Introducing GenieAI’s Agentic Platform for Asset Managers

For every asset manager, there comes a point where investment operations become harder to scale than investment strategies themselves.

Portfolios grow more complex. Asset classes multiply. Reporting requirements evolve. Data flows across custodians, prime brokers, fund administrators, portfolio systems, and internal tools. Even the most sophisticated firms find themselves relying on highly skilled teams to manually reconcile data, validate numbers, investigate discrepancies, and rebuild the same analyses in slightly different forms.

At GenieAI, we know this problem deeply — because we lived it.

Before setting out to build an agentic platform, our team spent years building a portfolio and risk management system for modern asset managers, working closely with hedge funds, investment offices, and institutional investors operating across digital assets, public equities, and alternatives. As we supported real portfolios in production, we gained a firsthand view into the operational work required to keep these organizations running.

What we saw ultimately led us to build GenieAI’s Agentic Platform for Asset Managers.

What We Learned Building Portfolio and Risk Systems

As our portfolio and risk platform evolved, clients increasingly asked for custom analytics, bespoke reports, and tailored operational workflows. They needed reconciliation logic that reflected their strategies, exposure views that matched internal frameworks, and reports that adapted to different stakeholders and committees.

Every request was reasonable. No two asset managers operate the same way.

But the cost of delivering this flexibility using traditional approaches was clear. Each new requirement often meant manual data work, custom logic, one-off scripts, or engineering effort that had to be maintained as requirements changed. Investment operations teams and technology providers alike were spending enormous time just keeping workflows functional.

We realized that the problem wasn’t a lack of tools or data. The problem was that investment operations rely on an operating model that doesn’t scale.

Why Workflow Creation from Prompt Became Essential

If investment operations were ever going to scale, workflows had to be created and adapted without relying on scarce and expensive engineering resources.

That meant enabling fund operators to describe what they needed in natural language and letting the system do the rest. But in financial services, this is not a simple problem.

Creating workflows from prompt requires sophisticated workflow management, dynamic sequencing, and the ability to embed custom analytics when needed. In many cases, that means running Python-based logic for portfolio-specific calculations, risk analytics, or reconciliation rules.

Just as importantly, it requires independent agents to validate the work of other agents. One agent may generate an exposure calculation or reconciliation result, while another agent checks assumptions, logic, and outputs before anything is trusted. This multi-agent design, with agents explicitly checking each other’s work, became foundational to our platform’s architecture.

Without this structure, AI-driven automation in finance simply isn’t reliable.

Why a Purpose-Built Financial Ontology Was Non-Negotiable

As we pushed deeper into AI-driven workflows, another realization became unavoidable.

In financial services — and especially in investment operations — generic AI systems are not sufficient. Financial data is precise, contextual, and unforgiving. Trades, positions, balances, cash flows, P&L, NAV, and fees all have meanings that depend on structure, timing, and intent.

Without a purpose-built ontology, even powerful AI models will misinterpret data, confuse concepts, and introduce subtle errors that are unacceptable in an institutional environment.

We built GenieAI around a proprietary investment-operations ontology that defines how financial data is structured, related, and validated across asset classes. This ontology anchors every agent’s reasoning, dramatically reducing ambiguity and bringing hallucinations as close to zero as possible. In investment operations, this level of precision is not optional — it is required.

Why Human-in-the-Loop Workflows Are Essential

Even with advanced agents and a robust ontology, one principle remained central.

Humans must remain in control.

Investment operations require accountability, transparency, and auditability. Fully autonomous, black-box automation does not work in institutional finance.

GenieAI’s Agentic Platform is therefore built around human-in-the-loop workflows. Operations teams can review, approve, and refine workflows before they run. Outputs can be inspected and overridden. Exceptions are surfaced clearly rather than hidden. Firm-specific logic is explicit and customizable.

AI agents do the execution work. Humans retain judgment and final authority.

This balance is what allows agentic systems to operate safely and effectively in real investment organizations.

What Is GenieAI’s Agentic Platform for Asset Managers

GenieAI’s Agentic Platform allows asset managers to describe operational workflows in natural language and have intelligent agents build, execute, and maintain them.

A workflow might involve reconciling trades and balances across multiple sources, validating P&L and NAV, applying strategy-specific rules, generating summaries, and escalating unresolved issues. Behind the scenes, the platform interprets the request through its financial ontology, constructs a multi-agent workflow, injects custom analytics where needed, validates results through independent agents, and runs the workflow continuously with human oversight.

In effect, AI agents take on the repetitive, time-consuming work that investment operations teams perform every day.

Designed to Work With How Asset Managers Already Operate

The Agentic Platform is designed to fit naturally into how asset managers already run their operations.

Some firms rely heavily on GenieAI’s portfolio and risk management system. Others use a combination of internal tools, third-party platforms, custodians, prime brokers, and fund administrators. Most operate across a hybrid environment that reflects years of evolution.

The platform works within this reality. It connects to existing data and workflows and focuses on automating the operational work that happens around them. Rather than forcing organizations to change systems or operating models, it reduces the manual effort required to keep everything consistent, accurate, and up to date.

The goal is not replacement. It is leverage.

The Future of Investment Operations Is Agentic — and Practical

Investment operations will not be transformed by another dashboard or another system migration. They will be transformed by agentic layers that reduce manual work, adapt to complexity, and integrate naturally into existing operating environments.

GenieAI’s Agentic Platform reflects this shift. It brings together deep investment-operations expertise, purpose-built financial intelligence, and human-in-the-loop agentic workflows to let AI agents do the work that consumes operations teams today.

For asset managers operating in complex, operations-intensive environments, this is a practical path to scale.

If you’d like to see how the platform works in practice, we’d be happy to walk you through it.

Watch the platform preview → https://youtu.be/T-dvLF1hM0A

‍