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Most businesses still hide critical data behind a phone call. Pricing, availability, policies. If you want it, you have to call. So we built an AI agent that does the calling for you. It asks questions, leaves voicemails, handles callbacks, and turns conversations into structured data. No scraping. No forms. Just voice. To test it, we pointed it at a real problem: med spas don’t publish Botox pricing, but they share it over the phone. Our agent dialed every med spa in Boston, completed roughly 250 calls in under an hour, and produced real pricing data across the city. The result showed how much insight is still trapped in phone conversations. Use cases are wide open: market research, hospitality availability checks, automotive quotes, home services estimates, healthcare intake info, and more. If you want to hear the AI in action, you can try the demo yourself. You call in, and the agent will respond like a hostess at a high end steakhouse. Give 617-798-9602 a call Check it out at, https://lnkd.in/eHQTzXz3

I read an interesting case study about Heartland using a vendor to analyze markets for expansion. It got me thinking about what happens after the initial research. The market keeps changing. Why not build a view that keeps updating too? For practices in a market you're watching, you could continually track: - Reviews: how quickly are new ones coming in? What are patients happy or frustrated about? - Insurance: which plans are advertised as accepted? Has that changed? - Hours: who's adding evening appointments, cutting hours or opening on weekends? - Team and hiring: who's recruiting? What changes are showing up on team pages? Then put an LLM on top so your team can ask questions as they come up: "Which practices have cut evening hours recently?" "Where are complaints about appointment availability increasing?" "Which offices are hiring while adding new services?" The answers should point back to the evidence and when it was last checked. A job posting is a signal to investigate, not proof that a practice is growing. This is the kind of tool we can build at Setfive: collect the signals, track what changes and make it easy for your team to explore what matters. Useful before an acquisition, and while you're operating in the market afterward. If you could ask one question about a market you're watching, what would it be?

Here's another good DSO case study that highlights how they're leveraging data. Like most practices, no-shows and late cancellations are a big problem for North American Dental Group (NADG). A patient missing an appointment leaves an open chair, underutilized staff, and disrupts normal care for the patient. On the NexHealth blog, Dan from NADG described how his team built a machine learning model to predict if a patient would no-show an appointment. Before reading the case study, I expected that they would have used all sorts of model features like: * Time and type of appointment * Patient demographics * Office location To my surprise, Dan from NADG explained that the model is pretty effective by just looking at "did the patient confirm?" and "have they no-showed in the past?". For other dental groups, this is a useful starting point: connect the data you're already collecting to a problem your practices deal with every day. Confirmation status and appointment history could help your staff decide which patients need extra follow-up. At Setfive, we help groups bring that data together and build tools like this around their existing systems. How does your team decide which patients get an extra call before their appointment?

After a few conversations with leaders in scaling dental groups around building advanced analytics one of the questions I've been getting is "ok but who has done this?" Answering this is usually difficult because if you don't have a previous client in the space that's comfortable with you talking about the project there's not a lot to point to. Interestingly, in dental the largest DSOs have been featured in several case studies describing the data, software, and AI tools they've built. Pacific Dental Services (PDS) is a good example of one. On the VRP blog they walked through their engagement with PDS in a good amount of detail. The post describes how PDS had a gap between ad spend, patient phone calls, and booked appointments. They were unable to get a channel level understanding for which ad dollars actually converted into booked revenue for practices. Did Meta spend produce high LTV patients? Did Google Ads actually result in bookings? The operations team was flying blind. In the post, VRP described how PDS implemented Salesforce Marketing Cloud to get end to end intelligence around their marketing channels. The net result was dropping cost per lead by 17%, driving $1.5m in saved marketing spend for 2022. At Setfive, this is our jam. Identify the problem, aggregate the data needed to solve it, and write the code to address the problem. Do you have a business question that you wish you could answer but don't have the data infrastructure in place? Let's chat.

At DentalAtlas we work with companies to help answer questions about the dental market. Recently, we worked with Daniel Lundberg from Rheyo to help them understand the market for remineralization treatments like Curodont. After some crawling, calling, and analysis we had a strong corpus of data to share with the Rhyeo team. But as we started to prepare a deck we began to ask ourselves if that was the best way to deliver the results. Decks are great for communicating something like "here are the 3 numbers you care about" but they fall short when you want the end consumer to be able to go deeper on items they're curious about. Decks don't let you impress the Zoom by being able to "double click on it" in real time. So we did what we do best…and built an interactive report into https://dentalatlas.io/ to let the Rheyo team explore what we found. The report answers the high level questions with specific outputs and then lets the consumer interactively drill down, ask open ended questions in English, and of course download all the raw data for consumption in offline Claude or Codex. We've continued to experiment with this data delivery approach with great results. If you're looking to develop unique market insights in the dental or medical space I'd love to connect.

At DentalAtlas we work with companies to help answer questions about the dental market. Recently, we worked with Daniel Lundberg from Rheyo to help them understand the market for remineralization treatments like Curodont Despite what OpenAI and Anthropic would like you to believe, you can't just ask a frontier model to "tell me everything about dental practices using Curodont" and get an exhaustive answer back. We had to do the shoe-leather research ourselves, aggregate the data, and build a scoring system. We aggregated signals like: * Practice website mentions for Curodont * Patient reviews mentioning treatment types or brands like Curodont * Data from phone calls to practices asking about treatment availability and pricing * Public insurance plan documents Independently, each signal is limited but by combining them and adding a custom scoring model we were able to create a view into an otherwise opaque market. That’s the difference between buying a list from a vendor and having research you can use to make a product decision.