InterVision accelerates AI development using AWS LLM League and Amazon SageMaker AI

Cities and local governments are continuously seeking ways to enhance their non-emergency services, recognizing that intelligent, scalable contact center solutions play a crucial role in improving citizen experiences. InterVision Systems, LLC (InterVision), an AWS Premier Tier Services Partner and Amazon Connect Service Delivery Partner, has been at the forefront of this transformation, with their contact center solution designed specifically for city and county services called ConnectIV CX for Community Engagement. Though their solution already streamlines municipal service delivery through AI-powered automation and omnichannel engagement, InterVision recognized an opportunity for further enhancement with advanced generative AI capabilities.

InterVision used the AWS LLM League program to accelerate their generative AI development for non-emergency (311) contact centers. As AWS LLM League events began rolling out in North America, this initiative represented a strategic milestone in democratizing machine learning (ML) and enabling partners to build practical generative AI solutions for their customers.

Through this initiative, InterVision’s solutions architects, engineers, and sales teams participated in fine-tuning large language models (LLMs) using Amazon SageMaker AI specifically for municipal service scenarios. InterVision used this experience to enhance their ConnectIV CX solution and demonstrated how AWS Partners can rapidly develop and deploy domain-specific AI solutions.

This post demonstrates how AWS LLM League’s gamified enablement accelerates partners’ practical AI development capabilities, while showcasing how fine-tuning smaller language models can deliver cost-effective, specialized solutions for specific industry needs.

Understanding the AWS LLM League

The AWS LLM League represents an innovative approach to democratizing ML through gamified enablement. The program proves that with the right tools and guidance, almost any role—from solutions architects and developers to sales teams and business analysts—can successfully fine-tune and deploy generative AI models without requiring deep data science expertise. Though initially run as larger multi-organization events such as at AWS re:Invent, the program has evolved to offer focused single-partner engagements that align directly with specific business objectives. This targeted approach allows for customization of the entire experience around real-world use cases that matter most to the participating organization.

The program follows a three-stage format designed to build practical generative AI capabilities. It begins with an immersive hands-on workshop where participants learn the fundamentals of fine-tuning LLMs using Amazon SageMaker JumpStart. SageMaker JumpStart is an ML hub that can help you accelerate your ML journey.

The competition then moves into an intensive model development phase. During this phase, participants iterate through multiple fine-tuning approaches, which can include dataset preparation, data augmentation, and other techniques. Participants submit their models to a dynamic leaderboard, where each submission is evaluated by an AI system that measures the model’s performance against specific benchmarks. This creates a competitive environment that drives rapid experimentation and learning, because participants can observe how their fine-tuned models perform against larger foundation models (FMs), encouraging optimization and innovation.

The program culminates in an interactive finale structured like a live game show as seen in the following figure, where top-performing participants showcase their models’ capabilities through real-time challenges. Model responses are evaluated through a triple-judging system: an expert panel assessing technical merit, an AI benchmark measuring performance metrics, and audience participation providing real-world perspective. This multi-faceted evaluation verifies that models are assessed not just on technical performance, but also on practical applicability.

The power of fine-tuning for business solutions

Fine-tuning an LLM is a type of transfer learning, a process that trains a pre-trained model on a new dataset without training from scratch. This process can produce accurate models with smaller datasets and less training time. Although FMs offer impressive general capabilities, fine-tuning smaller models for specific domains often delivers exceptional results at lower cost. For example, a fine-tuned 3B parameter model can outperform larger 70B parameter models in specialized tasks, while requiring significantly less computational resources. A 3B parameter model can run on an ml.g5.4xlarge instance, whereas a 70B parameter model would require the much more powerful and costly ml.g5.48xlarge instance. This approach aligns with recent industry developments, such as DeepSeek’s success in creating more efficient models through knowledge distillation techniques. Distillation is often implemented through a form of fine-tuning, where a smaller student model learns by mimicking the outputs of a larger, more complex teacher model.

In InterVision’s case, the AWS LLM League program was specifically tailored around their ConnectIV CX solution for community engagement services. For this use case, fine-tuning enables precise handling of municipality-specific procedures and responses aligned with local government protocols. Furthermore, the customized model provides reduced operational cost compared to using larger FMs, and faster inference times for better customer experience.

Fine-tuning with SageMaker Studio and SageMaker Jumpstart

The solution centers on SageMaker JumpStart in Amazon SageMaker Studio, which is a web-based integrated development environment (IDE) for ML that lets you build, train, debug, deploy, and monitor your ML models. With SageMaker JumpStart in SageMaker Studio, ML practitioners use a low-code/no-code (LCNC) environment to streamline the fine-tuning process and deploy their customized models into production.

Fine-tuning FMs with SageMaker Jumpstart involves a few steps in SageMaker Studio:

• Select a model – SageMaker JumpStart provides pre-trained, publicly available FMs for a wide range of problem types. You can browse and access FMs from popular model providers for text and image generation models that are fully customizable.
• Provide a training dataset – You select your training dataset that is saved in Amazon Simple Storage Service (Amazon S3), allowing you to use the virtually limitless storage capacity.
• Perform fine-tuning – You can customize hyperparameters prior to the fine-tuning job, such as epochs, learning rate, and batch size. After choosing Start, SageMaker Jumpstart will handle the entire fine-tuning process.
• Deploy the model – When the fine-tuning job is complete, you can access the model in SageMaker Studio and choose Deploy to start inferencing it. In addition, you can import the customized models to Amazon Bedrock, a managed service that enables you to deploy and scale models for production.
• Evaluate the model and iterate – You can evaluate a model in SageMaker Studio using Amazon SageMaker Clarify, an LCNC solution to assess the model’s accuracy, explain model predictions, and review other relevant metrics. This allows you to identify areas where the model can be improved and iterate on the process.

