Vertical AI Platforms vs. General-Purpose LLMs: Making the Build vs. Buy Decision in Financial Services

The Platform Paradox: When 90% Goes Unused

The financial services landscape is experiencing a proliferation of vertical AI platforms, each promising to revolutionise how investment banks, private equity firms, and asset managers operate. Tools like Rogo and Model ML have emerged from stealth with significant funding and bold claims about transforming financial workflows. Meanwhile, general-purpose large language models like Claude continue to advance at remarkable pace, offering increasingly sophisticated capabilities out of the box.

For executives navigating their organisation's AI transformation, the question isn't whether to adopt AI - it's how. Should you invest in purpose-built vertical platforms that promise everything but deliver value on only a fraction of features? Should you leverage general-purpose LLMs that require more configuration but offer complete flexibility? Or should you build bespoke solutions that match your exact workflows?

A recent conversation with a boutique investment bank crystallised this challenge perfectly:

"We're looking at Rogo, and it's really good at doing certain things, but we don't do those things. It's like paying for a product where I use 10% of what it does - is that the best way to think about it?"

What Are Vertical AI Platforms Actually Delivering?

Vertical AI platforms represent a new category of enterprise software specifically architected for financial services workflows. Unlike general-purpose tools adapted for finance, these platforms are designed from the ground up with financial use cases at their core - at least in theory.

Rogo has positioned itself as the secure generative AI platform for elite financial institutions. Founded by former bankers, the platform fine-tunes OpenAI's models on vast financial datasets including S&P Global, FactSet, and Crunchbase, enabling it to search and analyse over 50 million financial documents. The platform integrates directly into workflows for pitch deck creation, due diligence automation, and precedent transaction analysis, with the company claiming analysts save up to 10 hours weekly.

Model ML takes a different architectural approach, positioning itself as an AI-native workspace that replaces traditional productivity suites. The platform features a voice-first interface and provides its own versions of spreadsheets, presentations, and documents - all connected to an organisation's data sources, CRMs, and databases.

Both platforms share common characteristics: deployment on customers' own infrastructure for security, granular permission controls, comprehensive audit trails, and integration with firms' proprietary data. They've attracted significant investment and blue-chip customers, suggesting genuine demand for finance-specific AI tooling.

But here's the critical insight from our work with financial services firms: demand for the concept doesn't equal satisfaction with the implementation.

The Utilisation Problem: Why Off-the-Shelf Falls Short

The fundamental challenge with vertical platforms isn't their capability - it's their assumption that all financial services firms operate identically. Consider these real-world scenarios we've encountered:

The Reporting Conundrum: One firm implemented a leading CRM solution designed for investment banking. The platform could do "amazing stuff," but couldn't generate the simple reports they actually needed: conversion rates, win/loss analysis by banker, pipeline by stage. The MD ended up exporting data to Excel weekly because the platform's sophisticated analytics didn't answer their basic questions.

The Template Trap: Vertical platforms come with pre-built templates for pitch decks and financial models. But every firm has its own house style, its own analytical approach, its own way of presenting information. When platforms can't match your specific templates, analysts spend time reformatting outputs - negating the time savings.

The Integration Illusion: Platforms promise seamless integration with your data sources. In practice, firms moving from legacy systems (like IgniteTech to SharePoint) find that "AI-ready" platforms can't actually access their data without significant middleware development.

The Competitive Convergence: Perhaps most critically, when every firm uses the same platform with the same fine-tuning, where's the differentiation? If both you and your competitors are using Rogo to generate pitch decks, you're both drawing from the same well of insights.

As one boutique bank partner noted:

"Everybody else has it, so what's giving us the edge?"

The Hidden Costs of "Comprehensive" Solutions

Beyond direct subscription costs, vertical platforms impose hidden expenses:

Change Management Overhead: Introducing a new platform means training teams, adjusting workflows, and managing resistance. When the platform only addresses 10% of actual needs, adoption becomes a constant struggle.

Vendor Lock-in: Once you've built workflows around a platform's specific features, switching becomes prohibitively expensive. You're locked into their roadmap, their pricing increases, their feature decisions.

Innovation Constraints: Platforms move slowly compared to frontier AI development. When Claude or GPT releases groundbreaking capabilities, platform users wait months for integration - if it comes at all.

The Build Alternative: Modular Development on General-Purpose LLMs

Leading firms are discovering a different path: building modular, bespoke workflows on top of general-purpose LLMs. This approach offers several advantages:

Precise Fit: Instead of adapting to a platform's assumptions, you build exactly what you need. A compliance bot that understands your specific KYC requirements. A presentation generator that uses your templates. A lead generation system that applies your proprietary insights.

