How AWS Custom Model Program Helps Organizations Build Smarter, Tailored AI Solutions

Every organization has a unique combination of data, processes, and values. That’s why one-size-fits-all AI solutions often fall short. Recognizing this, Amazon Web Services (AWS) launched the Custom Model Program within its Generative AI Innovation Center—a comprehensive initiative that helps companies design and optimize AI models tailored to their specific business context.

Since its launch in 2024, the program has partnered with global enterprises and startups across industries such as legal services, financial operations, healthcare, software engineering, telecommunications, and manufacturing. The result: AI systems that are faster, more efficient, and deeply aligned with each company’s identity—delivering better accuracy, brand consistency, and lower operational costs.

As organizations evolve beyond basic chatbots and proof-of-concept projects, AWS is helping them move toward advanced AI personalization and optimization—beyond prompt engineering or simple retrieval-augmented generation (RAG).

From Innovation to Impact: The Power of Custom AI

The AWS Generative AI Innovation Center supports customers at every stage—from defining business goals to optimizing hardware performance. This approach ensures that customization efforts deliver measurable impact across cost, speed, and scalability.

A great example comes from Cosine AI, creator of an AI developer platform. By fine-tuning Amazon Nova Pro, a foundation model from AWS, using Amazon SageMaker, Cosine AI achieved:

• 5× more A/B testing capability
• 10× faster developer iteration cycles
• 4× overall project speed improvement

These results demonstrate how strategic model customization can unlock exponential efficiency—especially as companies transition to agentic systems, where speed, precision, and coordination are critical for success.

Five Expert Tips for Maximizing ROI from Custom AI Development

1. Start with Business Goals, Not Technology

Successful AI projects begin with clear business outcomes, not just technical ambition. The AWS Innovation Center has found that projects built with a “working backwards” approach—starting from measurable goals—achieve a 65% success rate, with some launching in under 45 days.

By focusing on impact first (e.g., reducing customer service time or improving fraud detection), teams ensure that every technical decision aligns with strategic business priorities.

2. Choose the Right Customization Approach

Not every AI project requires deep model re-training. AWS helps customers begin with simpler, lower-cost optimization methods—such as prompt engineering and retrieval-augmented generation (RAG)—before moving to deeper customization.

Key approaches include:

• Supervised fine-tuning – Enhances tone, phrasing, and accuracy for specific tasks (as Volkswagen did to improve brand consistency from 55% to 70%).
• Model efficiency tuning – Optimizes inference speed and infrastructure use, reducing costs.
• Reinforcement learning – Refines model behavior to reflect organizational preferences.
• Continued pre-training – Builds stronger domain or language expertise (e.g., Athena RC’s Greek-language models Meltemi-7B and Llama-Krikri-8B).
• Domain-specific foundation model development – For highly specialized industries, like TGS in energy exploration, which used AWS infrastructure to detect geological structures faster and at lower cost.

3. Define Success Metrics Early

AI success must be measurable. AWS encourages organizations to set both business-level and technical-level KPIs.

Examples include:

• Accuracy, latency, and cost per request
• Brand alignment or tone consistency (as Volkswagen achieved)
• Review or decision-making speed (as Robin AI improved by 80%)

AWS also supports developing automated evaluation methods using “LLM-as-a-judge” frameworks to ensure continuous quality improvement.

4. Optimize Hardware and Infrastructure

Custom AI is not just about algorithms—it’s about running them efficiently. Companies like TGS used AWS’s high-performance GPU infrastructure to process petabytes of seismic data with >90% GPU efficiency, cutting delivery times from weeks to days.

Similarly, Synthesia, a leader in AI video generation, collaborated with AWS to optimize its deep learning pipelines—reducing latency and boosting throughput by 29%.

With Amazon Bedrock, organizations can take advantage of pre-optimized environments for training and inference, achieving scalability without sacrificing performance.

5. One Size Doesn’t Fit All

Every use case is unique. While large general-purpose models are powerful, many business scenarios require smaller, faster, or domain-specific AI systems. By adopting a modular AI architecture—using different models for different functions—organizations can balance accuracy, cost, and agility.

Services like Amazon Bedrock make it easy to integrate and switch between multiple model families, including custom-tuned versions, through a unified API—future-proofing your AI infrastructure.

AWS Innovation Center: Turning AI Vision into Business Value

The AWS Generative AI Innovation Center’s Custom Model Program provides end-to-end expert support—covering:

• Model selection and tuning
• Hands-on technical collaboration
• Seamless integration with AWS services like Amazon SageMaker and Bedrock
• Full data control within the customer’s secure AWS environment (VPC)

This approach has helped organizations across industries accelerate time-to-market, reduce costs, and unlock the full potential of generative AI.

As an official AWS Partner, Softprom helps enterprises in Europe and beyond access AWS expertise, deploy AI responsibly, and build tailored AI models that deliver measurable business outcomes.

Conclusion

Custom AI is no longer a luxury—it’s a competitive necessity. Through AWS’s Custom Model Program and the Generative AI Innovation Center, organizations can design AI systems that reflect their brand, optimize operations, and scale innovation responsibly.

With Softprom and AWS, your business can build AI that truly matches your DNA—driving efficiency, insight, and sustainable growth.

Contact Softprom to learn how AWS can help your organization develop customized AI models that deliver measurable impact and align with your unique business goals.