Many conversations about AI adoption start with a question that sounds logical, but is fundamentally flawed:

Should we use ChatGPT or build our own AI model?

Framing it as an either-or decision is a false dichotomy. ChatGPT and custom AI models are not competing approaches; they represent complementary layers of a modern AI strategy.

ChatGPT, or any large language model offered as a service, provides an accessible and powerful foundation for exploring generative AI and rapidly testing ideas. Custom AI solutions, on the other hand, extend those capabilities by aligning them with an organization’s unique data, processes, and business goals. When combined, they unlock far greater strategic value than either could alone.

The real question isn’t which one to choose, but how to combine both effectively to create AI solutions with measurable business impact.

ChatGPT: Powerful, But Not Personalized

ChatGPT has become the public face of generative AI. It enables professionals to draft reports, analyze data, and summarize information in seconds. For individual productivity, it’s a remarkable tool, intuitive, cost-efficient, and continuously improving. Its accessibility has accelerated AI adoption across industries, helping teams experiment, learn, and unlock new ways of working faster than ever before.

However, as organizations move beyond experimentation and seek AI integration with business workflows, ChatGPT’s limitations become evident. It’s a general-purpose platform, designed for broad use rather than specific enterprise needs. It doesn’t offer the data governance, workflow customization, or contextual understanding required to drive measurable business outcomes.

While ChatGPT is trained on vast amounts of public data, it lacks access to a company’s proprietary knowledge, internal systems, and domain expertise, the very elements that make AI truly strategic. Understanding these boundaries is essential for building solutions that not only generate insights but also deliver tangible business impact.

Custom AI Model: Turning Technology into a Competitive Advantage

Building your own enterprise AI system, whether powered by OpenAI models, Google Gemini, or other large language models (LLMs), transforms AI from a productivity tool into a strategic business engine.

Unlike public interfaces, custom AI systems connect directly to your internal data, adapt to your industry’s context, and evolve alongside your business.

Here’s how an enterprise-grade AI solution works:

1. Centralized and Updated Knowledge

A custom AI solution connects directly to your company’s data sources (CRM, ERP, documents, reports), keeping information centralized, updated, and version-controlled. This creates a single source of truth that evolves over time.

2. Memory and Continuous Learning

A tailored system can learn from past interactions, store company knowledge, and continuously improve accuracy, building a living corporate brain that compounds value with every use.

3. Workflow Automation

While ChatGPT answers questions, enterprise AI automates processes, from verifying information and sending emails to updating databases. It doesn’t just respond; it executes. This enables end-to-end process automation, driving operational efficiency at scale.

4. Collaboration at Scale

A production-ready AI platform enables team collaboration, with roles, permissions, and interfaces tailored to different departments, so marketing, operations, and finance can all benefit from shared intelligence.

5. Security and Compliance

Enterprise AI operates within your environment, respecting internal policies and ensuring data confidentiality. You control access, integrations, and compliance with industry standards—critical for sectors like healthcare, fintech, and retail.

6. Brand and Voice Personalization

Your own AI can speak in your brand’s tone, follow your internal communication rules, and align with your corporate values, delivering consistent and on-brand experiences across all interactions.

The Power of Proprietary Data

The real differentiator between ChatGPT and a custom AI model lies in proprietary data.

Every company has its own language, datasets, and internal knowledge. When AI systems are trained or fine-tuned using that data, they gain context that no public model can replicate.

Organizations typically achieve this through RAG,  linking AI models to internal databases so they can retrieve accurate and current company information. With a Retrieval-Augmented Generation (RAG) system, you can connect the model directly to your own internal data, making it far more scalable than manually uploading files through the ChatGPT interface. A RAG-based knowledge base supports larger volumes of documents, version control, and the ability to customize preprocessing, whether it’s handling text, images, or tables. Without that context, even the most powerful model can produce generic or inaccurate outputs.

This technique ensures the model truly understands your business, your products, terminology, workflows, and decision patterns, turning it into an AI that performs like an expert in your organization.

How Marvik Can Help You

Using ChatGPT is a great first step—it helps teams explore what’s possible with generative AI. But building your own enterprise AI solution is what truly turns artificial intelligence into a lasting competitive advantage.

At Marvik, we help organizations move beyond experimentation by designing and deploying custom AI systems tailored to their specific goals, data, and workflows. As a hands-on AI consulting firm, our focus is on creating production-ready AI that integrates seamlessly with existing operations and delivers measurable business outcomes.

Our work typically involves:

• Developing AI tools powered by large language models adapted to each organization’s context
• Connecting AI capabilities directly to business systems for automation and data-driven decision-making‍
• Guiding companies through AI readiness, governance, and scalability to ensure sustainable adoption

Whether you’re exploring generative AI consulting, looking to extend your internal AI team, or aiming to operationalize AI across departments, the goal remains the same: to move from testing AI to achieving business impact at scale—securely, efficiently, and strategically.