A. Chaudhry
Analyst · Maxim Group
Thank you, Steve, and good morning, everyone. Before I get into first quarter, I want to remind everyone of the framework we are using to apply AI across CareCloud, because everything I'm about to walk you through fits inside that framework. And I think it's the clearest way to understand both what we are doing today and what compounds over time. As Steve mentioned, we are pursuing AI along 3 parallel tracks. The first is back-end cost and efficiency optimization, where AI applied inside our own RCM, financial, and administrative operations to do the work we already do for our clients, but faster, more accurately and at a much lower cost. The economic outcome shows up in the margins. The second is embedding AI into our existing customer-facing applications, our EHR, practice management, patient engagement and benchmarking platforms, bringing AI inside the products our clients already use makes them smarter, stickier and more valuable without asking clients to buy something new. The outcome shows up in retention, expansion and the strength of our existing revenue base. The third is building entirely new AI products for discrete, high-value problems in healthcare operations. StratusAI Front Desk Agent and cirrusAI Notes are the 2 most visible examples today with AI prior authorization, AI-assisted medical coding and additional clinical documentation capability and active development. The outcome is new revenue lines as those products mature. These 3 tracks are not separate strategies competing for resources, they are the same investment compounding 3 different ways. Let me walk you through where each one stands at the end of Q1. On the back-end track, we continued in Q1 to apply AI across our own RCM financial and administrative operations. This is a track that gets the least external attention, but it is where AI is creating its most measurable near-term impact. Inside our RCM operations, AI is reducing claim errors, improving documentation accuracy and increasing first pass acceptance rates to payers. Across our administrative and financial functions, it is helping our internal teams handle higher volumes with the same headcount. We are also adopting AI-driven tools across the software development life cycle such as code generation, code review, QA and testing and application design. This is the same productivity revolution the broader software industry is going through, and we are participating in it as a deliberate strategy. Over time, we expect 2 compounding outcomes, higher code quality and meaningfully more output per engineer. For a company shipping across the wide product surface EHR, RCM, practice management, patient engagement, benchmarking and an expanding AI portfolio, that engineering leverage matters. How we measure progress on this track matters. We are not just tracking lag indicators, outcomes like acceptance rates and denial ratios that tell you what already has happened. We are actively monitoring lead indicators, the signals that predict revenue cycle performance before it shows up in the financials. How early errors are caught, how many claims are pre-validated before submission, how much human intervention is required for a claim and how effectively our AI predicts denials, so that rules can be configured proactively, not reactively. These upstream metrics are where AI creates its leverage, and they are what give us confidence in where the trajectory is heading, not just where it has been. Our longer-term ambition is to set a new industry benchmark, zero-touch claims, a fully automated workflow where AI handles intake, validation, submission and follow-up with minimal human intervention, allowing billing teams to focus on acceptance rather than routine processing. Q1 was a quarter of measurable progress on the underlying lead indicators that bring that vision closer. The second track is bringing AI into the products our clients already use every day. Our existing suite, EHR practice management, patient engagement, benchmarking represents thousands of touch points per client per day. Everyone is an opportunity to make our software more intelligent without asking the client to buy something new. This creates more lasting AI value than launching a new product, because it improves everything already deployed with customers. In Q1, we continued deepening AI inside these platforms, improving how our EHR surfaces relevant information at the point of care, making our practice management system more predictive about scheduling and intake, and enhancing the analytical depth of our benchmarking capabilities. None of this is a new product announcement, it is continuous embedded improvement to platforms our clients are already paying for. The most successful version of this track is one where AI inside the product is invisible to the user. They simply find that the software is doing more for them than it used to. We will share specific results as they become meaningful to disclose. This track is also where leverage on our acquisitions plays out. Some of the platforms we brought in through Medsphere and the MAP App serve a different client segment than our ambulatory base, hospital systems, health networks and emergency departments. The AI work there is in earlier stages, but the principle is the same. The platforms get more value and AI is part of them. And that value accrues to clients already on them, that is leverage we paid for, and we are working through it methodically. The third track is the one that gets the most public attention, new stand-alone AI products for specific high-value workflows. This is where stratusAI Front Desk Agent and cirrusAI Notes live and where our development pipeline continues to expand. Let me start with stratusAI Front Desk Agent, our agentic AI Front Desk solution. We continue to sign new business in Q1, almost entirely from within our existing client base, exactly the motion we wanted at this stage. Our priority right now is not maximizing contracts signed, it is making sure every agent we sign is implemented well, completes its trial successfully and earns the right to expand inside this account. Expansion means more agents per client, additional functions, extended coverage hours and broader used cases. This is the curve we are deliberately working depth before breadth, because it produces durable standing revenue rather than a flurry of signed contracts that don't convert into real used case. Within the Desk Agent suite, stratusAI Voice Audit continues to play an important complementary role, giving practice administrators visibility into both AI handled and staff handled calls. Some clients adopt Voice Audit alongside Desk Agent from day one. Others bring it on later as their AI deployment matures. Either way, it deepens our broader stratusAI footprint inside the account. Turning to cirrusAI Notes, our ambient documentation product. Notes continues to be an entry point for many providers into the cirrusAI family on ambulatory side, where it serves the most acute pain point in clinician stay. What I want to highlight this quarter is the integration efforts underway to bring cirrusAI Notes into the inpatient platforms we acquired through Medsphere, opening the door to AI-assisted documentation inside hospitals and health systems, a different clinical workflow, user and buying center that ambulatory market we have served historically. This is exactly the cross-pollination between our acquisitions and our AI portfolio that we described as the multiplier effect when we closed Medsphere. Beyond Front Desk Agent and Notes, our pipeline continues to advance. AI prior authorization, AI-assisted medical coding and additional clinical documentation capabilities are all in active development inside the AI Center of Excellence and bringing those to market is a goal for this year. We will share more on each as they get closer to client readiness. Let me close by coming back to the 3 tracks framework, because I think this is where the strategic picture comes together. A company pursuing only the third track, only new AI products is making the bet that depends entirely on those products achieving scale. A company pursuing only the first track, only internal cost optimization captures margin, but doesn't differentiate its products. A company pursuing only the second track only bringing AI into existing apps strengthens retention, but doesn't create new revenue lines. CareCloud is doing all 3 at once, and the reason that matters is that each track derisks the other. Internal AI improves our economics regardless of how fast the new AI products itself. Embedded AI strengthens our existing revenue base regardless of how fast we capture new markets. And new AI products give us a path to entirely new revenue lines built on top of an installed base that AI is already making stronger every day. Q1 was a quarter where each of those 3 tracks move forward. Each one continue to compound in the direction we have been describing and together, they form the durable profitable AI strategy we are executing. With that, I will turn it over to Norm to walk you through the financials. Norm?