How to Build Custom AI Business Solutions for FSIs

If you’re a mid-sized to enterprise-level FSI firm searching for custom AI business solutions, it’s likely that:

• You’ve tried off-the-shelf tools, only to find they don’t address your infrastructure, integration, or regulatory challenges.
• You’ve experimented with building in-house but lack the expertise, resources, or time to get beyond prototypes.
• Your proofs of concept have stalled, leaving you without a clear path to production-ready results.

We’ve seen these challenges play out across the industry, and we know what works (and what doesn’t) when it comes to getting AI into production.

​​At Neurons Lab, we co-create custom AI solutions with financial services executives, helping align leadership goals with the right technologies while ensuring regulatory compliance and measurable business outcomes. Our experience spans more than 100 clients, including Fortune 500 companies and governmental organizations, which gives us the perspective to help firms cut through complexity and create AI solutions with confidence.

In this article, we’ll cover

• • Off-the-shelf vs custom AI solutions for FSIs
  • How to build a custom solution for your FSI
  • When it makes sense to build in-house vs working with an AI consultancy
  • How to build custom AI solutions for financial services with Neurons Lab
  • Why firms such as Axa, Visa, PrivatBank, and dAppForge, partner with Neurons Lab for tailored AI solutions
  • How a global asset management firm built a custom AI-driven investing product with Neurons Lab
  • FAQ on custom AI solutions for FSIs

Looking for an AI-exclusive consulting firm that builds custom solutions for financial services firms? Book a call with us today.

Off-the-Shelf vs Custom AI Solutions for FSIs

Off-the-shelf tools require your business to adjust to their limitations, whereas custom artificial intelligence solutions are built to your specifications. The difference becomes clear when we compare simple versus more complex use cases.

Consider a small research team producing a few market reports each week. Their tasks are repetitive, data is publicly available, and compliance risks are low. This makes an off-the-shelf AI tool like ChatGPT or Perplexity Finance a more practical choice than custom development as it enables you to get started quickly and easily. 

A Perplexity Finance market report Image source: Perplexity Finance

More complex use cases, like customer support, present greater challenges. With many off-the-shelf solutions, you can launch a basic chatbot quickly through subscription-based pricing and minimal configuration. This speed is appealing, but the limitations become clear almost immediately:

• Results plateau once the tool reaches the limits of its pre-built functionalities (e.g., a chatbot handles account balance inquiries but fails with loan restructuring or investment questions)

Very simple chatbot responses to banking requests. Image source: Inbenta

• Sharing sensitive customer data with third-party platforms often violates financial regulations, such as GDPR or industry-specific requirements set by regulators like the OCC in the US or the MAS in Singapore.

Third-party access can create hidden entry points for attackers, exposing sensitive customer data and increasing regulatory risk.  Image Source:  Process Unity

• Generic chatbots can’t connect with core systems to handle tasks like claims processing, policy updates, or account retrieval, making them unsuitable for day-to-day operations and leading to a poor user experience

Generic chatbots can’t connect with core systems or software, like Avaloq – Image source: Computerworld.ch

This is where custom AI-powered solutions become essential, especially in financial services, where needs vary widely. 

For example, a wealth management firm may require an AI assistant that delivers data-driven insights and on-demand calculations for relationship managers. A crypto exchange, on the other hand, may want a more conversational chatbot designed for closing deals for its customers. Off-the-shelf tools can’t accommodate these differences and would require integrations and customizations before they are fit for purpose.

These examples highlight why custom AI becomes essential in financial services. While off-the-shelf tools are useful for low-risk tasks, only custom solutions allow you to integrate AI with core systems, meet compliance standards, and scale with your operations. The table below summarizes the differences.

How to Build a Custom Solution for your FSI

Building a custom AI solution for financial services requires a different approach than simply adopting off-the-shelf tools. The process starts small but sets the foundation for long-term scalability.

To get started, consider the following steps:

1. Identify an internal champion. This is typically a technical or engineering person already experimenting with AI, for example, a cloud engineer. This champion can take on a  “Head of AI” role informally and lead the project.
2. Form an internal team. Rather than hiring a brand new team, build it from existing staff with relevant skills (e.g., cloud engineers, data scientists). Aim to deliver your first proof of concept (POC) with current resources.
3. Build a Proof of Concept (POC). Use the infrastructure you already have. If your systems run on AWS, build your POC there to reduce overhead, rather than introducing a new platform for a single use case.
4. Evaluate your POC. Measure performance with clear technical KPIs like latency, accuracy, compliance, hallucination rate, and tone. Too often, teams rely on “vibe checks”—judging a system by how it feels in a quick demo. This leads to stalled projects and poor decisions. Instead, adopt structured testing with defined scenarios and metrics so you know whether a system is truly ready to move forward. 

