One thing I’ve noticed over the past couple of years of working at ECI: I spend an inordinate amount of time talking to customers. It’s the teams on the ground: compliance leads, product managers, developers…the folks who actually are trying to make AI work every day.
And despite being scattered across dozens of firms of different sizes and industries, it’s the same story across the board.
Everyone’s been told they need an AI strategy. So they buy tools. Copilot. ChatGPT Enterprise. Experiment with agents. Run some pilots. See some early successes. Lots of curiosity. But after a short period, the questions start coming and they come fast: from senior leadership, from regulators, from LPs.
Who has access to which models? Where is our data going? Are we compliant with SEC Reg S-P, DORA, the EU AI Act? What happens if a model provider goes down? Who owns the prompts? Who’s monitoring output quality over time? Who knows what we’re spending?
Most firms simply don’t have answers to these questions. Not because they aren’t spending money, but because nobody has built the structure to actually manage and operate AI at scale responsibly…yet.
ELLA was built to solve that problem.
So what is ELLA? ELLA Is a Managed Service For AI
ELLA is defined as our Enterprise Lifecycle & Leverage Architecture. It is a multi-tiered approach with a managed service built around how regulated firms actually use AI.
We think about this similar to how most firms think about managed cybersecurity or even managed cloud, except it’s been built from the ground up for AI.
We operate it. You use it. And it covers everything from understanding where you are today, putting the right controls in place, building real use cases, and keeping everything running cleanly and compliantly over time.
It is broken down into four Pillars:
ELLA Enable: Getting Your Foundation Right
This is where most firms begin their journey, and for good reason.
Before AI can actually create value, you need to understand your data and workflows (and most importantly your people). Where are they trying to add value? Where could AI help? What tools make sense?
Includes:
* AI readiness assessments
* Role-based training for all tools (Copilot, Claude, ChatGPT, etc.)
* Adoption support
The great thing about starting here? If done correctly, your teams will be ready for whatever tool comes along next. Enablement isn’t tool-specific.
We also provide bespoke training programs specifically for financial services use cases. Enabling your portfolio managers is vastly different than enabling marketing or operations teams. We get that.
The fact of the matter is: AI licenses have a terrible habit of going underused after 90 days. Enable is how we change that.
ELLA Protect: Governance and Compliance That Actually Works
Regulated firms don’t need AI they can use. They need AI they can prove they use appropriately.
This layer makes AI usable. The Protect stack maps directly to common regulatory frameworks (SEC, FINRA, DORA, EU AI Act) and translates those requirements into something teams can operate.
Included are:
* AI risk assessments
* Data classification / DLP
* Monitoring via ELLA IQ (with pre-built logic for many common high-risk scenarios)
Instead of panicking about regulations as they’re announced, this puts processes in place that are defensible from day one.
ELLA Build: Where AI Powers Actual Workflows
With the basics in place, this is where the AI transformation happens. We focus on turning AI into measurable workflows.
* Data readiness
* Agent design / prompt engineering
* System integration
But just as important is maintaining those workflows over time.
* Performance monitoring
* Drift detection
* Model management
Building something with AI is easy. Operating that something for a year+ is hard. Every firm will go through this at some point. And that’s where most internal projects fail.
ELLA Gateway: Doing AI At Scale Means Running AI
Gateway is where it all comes together. At scale. With controls.
Gateway includes:
* Multi-model routing / failover
* Security / access controls
* Usage / cost visibility
* Performance optimization
If a model provider has an issue, employee's workflows keep running. If a new, better model comes along, you can plug it in with no disruption. If someone starts abusing the system (costing your firm money or exposing it to risk), you’ll know before rather than after the fact. It also handles routing to specific models based on your governance rules. So if “Finance” can use GPT-4 but “Marketing” can only use Assistant, we’ve got you covered.
Why Can’t I Just Build This Myself?
I’ve heard this exact question from dozens of firms. Some waste no time asking. Others spend far too long trying to figure it out themselves.
Operating AI at scale isn’t hard if you only care about one or two of these pillars. Ensuring your models won’t get your firm fired/regulated out of business? Sure. Easy enough for internal teams with enough resource…
Who’s looking at cost & usage? Who’s planning for tool sunset? Who’s continuing to enable the business as tools evolve?
Even large firms with tons of resources are struggling to operate AI safely at scale. ELLA is built to shoulder that burden so your teams can focus on what AI can actually do to differentiate your business.
In practice, we generally see customer engagement follow similar path:
1. Start with Enable: Understand where you are today and develop a clear roadmap for the future
2. Layer on Protect: Once you have a baseline you can start to implement governance & monitoring
3. Build: Once you have “Enable & Protect” covered we spend a lot of time helping firms build meaningful use cases
4. Run everything through Gateway: once you’ve got real use cases, we help you operate them at scale
Over time, what begins as experimentation becomes actually running AI like you do everything else.
Who Is This For?
Everyone.
ELLA is built for regulated investment firms. That means asset managers, alternatives, wealth firms, family offices. Essentially any team responsible for deploying AI across the organization and needing to do so in a responsible way.
If you’re in a situation where you’re being asked “what’s our AI strategy, and how is it governed?” and don’t know how to answer, this is basically that answer instantiated as a team.
The firms that win with AI are not going to be the ones doing the flashiest projects. They’ll be the ones running AI consistently and safely for years, stuck in a way that senior leadership and regulators are familiar with.
Sounds like an operating model problem to me. We’re solving it with ELLA.