Incremental Investment: Start with simple wins - perhaps a £5,000 proof of concept for automated compliance queries. If it works, expand. If not, pivot. No massive upfront commitments.

Competitive Differentiation: Your proprietary methods, unique data assets, and distinctive workflows become embedded in your AI systems. Whilst competitors use generic platforms, you're building genuine competitive advantage.

Future Flexibility: As AI capabilities evolve, you can immediately incorporate improvements. New model releases, novel techniques, emerging capabilities - all accessible without waiting for vendor updates.

A Framework for Decision-Making

Rather than viewing this as a binary buy-versus-build choice, consider this practical framework:

Start with Workflow Mapping

Before evaluating any solution, map your actual workflows. What do your teams do daily? Where are the repetitive tasks? What's truly standardised versus what requires judgement? Often, firms discover their workflows are more unique than they assumed.

Assess Your Starting Position

• If you have limited AI experience: Begin with general-purpose LLMs for experimentation. Build organisational muscle before committing to platforms.

• If you have clear, repetitive workflows: Consider platforms, but negotiate proof-of-concept periods to validate actual utilisation.

• If you have proprietary methods: Lean towards bespoke development to preserve competitive advantage.

Consider Hybrid Approaches

The most sophisticated strategies often combine elements:

1. Phase 1: Use Claude or GPT directly for exploratory work and to identify high-value use cases

2. Phase 2: Build lightweight automations for proven workflows using general-purpose LLMs

3. Phase 3: Evaluate whether platforms add value for specific standardised processes

4. Phase 4: Develop bespoke solutions for competitive differentiators

Calculate True ROI

When evaluating platforms, consider:

• What percentage of features will you actually use?

• How much customisation will be required?

• What's the opportunity cost of vendor lock-in?

• Could you achieve 80% of the value with 20% of the investment through bespoke development?
  
  

The Goldman Sachs Lesson

Why are firms like Goldman Sachs reportedly gaining advantage through AI? Not because they're using better off-the-shelf platforms, but because they're building bespoke solutions. They have AI agents monitoring markets for specific triggers, generating alerts through their lens, creating analyses using their methodologies.

Smaller firms often assume they can't compete with this approach. But modern AI development has democratised capability. With tools like Claude, development frameworks like Replit, and modular architectures, boutique firms can build sophisticated solutions without massive technology teams.

The key is starting simple. One firm we work with began by building a basic KYC decision tree - a two-day project that now saves hours weekly. They've since expanded to automated presentation generation, intelligent CRM reporting, and proactive lead identification. Each module cost less than a month of enterprise platform subscription.

Looking Forward: The Modular Future

The future of AI in financial services isn't monolithic platforms trying to be everything to everyone. It's modular, composable systems that combine:

• General-purpose LLM capabilities for reasoning and analysis

• Lightweight integrations with existing data sources

• Bespoke workflows matching your specific processes

• Proprietary insights embedded in custom agents

This approach requires thoughtful planning and expert implementation, but delivers something platform vendors can't: a system that does exactly what you need, nothing more, nothing less.

As one client reflected after building their bespoke solution:

"We spent a year evaluating platforms that could do amazing things we didn't need. We built what we actually wanted in two months. The ROI calculation wasn't even close."

Making Your Decision

The choice between vertical platforms and bespoke development isn't about which is objectively "better" - it's about what fits your organisation's specific needs, capabilities, and competitive strategy.

If you're exploring AI transformation, start by asking:

• What workflows would genuinely benefit from AI augmentation?

• How unique are our processes compared to industry standards?

• Where does AI touch our competitive advantage?

• What's our appetite for innovation versus standardisation?

At QuantSpark, we specialise in helping financial services firms navigate these decisions through our Buy vs. Build assessments.

We've seen the full spectrum - from successful platform implementations to transformative bespoke builds - and understand the nuances that determine success.

 

Whether you're considering platforms like Rogo and Model ML, exploring what's achievable with Claude and general-purpose tools, or thinking about bespoke development, the key is starting with clear use cases and measurable outcomes. The organisations that will lead aren't those with the most expensive tools, but those making thoughtful decisions about where to standardise, where to customise, and how to build genuine competitive advantage through AI.

For a confidential discussion about your AI strategy and how to evaluate the build versus buy decision for your specific context, contact our team at QuantSpark.