AI comes with high expectations. According to McKinsey’s 2025 survey, 88% of organizations use AI at least in one business function, but just 7% have scaled it company-wide. This imbalance means teams feel pressure to move fast while managing risks. Gartner, in turn, predicts that at least 30% of generative AI projects will be abandoned after the proof of concept, often because they don’t show value soon enough.
Many organizations see AI as a long-term effort, but the real challenge is finding out quickly if an AI idea works before investing too much in something that might not succeed. Our team recently built an AI assistant MVP for a gift retailer in just three days to test if it could deliver results and then quickly move with validation. In this article, we’ll share some thoughts on how to approach rapid AI prototyping when proving your ideas quickly matters more than making everything perfect.
The Business Challenge
To decide if a product idea is worth building, you just need to put a simple version in front of users, watch what they do, and learn from their feedback. The faster you confirm or reject your main assumption, the more you get out of a small budget and avoid heading in the wrong direction.
We used this mindset when working with an online gift card retailer and their recommendation system. It was already working well, raising average order value by 15%. However, our audit showed a key difference in user behavior. Some shoppers knew exactly what they wanted and bought gift cards quickly, while others just knew they needed a gift and did not know where to begin. Existing search and recommendations helped the first group, but the second group often got stuck. This is a classic fit for conversational AI.
So, we suggested our client personalize the gift search with an AI assistant. As part of our AI consulting services, we framed the idea of AI proof of concept and needed to test it.
We couldn’t afford to overengineer, especially since industry data shows that building an MVP can cut development costs by up to 50% because they avoid long development cycles, detailed planning, and building every feature. So, our team chose rapid prototyping to test our assumptions with real users in small steps.
Our Rapid AI Prototyping Approach
Our goal was simply to figure out if an assistant can make it easier for shoppers who do not know what to search for. In similar cases, it is recommended to follow the next guiding principles:
- Treat the AI assistant prototype as an experiment. AWS offers similar advice in its PoC guidance, saying that a proof of concept should show business value and reduce investment risk.
- Focus on the core intelligence instead of perfecting the interface. At this stage, the user interface isn’t the main issue. What matters most is how well the assistant understands requests, keeps context, and improves its suggestions.
- Use existing tools and features whenever possible. In rapid prototyping, building custom parts too early can slow down delivery and make it harder to adjust after getting feedback.
All of this meant focusing on whether the MVP could understand how shoppers describe the recipient and occasion in plain language, and whether the assistant could offer helpful options with a short explanation. Everything else is optional until it becomes necessary.
Key Product Decisions That Saved Time
When you only have a few days, speed comes from making the right choices. First, we used the latest AI tools and treated the MVP as a way to learn fast. We built a working prototype in three days on the Replit platform, which allows writing requirements in plain language and letting an AI agent turn them into working code.
The choice to go with Replit also allowed us to:
- Skip local setup. Because it runs in the browser, we didn’t waste time installing tools or fixing work-on-my-computer problems.
- Build a full prototype fast. We didn’t need to set up servers, CI/CD, or infrastructure as code to go from an idea to a working version people could try.
- Share instantly. We could send a link to a live demo right away and get immediate feedback.
- Iterate quickly. We could change prompts or logic, rerun it, and test again within minutes.
- Keep costs low early. We spent about $25 to host the prototype.
Our second decision in AI prototype development was to avoid incremental upgrades, which often make teams focus on tuning, edge cases, and legacy constraints. Instead, we built a system to handle how people actually ask for gifts, often in vague terms and without the right keywords. To achieve this, we focused on three main capabilities:
- finding relevant options based on the meaning of a request;
- interpreting messy yet real-world input so users didn’t have to search perfectly;
- giving recommendations with short explanations.
Results and Early Validation
We chose a platform that let us keep moving fast after the prototype. We used AWS Bedrock, which lets you work with different AI models through one entry point. We have already worked with AWS Bedrock on a previous AI assistant project for a fashion retailer, and the platform offers ideal capabilities for query understanding and efficient text request processing. Opting for this platform allowed our team to cut down setup time, avoided early vendor lock-in, and made it easier to move the MVP into structured testing.
Mariia Mikhalova
Data Scientist at SPD Technology
“This project taught me that you don’t have to build everything from scratch. Using existing AI solutions and broker platforms like AWS Bedrock are enough to create complex products fast.”
As a result, we went from the first demo to A/B testing in about a month and a half and got clear feedback early. Shoppers who were unsure did not want to search better. Instead, they wanted help figuring out what they were looking for. Users could easily describe the recipient and occasion in plain language, but they also wanted quick ways to refine results, like changing the budget, tone, or age range, and simple reasons for each suggestion.
The generative AI MVP also surfaced a few extra observations about the assistant:
- It can provide access to a new customer segment with shoppers who need a gift but don’t know the recipient’s preferences.
- Concise explanations make suggestions feel more actionable.
- Vague or unusually phrased requests didn’t break the experience.
The MVP gave us real feedback and confirmed our direction for future improvements to the AI assistant.
Conclusion: When Rapid AI Prototyping Makes Sense
In our project, the choice to build an AI assistant MVP let us validate the idea. We put a working version in front of users early and checked if it solved the real problem. To deliver in just a few days, we had to think like we were building internal tools, even though the experience was for customers. We set up a simple workflow for testing, reviewing quality, and making fast changes. At the same time, the work was like an innovation lab project, focused on an evidence-based experiment, designed to explore an AI concept without turning it into a long program.
As a model for early-stage product ideas, the MVP did more than just prove the direction. It showed what the real product would need next, like ways to refine results, clear reasons behind suggestions, and practical guardrails to make the experience trustworthy as it grows.
This bigger value of rapid AI prototyping is not unique to our project. AI MVP development helps businesses move from idea to evidence faster and decide where deeper engineering is worth it. If you’re exploring an AI concept and want to validate it quickly, we’d be glad to offer our AI/ML development expertise to help you move from hypothesis to MVP and beyond.