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Product Recommendation Engine Demo

Datavail developed a custom AI product recommendation engine for a leading equipment leasing company that was struggling with time-consuming product substitution processes.

AI-Powered Product Recommendation Engine: 85% Faster Product Substitutions

When a customer requests an unavailable product, every second of delay risks losing the sale. This demo shows how a leading equipment leasing company deployed a Generative AI product substitution engine that recommends alternatives in under five seconds—achieving 85% accuracy while freeing product teams from manual lookup requests. See how intelligent automation transforms inventory constraints into faster customer service and higher conversion rates.

Instant product recommendations through a simple chat interface that analyzes specs, availability, and pricing in seconds
85% substitution accuracy using Azure OpenAI services combined with private product data for reliable alternatives
Secure hybrid architecture that keeps sensitive product information private while delivering fast, accurate responses
Dramatic time savings for both sales and product teams, eliminating hours of manual research per week
Real implementation showing how the system handles product details, location availability, and out-of-stock scenarios through natural conversation

Watch the Full Demo to See AI Product Recommendations in Action

Frequently Asked Questions

How does AI recommend product substitutes accurately without manual input?

AI product recommendation engines analyze multiple data points simultaneously—technical specifications, availability across locations, pricing structures, and historical substitution patterns. The system learns which product attributes matter most for successful substitutions in your industry. When a product is unavailable, the AI identifies alternatives that match critical specs while considering real-time inventory levels. Sales teams simply ask questions in natural language, and the system delivers ranked recommendations with explanations in seconds.

Can AI product recommendation systems work with proprietary product catalogs?

Yes. The solution combines your private product data with large language models through a secure hybrid architecture. Your sensitive product information, pricing, and inventory data stay within your environment and never train public AI models. The system connects to your existing product databases and ERP systems, so recommendations reflect current availability and specifications.

What efficiency gains can companies expect from AI product recommendations?

Companies typically see dramatic reductions in time spent researching product alternatives. The equipment leasing company in this demo reduced response times from several minutes to under five seconds. Sales teams handle more requests per day without increasing headcount. Product specialists spend hours less per week on routine lookup requests, focusing instead on complex customer needs.

Show Transcript

Speaker 1: This chart shows some of the KPI features that we're using. I won't go through all of these, but it just shows things that we're monitoring, or data that we're capturing. And then our actions on the right show we can set triggers in Accelatis just so that we get notifications if we reach a certain threshold. If you look at an example on the bottom for HFM, we're tracking the task and data audit logs to make sure that those don't get too big. This slide shows an example of those triggers or the emails that we get from Accelatis. My self and some of the other admins will get an email saying that the audit log reached a certain threshold, we need to go in and archive it. Then we run through our process that does that. It's really nice that we get these during the close or whenever. It's good and bad because you can get nervous when you start seeing them, but you can be proactive about doing what you need to do to minimize any impact on the users. Speaker 2: There is an opportunity that we haven't discussed, though, is that you can take that trigger and then tie it to an automation drop to actually do the archiving. So, it depends on your internal process. Some of the value is a trigger could kick off an automation job that could remediate the problem. And it just depends on what your internal processes are and whether you want to handle it annually or you want the system to do some of it for you. But a lot of these things can be connected pretty easily depending on and how far you want to go. Speaker 1: This is one of our real life examples of how we use the health checks to troubleshoot an issue. We had several HFM users reporting slowness and poor response time. A lot of times you'll get an email, "Hey, HFM is slow". It's like, okay, can you give me a little more detail, but when we start seeing multiple users saying that, okay we need to go check something out, we run right to Accelatis. As an administrator, I try logging into HFM. I can get in, but I can't navigate to system messages, so even I'm having issues. I have access to everything. So, we ran the health checks and the results of that are up here showing that the user simulation feature generated a failure and it also showed lengthy response times, not what we would normally see. The activity analysis feature showed that HFM was having issues with a specific sequel table related to HFM. From there, we go to our DBA and the DBA found dead locks on the HFM data audit table. As an admin, when I couldn't get into the system messages or the audit logs, that's why there was a dead lock on the table. Then I wasn't able to even get back into the application. Then we performed a restart. We stopped services, and rebooted, and everything came back okay. We logged in [inaudible 00:03:12] Oracle on the specifics of the dead lock on the table and We were able to provide them with all the logs and everything that they needed. It just shows how Accelatis helped us get right to the root of the area and how Oracle ... we were able to quickly provide Oracle with what they needed so they could research it.