Taken together, these steps create a strong foundation for experimenting with AI inside your organization and proving early value without heavy investment.

These steps can take you from idea to proof of concept. But moving beyond POCs to scalable, production-ready systems is where many FSIs hit capacity walls. 

For example, you’ve integrated Claude Finance with your infrastructure, but the answers to your prompts take longer than 30 seconds. And when your AI solution finally does respond, it’s not accurate enough and even hallucinates. Even after months of effort, your team may find itself stuck refining the prototype, spending more time troubleshooting than delivering reliable, production-ready outcomes. 

Recognizing when in-house efforts have reached their limits and when external expertise is needed becomes critical for long-term success.

When it makes sense to build in-house vs working with an AI consultancy

Building in-house makes sense if you have an experienced AI team with proven machine learning (ML) projects. Learn more about what it takes to build your own AI engineer to support such efforts.

For many FSIs, however, common challenges create natural points where consultancy support delivers more value:

• Your bootstrapped POCs often stall without deeper AI expertise, leading to missed milestones and unmet expectations. Internal teams may succeed in prototyping (e.g., a basic chatbot) but struggle to scale securely into production systems like payments or core banking.
  • Instead, AI partners bring the expertise to move from POC to production quickly and reliably.
• Your customer data is siloed and scattered across systems, limiting outcomes. High-value AI use cases like personalized advice and customer support require unified data streams. Knowing only that a client is 31 and lives in London isn’t enough; without spending patterns or portfolio details, your AI solution cannot deliver meaningful recommendations.
  • An AI consultancy specialized in financial services knows how to integrate fragmented data sources to create a single customer view, enabling accurate insights and personalization at scale.

Caption: Siloed vs unified customer view.  Image Source:  Stibo Systems

• Your regulatory complexity demands structured testing. AI agents risk unapproved language or inaccurate outputs. For instance, an investment assistant must avoid “risk-free” phrasing and instead use compliant terminology such as “lower risk.”
  • A specialized AI enablement team can design systems with compliance and security controls built in from the start, reducing regulatory and reputational risk.

Even if your firm has strong internal expertise, external partners offering specialized AI services can be valuable when speed or experimentation is the priority. They can take on exploratory or lower-priority projects while your in-house teams remain focused on mission-critical initiatives.

If you decide that external support is the right path, the next step is choosing the right partner. This is where Neurons Lab works with you to design and deliver custom AI business solutions tailored to your regulatory, data, and customer realities.

How to Build Custom AI Solutions for Financial Services with Neurons Lab

Financial institutions face requirements that off-the-shelf tools can’t meet: strict compliance standards, complex integrations, and the need for solutions tailored to their business. As an AI-first enablement firm, Neurons Lab helps you address those gaps directly by building custom AI systems that are secure, compliant, and fit for your specific business purposes. 

Here’s how you can build tailored AI solutions for your FSI with Neurons Lab: 

1. Receive support across the entire AI lifecycle. From executive alignment and use case prioritization (e.g., workshops to decide which use case to target) to building proofs of concept and developing production-grade solutions, we guide you through every stage.
2. Co-create with an experienced AI enablement partner: Let us handle the technical work, including data access and integration, AI model development (e.g., using LLMs, frameworks like LangChain), and custom agent design aligned to your needs.
3. Empower your teams for AI development: We help you embed and scale AI internally by educating teams, onboarding new talent, and supporting sustainable adoption.

Neurons Lab supports FSIs across the full AI lifecycle, from strategy workshops to production deployment.

Our experience with financial services clients shows that moving beyond generic tools to custom solutions delivers real outcomes. 

For example, we partnered with a wealth management firm to create a custom AI solution for its relationship managers (RMs). Previously, RMs spent hours searching through research reports or relying on analysts for insights. Their new custom AI solution combines insights from markets, client portfolios, and product catalogues to surface tailored recommendations instantly and support faster data-driven decisions.

