At Tagboard, we’re always finding new ways to make storytelling easier and more impactful. That’s why we’re introducing Tagboard’s Spark AI, a powerful AI-driven production assistant designed to streamline Shoppable content creation. In collaboration with Ollion, an expert in cloud and AI technology, and powered by AWS, Spark AI helps you quickly find the right products and promote them with ease—so you can focus on creating great content without slowing down.
“Tagboard’s Spark AI is a prime example of how applied AI and cloud-native architectures can transform the way media teams engage their audiences,” said Dustin Snyder, Senior Director of Cloud Solutions at Ollion. “We collaborated closely with Tagboard to develop an intuitive and scalable AI integration—powered by Amazon Bedrock—that empowers producers to act at the speed of live. Spark AI shows how the right tech foundation can unlock real-time creativity and frictionless revenue in a seamless, human-centric workflow.”
This builds on Tagboard’s latest innovation, Shoppable, which launched earlier this year. Seamlessly integrating e-commerce into live production through our partnership with Fanatics, Shoppable empowers content producers and commerce teams to transform key moments into interactive shopping experiences—turning fan engagement into frictionless revenue with a single click.
In today’s fast-paced content landscape, producers face a common challenge: limited experience and time constraints that hinder the ability to create, build, and optimize a Shoppable strategy effectively. Spark AI takes the guesswork out of the process, making it easier than ever to enhance engagement and drive revenue.
Solving the Curation Challenge with AI
Traditionally, picking the right Shoppable products takes time and a lot of manual effort. Spark AI takes out the guesswork by using AI to recommend products that match your show’s unique content and audience. The result? More efficient workflows, smarter recommendations, and increased revenue opportunities.
Key Benefits of Tagboard’s Spark AI:
- Faster Product Discovery – Eliminate the time spent manually searching for the right products.
- More Engaging Content – Serve up audience-relevant Shoppable items based on real data.
- Higher Revenue Potential – Leverage AI-driven insights to curate high-performing product recommendations.
How Tagboard’s Spark AI Works
Spark AI takes a profile-based, adaptive approach to product curation. Spark AI actively learns and refines recommendations based on user input, audience behaviors, and past selections.
1. Profile-Based Personalization
- Users create both a team profile (industry, organization, location) and a user profile (show type, platform, audience demographics, and buying behaviors) to ensure highly relevant recommendations.
For example, if you go live Sunday mornings for diehard NFL fans, they might be willing to invest in niche team merch—but only for their team. By sharing these insights, you enable Spark AI to tailor recommendations to your unique programming and audience, letting your Spark AI production assistant do the heavy lifting!
- These profiles ensure recommendations are contextually relevant and aligned with the show’s content and audience preferences.
2. Integrated Seamlessly into Shoppable
- Spark AI is integrated directly within Tagboard’s Creator workflow, ensuring a frictionless experience.
- Users interact with Spark AI in the same space where they manage their Shoppable content, keeping discovery and curation in one seamless flow.
3. Easy, Conversational Interface
- Users engage in a guided conversation that creates a smooth, intuitive feedback loop.
- Delivers personalized responses that are dynamic, context-aware, and adaptive to user needs.
4. AI-Driven Learning & Continuous Optimization
- Spark AI continuously learns and adapts based on:
- User feedback (thumbs up/thumbs down on recommendations)
- Past selections
- Audience engagement trends
- Production history
- Over time, Spark AI refines its suggestions, making them increasingly accurate and impactful.
The Technology Behind Spark AI
Spark AI is engineered with a robust and scalable, AWS supported, architecture to ensure seamless performance and adaptability. The key technical components include:
- Cloud-Native Infrastructure: Spark AI leverages AWS services including S3, Lambda, SQS, API Gateway, and RDS to handle data processing, request management, and seamless scalability.
- AI Foundation: Spark AI is powered by Amazon Bedrock, with Claude 3.5 Sonnet as its foundational model. This allows for highly sophisticated natural language processing and adaptive learning.
- Agent-Based AI Processing: Spark AI utilizes Agents functionality in Bedrock to dynamically refine recommendations, guiding users through structured conversations and providing highly relevant product selections.
- Dynamic Product Recommendations: Product rankings and recommendations are generated based on context-aware prompts derived from the user request. Instead of relying on static rules, Spark AI intelligently formulates queries to fetch the most relevant results.
- Profile-Driven Prompt Engineering: Spark AI constructs a precise prompt using both team and user profile data, ensuring the recommendations align perfectly with the user’s audience and content strategy.
Bringing AI-Powered Curation to Your Workflow
Spark AI is more than just a recommendation engine—it’s a game-changer for content teams looking to elevate their Shoppable strategy with minimal manual effort. By embedding AI-driven personalization directly into Tagboard Creator, we’re making it easier than ever to surface the right products, engage audiences more effectively, and unlock new revenue streams.
Ready to experience the power of Tagboard’s Spark AI – your AI production assistant? Try Spark AI today and transform the way you create revenue-generating, Shoppable content.