The healthcare supply chain is a maze of data, preferences, and pricing. One innovative healthcare purchasing platform set out to simplify that complexity by giving physician group practices transparency, control, and measurable savings. Their system helps medical practices manage vendor relationships, streamline purchasing, and save 15-20% on costs.
We partnered with them to design the platform that makes medical purchasing faster, smarter, and more transparent. So, when they began exploring how artificial intelligence could enhance that experience, the next step wasn’t starting from scratch, but seeing how AI could make a strong system even stronger.
The platform already captures a wealth of purchasing and pricing data across vendors and products. But much of what made this project special wasn’t just the data but the expertise behind it.
Its founder has deep, hard-earned knowledge about how to build vendor relationships, select the right products, and recognize true savings opportunities. The question became: Could AI learn to make those same recommendations with the same level of insight and accuracy?
Together, we set out to create a model that could mimic the founder’s decision-making process: analyzing data through the same lens she would when advising a client. The AI model was trained to identify optimal product selections, evaluate pricing, and recommend cost-saving alternatives across complex vendor networks.
After extensive training and testing, the system reached 94% alignment with the founder’s own recommendations, an exciting result that showed how AI can begin to capture and extend the intelligence of experienced professionals.
In most industries, 94% accuracy would signal that an AI model is ready for deployment. But in healthcare purchasing, where every decimal can affect compliance, budgeting, and trust, the final few percentage points matter.
Rather than viewing this as a limitation, our partner saw it as a moment of clarity: a chance to ask bigger questions about AI readiness and real-world reliability.
When does “accurate enough” become actionable? And how do organizations balance innovation with confidence?
These are questions every business exploring AI will face. Accuracy is important, but so is learning. AI models only improve through interaction, and that evolution starts when organizations are willing to experiment with what’s possible.
Pursuing that final few percentage points of accuracy can take months of additional refinement. But sometimes, the most valuable insights come from trying before everything is perfect.
In this case, the exploration showed where AI could drive measurable value, and what kinds of data would make the model even stronger in the future. It revealed potential predictive intelligence to make purchasing decisions more strategic, while affirming the team’s commitment to innovation through careful, data-informed steps.
Every test and iteration adds to a foundation that makes future AI adoption smoother, smarter, and more impactful.
We see projects like this as milestones in a longer journey, not just for AI, but for how teams innovate together. The willingness to explore the frontier of AI reflects the kind of forward thinking that moves industries forward.
AI doesn’t have to be flawless to be transformative. Sometimes, it’s the “almost there” moments that spark the next big leap. Because the real promise of AI isn’t perfection, it’s progress.
We believe AI works best when it’s built around human expertise. If your team is exploring how to capture that knowledge and scale it through technology, we’d love to help you take the first step. Let’s explore what AI could learn from you.
Posted in Clients, Software Services