With this solution:

• RMs respond to market changes and client queries confidently with real-time, data-backed insights.

• RMs generate compliant, client-ready proposals in minutes instead of hours.

• Daily reports are delivered in seconds, improving visibility into interactions, performance, and productivity while enhancing overall operational efficiency.

For the firm, the solution is already generating tangible results, such as:

• 70% reduction in RM administrative burden
• 2x increase in client coverage
• 20% improvement in customer satisfaction scores

By focusing on custom AI business solutions, Neurons Lab helps FSIs achieve faster decision-making, reduced risk, and tangible efficiency gains.

In the next section, we outline how you’ll benefit from choosing us as your AI enablement partner.

Why Firms such as Axa, Visa, PrivatBank, and dAppForge Partner with Neurons Lab for Tailored AI Solutions

Financial services firms partner with Neurons Lab because we focus exclusively on AI, bring deep financial services expertise, and deliver custom solutions with speed. 

Unlike off-the-shelf tools, which plateau quickly and fail regulatory checks, we build AI systems that are compliant, secure, and adaptable to your environment. Here’s what you’ll get with Neurons Lab as your custom AI solution partner:

You’ll Go from POC to Production in 2 to 4 Weeks, not Months

Many FSIs stall when moving from proof of concept to production. At Neurons Lab, we bridge that gap with financial services-specific solutions and an exclusive focus on AI. Unlike using tools that stop at basic features (or generalist consultancies that spread teams thin across various industries), you’ll have the expertise of 500+ engineers to help you create custom solutions, extend your AI capabilities, and get faster results.

You can customize our AI accelerators to cover your business needs and specific use cases, such as:

• Customer support chatbots that handle routine queries with a brand-aligned tone.
• OCR-powered document processing for compliance requirements, like know-your-customer (KYC), anti-money laundering (AML) and risk checks.
• Voice agents that manage high call volumes and increase productivity.
• Wealth management copilot that automates compliance and admin tasks and complex workflows, and generates personalized talking points and portfolio recommendations.

Each accelerator includes tested code, reusable components, and proven frameworks. This allows you to start from a strong foundation, avoid months of rework, and move from idea to production in as little as two to four weeks.

You’ll also receive PoC guidance that comes with code implementation, documentation, test cases, and results that prove the value of your use case before you commit major resources. Once your solution is deployed, we’ll monitor its performance, gather feedback, and make ongoing improvements.

Our AI-only focus also means you always have access to the latest developments in machine learning and generative AI, translated into practical outcomes, such as faster innovation cycles and lower infrastructure costs.

You Partner with Experts who Understand Financial Services Inside Out to Build Compliant, Secure AI that Works in your Environment 

Off-the-shelf solutions often fail when they run up against compliance requirements. With Neurons Lab, you’ll get custom AI solutions designed to meet the standards set by regulators, such as GDPR, OCC, MAS, and ISO/IEC 27001, while ensuring your data never leaves secure environments.

Our teams know financial services inside out, with experience that ranges from fraud detection and AML/KYC automation to credit risk modeling and regulatory reporting. With our expertise, you can address data fragmentation early by cleaning, structuring, and unifying your data through streamlined extract, transform, and load (ETL/ELT) workflows. 

Beyond technical delivery, you’ll receive governance frameworks that cover AI policies, ethical standards, and compliance guardrails. This ensures your systems pass regulatory review and continue to perform reliably in real-world conditions.

This means you’ll have production-ready AI systems with the flexibility to scale into advanced use cases without costly rebuilds. With enterprise-grade security controls in place, your data remains private, outputs remain accurate, and intellectual property is fully protected.

Neurons Lab’s tech stack aligns with industry regulations and offers enterprise-grade trust and safety features

You get Flexible Frameworks and End-to-End Support to Scale Fast 

Off-the-shelf tools plateau quickly when scaling. They can’t adapt to your unique workflows or gain adoption across diverse teams. Even after a custom solution goes live, you may often struggle with scaling due to skills gaps, resistance to AI adoption, or poor testing practices. With Neurons Lab, you can address these head-on with:

• Structured evaluation frameworks that replace typical “vibe checks” with systematic testing for accuracy, hallucination monitoring, and brand alignment. By designing test scenarios with multiple questions per case and tracking metrics until thresholds are met, you know if your AI is truly ready for large-scale rollout.
• AI literacy and training programs. From GenAI fundamentals to role-specific workshops, we help your teams see AI as a co-pilot that enhances their work, not a threat.
• Ongoing support with post-deployment workshops and continuous improvement cycles to ensure adoption sticks and your systems grow more effective over time.