This streamlined approach significantly reduces the complexity of developing and deploying specialized AI models while maintaining high performance standards and cost-efficiency. For the AWS LLM League model development phase, the workflow is depicted in the following figure.

During the model development phase, you start with a default base model and initial dataset uploaded into an S3 bucket. You then use SageMaker JumpStart to fine-tune your model. You then submit the customized model to the AWS LLM League leaderboard, where it will be evaluated against a larger pre-trained model. This allows you to benchmark your model’s performance and identify areas for further improvement.

The leaderboard, as shown in the following figure, provides a ranking of how you stack up against your peers. This will motivate you to refine your dataset, adjust the training hyperparameters, and resubmit an updated version of your model. This gamified experience fosters a spirit of friendly competition and continuous learning. The top-ranked models from the leaderboard will ultimately be selected to compete in the AWS LLM League’s finale game show event.

Empowering InterVision’s AI capabilities

The AWS LLM League engagement provided InterVision with a practical pathway to enhance their AI capabilities while addressing specific customer needs. InterVision participants could immediately apply their learning to solve real business challenges by aligning the competition with their ConnectIV CX solution use cases.

The program’s intensive format proved highly effective, enabling InterVision to compress their AI development cycle significantly. The team successfully integrated fine-tuned models into their environment, enhancing the intelligence and context-awareness of customer interactions. This hands-on experience with SageMaker JumpStart and model fine-tuning created immediate practical value.

“This experience was a true acceleration point for us. We didn’t just experiment with AI—we compressed months of R&D into real-world impact. Now, our customers aren’t asking ‘what if?’ anymore, they’re asking ‘what’s next?’”

– Brent Lazarenko, Head of Technology and Innovation at InterVision.

Using the knowledge gained through the program, InterVision has been able to enhance their technical discussions with customers about generative AI implementation. Their ability to demonstrate practical applications of fine-tuned models has helped facilitate more detailed conversations about AI adoption in customer service scenarios. Building on this foundation, InterVision developed an internal virtual assistant using Amazon Bedrock, incorporating custom models, multi-agent collaboration, and retrieval architectures connected to their knowledge systems. This implementation serves as a proof of concept for similar customer solutions while demonstrating practical applications of the skills gained through the AWS LLM League.

As InterVision progresses toward AWS Generative AI Competency, these achievements showcase how partners can use AWS services to develop and implement sophisticated AI solutions that address specific business needs.

Conclusion

The AWS LLM League program demonstrates how gamified enablement can accelerate partners’ AI capabilities while driving tangible business outcomes. Through this focused engagement, InterVision not only enhanced their technical capabilities in fine-tuning language models, but also accelerated the development of practical AI solutions for their ConnectIV CX environment. The success of this partner-specific approach highlights the value of combining hands-on learning with real-world business objectives.

As organizations continue to explore generative AI implementations, the ability to efficiently develop and deploy specialized models becomes increasingly critical. The AWS LLM League provides a structured pathway for partners and customers to build these capabilities, whether they’re enhancing existing solutions or developing new AI-powered services.

Learn more about implementing generative AI solutions:

You can also visit the AWS Machine Learning blog for more stories about partners and customers implementing generative AI solutions across various industries.

About the Authors

Vu Le is a Senior Solutions Architect at AWS with more than 20 years of experience. He works closely with AWS Partners to expand their cloud business and increase adoption of AWS services. Vu has deep expertise in storage, data modernization, and building resilient architectures on AWS, and has helped numerous organizations migrate mission-critical systems to the cloud. Vu enjoys photography, his family, and his beloved corgi.

Jaya Padma Mutta is a Manager Solutions Architects at AWS based out of Seattle. She is focused on helping AWS Partners build their cloud strategy. She enables and mentors a team of technical Solution Architects aligned to multiple global strategic partners. Prior to joining this team, Jaya spent over 5 years in AWS Premium Support Engineering leading global teams, building processes and tools to improve customer experience. Outside of work, she loves traveling, nature, and is an ardent dog-lover.

Mohan CV is a Principal Solutions Architect at AWS, based in Northern Virginia. He has an extensive background in large-scale enterprise migrations and modernization, with a specialty in data analytics. Mohan is passionate about working with new technologies and enjoys assisting customers in adapting them to meet their business needs.

Rajesh Babu Nuvvula is a Solutions Architect in the Worldwide Public Sector team at AWS. He collaborates with public sector partners and customers to design and scale well-architected solutions. Additionally, he supports their cloud migrations and application modernization initiatives. His areas of expertise include designing distributed enterprise applications and databases.

Brent Lazarenko is the Head of Technology & AI at InterVision Systems, where he’s shaping the future of AI, cloud, and data modernization for over 1,700 clients. A founder, builder, and innovator, he scaled Virtuosity into a global powerhouse before a successful private equity exit. Armed with an MBA, MIT AI & leadership creds, and PMP/PfMP certifications, he thrives at the intersection of tech and business. When he’s not driving digital transformation, he’s pushing the limits of what’s next in AI, Web3, and the cloud.

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