assistive
Track every AI tool your analysts, bankers, and relationship managers are using — approved and shadow. License utilization, time saved per workflow, client data exposure detection. The visibility your model risk team needs before the examiner asks.
Olakai Agentic — Spend control
See the overrun before it happens.
Olakai tracks token spend across every AI coding tool — Cursor, GitHub Copilot, Anthropic, OpenAI — normalized by provider, team, and developer. Run-rate forecasting projects month-end costs from a 7-day trailing average, so a 3x overrun shows up as a trajectory problem weeks before it becomes an invoice. All of it running inside your own infrastructure.
- Token spend by provider, team, and developer in one normalized view
- Run-rate forecast with trajectory alerts at 50%, 80%, and 100% of budget
- 30-day spend simulation to model the impact of limit changes before you make them
Olakai Agentic — Audit trail
The audit trail your examiner will ask for. Ready before they ask.
For every autonomous agent running inside your institution — KYC workflows, fraud triage, credit decisioning — Olakai logs every input received, every tool called, every decision made, and every outcome. When your model risk team or regulator needs documentation, it exists and it’s current. Not reconstructed from logs the week before the review.
- Full execution log: inputs, tools called, decisions, outcomes — per agent run
- Policy enforcement at the workflow level — violations caught before the action is taken
- SR 11-7 and EU AI Act compliance reports generated automatically, inside your perimeter
The KPIs that matter in financial services
Every pillar gives you a different lens on AI performance. Here are the metrics Olakai actually measures — across every vendor, every team, inside your own infrastructure.
Olakai Agentic
Olakai Assistive
Why Measurement Changes Everything
Teams burn through annual AI coding budgets in four months when they’re watching monthly actuals instead of run-rate trajectory. Olakai catches the overrun before it happens — spend by team, by provider, by developer, in one view, inside your own infrastructure.
When the examiner asks for an AI audit trail, your team shouldn’t spend two weeks assembling it from logs, emails, and screenshots. Olakai logs every agent decision in real time. The report exists before anyone asks for it.
Analysts and bankers are using AI tools you’ve never heard of on client data you can’t trace. Olakai makes every tool visible, quantifies the risk, and lets you bring high-value shadow AI inside the perimeter with proper oversight — instead of blocking everything and losing the productivity gains.
Ask your AI program a question. Get a reasoned answer.
Kai synthesizes every Olakai data source — spend, adoption, agent logs, governance status — and answers in plain English. With the reasoning shown and the data behind every number.
- “Which AI coding tools are our engineering teams spending on, and what is each one returning in velocity gains?”
- “Which AI tools are touching client data right now, and which of those are unsanctioned?”
- “Show me the full audit trail for every agent decision in our KYC workflow over the last 90 days.”
Illustrative conversation on the right: pick a seat, ask, and watch Kai reason over the same data every Olakai dashboard uses.