This combination of technical rigor and organizational enablement ensures your AI solutions scale responsibly, something off-the-shelf products weren’t designed to do.

How a Global Asset Management Firm Built a Custom AI-driven Investing Product with Neurons Lab

A global asset management and investment advisory firm came to us with four key goals:

• Improve out-of-sample portfolio performance for more reliable results.
• Accelerate strategy development, cutting the time between ideation and implementation.
• Strengthen risk management in backtesting and scenario analysis.
• Build a scalable, secure cloud environment to handle large volumes of financial data and complex computations.

The firm already knew an off-the-shelf solution wouldn’t be able to help them achieve these complex goals and tasked us with designing and deploying a custom AI solution. This included an advanced backtesting framework to improve confidence in out-of-sample performance. We also implemented market structure algorithms to deliver more accurate estimates of market data and fundamentals. 

To support these capabilities, we built a scalable cloud architecture on AWS that runs large language models securely with high availability across multiple zones.

Neurons Lab’s scalable cloud architecture running LLMs securely with high availability across multiple zones.

As a result, the firm achieved measurable improvements across all four goals:

• Portfolio performance became more reliable, with better risk-adjusted returns
• Strategy development cycles became shorter, enabling the firm to deploy new strategies faster
• Risk management was strengthened through more accurate scenario analysis

And with AWS-based architecture providing a secure, scalable foundation, the firm is now able to meet current demands while maintaining high levels of security and compliance. Discover more.

Work With an AI-exclusive Partner for Compliant Custom AI Solutions

When you’re dealing with complex use cases, regulatory requirements, and integration challenges, only a tailored approach delivers measurable results. Working with an AI-exclusive partner that understands the financial services industry is crucial, as they can help you build custom AI solutions that are compliant, secure, and aligned with existing workflows, giving your firm a clear competitive advantage.

Just as important is how they work with you. The right partner provides support at every stage—from identifying use cases to deployment and post-launch support—so you can innovate faster, reduce costs, streamline operations, and scale AI technologies confidently organization-wide.

If you want to move beyond off-the-shelf tools and build custom AI solutions that meet your unique needs and drive measurable results, contact us today.

FAQs on custom AI solutions for FSIs 

Custom vs Off-the-Shelf AI: Which is Better for FSI Businesses?

Off-the-shelf AI is faster and cheaper to deploy for common tasks, while custom AI is more flexible, scalable, and tailored to your specific needs. The right choice depends on whether speed or strategic fit matters more to your organization. In the financial services sector, where compliance and data security are critical, custom AI often delivers the control and scalability firms need.

When Does it Make Sense to Use off-the-shelf vs Custom AI Solutions for Financial Services Firms?

It makes sense to use off-the-shelf AI for simple use cases where speed and cost are your main concerns, and where ease of deployment matters more than meeting complex regulatory requirements. Custom AI solutions are ideal for more complex use cases like AI agents, and when your business has strict compliance requirements and advanced integration needs.

What are Custom AI Agents?

Custom AI agents are specialized AI systems designed to perform specific autonomous tasks based on an organization’s unique needs. Unlike generic off-the-shelf AI tools, agents are trained on a targeted knowledge base and given the ability to interact with specific tools and systems. This allows them to independently complete complex, multi-step actions, such as automating a business process or providing tailored customer service.

How Much Does it Cost to Build an AI Solution as an FSI?

Estimates place the cost of building an AI solution at $50,000 to $100,000, though this can vary widely depending on project complexity, scope, data needs, infrastructure, and whether it’s built in-house or outsourced.

For financial services, you should also take into account the cost of regulatory class and governance (e.g., EU AI Act high-risk use cases like credit scoring/AML need risk management, data/record-keeping, etc.) as well as documentation, testing, monitoring, and board-level accountability under ESMA/MiFID guidance.

How Long Does it Take to Build a Custom AI Business Solution for an FSI?

It can take anywhere from a couple of weeks to several months to build a custom AI solution for a financial services firm. There are various factors to consider, including the complexity of your project, your data readiness, integration needs, compliance requirements, and governance needs. Whether you build in-house or work with an AI consulting partner is also a factor.