Thanks, Sujal. I want to start with what we're hearing from customers, because it is directly shaping how we work. The heart of it is one word, resolution. Our proteoform assays measure the specific molecular states of a protein, its isoform composition and its patterns of modification at single-molecule resolution with the sensitivity and reproducibility that we believe that existing affinity assays and mass spectrometry methods cannot match. What customers tell us again and again is that this lets them see biology that was simply invisible to them before. Critically, proteoforms are more than just additional protein detail. The specific form a protein takes often determines everything that matters about it, where it goes in the cell, what complex it joins, what pathway it activates, what phenotype it ultimately drives. This is one of the reasons the field collectively has struggled to make progress in diseases like cancer and Alzheimer's. Without the ability to resolve biology at this level, the most important signals have stayed out of reach. Let me add additional color to the trajectory Sujal described. A year ago, our preprint with Genentech, Mount Sinai, and the Neural Stem Cell Institute showed for the first time that we could resolve the tau proteoform landscape at single-molecule resolution. Our alpha instrument at the Buck Institute then generated data at a median CV of roughly 5.5% against an industry norm closer to 25%. And that data showed that different APOE genetic risk variants carry distinct tau proteoform signatures. This is a striking result that we believe is only measurable on our platform. APOE is one of the best-known genetic risk factors in Alzheimer's, yet it had never been linked to tau at a mechanistic level. And we feel that the difference we resolved is invisible to pre-existing proteomics tools. We have also found that model systems widely used in Alzheimer's research show markedly different tau proteoform landscapes from one another, a distinction that we expect could prove critical to how drug developers choose their models. These are exactly the kinds of biological differences that bulk methods cannot see and that iterative mapping was built to reveal. They are the kinds of findings that have led researchers to describe our data in their own words as, quote, a game changer and as something that, quote, will become critical to their work. Now let me talk about oncology and specifically why we are leading with AKT1. In Q2, we narrowed our oncology work to 3 candidates, AKT1, EGFR, and p53 and developed them in parallel. All 3 cleared our reproducibility and accuracy criteria and moved into development, which is in itself an important proof point. It tells us our assay building methodology is now repeatable, not a one-time success with tau. AKT1 is furthest along, and we expect that it will be first to market. It is a compelling place to start for both scientific and commercial reasons. AKT1 sits at a control hub for cell growth and survival signaling. There is already a multi-billion-dollar market in the AKT-targeted therapies, but those therapies have shown mixed clinical results, largely because patient selection relies on indirect biomarkers rather than direct evidence that the pathway is actually driving a given tumor. Proteoform-level resolution provides that direct readout. We anticipate it will be able to identify the patients who are truly dependent on the pathway. In other words, this is a way to improve response rates for drugs that are already on the market with exactly the kind of insight existing proteomics cannot reach. That brings me to why we expect that we can expand the proteoform portfolio so quickly. Proteoform development is now bottlenecked by capacity, not by scientific uncertainty. We have what we believe is a proven template. We anticipate that the remaining unknowns on each new target are contained, so adding people should translate directly into more assay content and more capabilities delivered faster. The math is compelling. Our tau assay took about 5 years to build. Our first oncology markers reached that same technical bar in about a year. As we scale the team, we expect to add new assays at a cadence measured in months rather than years, growing from a small handful of assays today toward roughly 20 proteoform assays anticipated by the middle of 2028. That speed lets us be disciplined about where we go next. We are prioritizing neuroscience, oncology, immunology and cardiology. Within those areas, we look for targets with ready access to antibodies and a large market opportunity, which we assess through clinical trial activity, publications, NIH funding and direct customer input. Encouragingly, the demand we're hearing from customers lines up almost exactly with the internal target list we had already built. The scaled-up proteoform development team will aim to qualify new antibodies, build controls and immunoprecipitation protocols, develop and qualify new sample types and drive down the sample input required. Once we hit our performance criteria on a target, we plan to verify and validate the assay, bring in outside collaborators and move to early access. The reallocation of our assay development resources does two things. It gives us the potential to bring new assay content to market on a regular, predictable cadence instead of in occasional bursts, and it is expected to pull forward the enabling capabilities customers ask for most. In particular, lower sample input requirements and access to new sample types, including biofluids like cerebrospinal fluid and plasma. Biofluid access matters enormously because it is what unlocks the large majority of the biomarker market. Here is where that leaves the roadmap. Our tau proteoform assay stays in early access with general availability of consumable kits expected in mid-2027. Our AKT1 proteoforms assay is anticipated to enter early access in late 2026 with general availability expected in mid-2027. A second oncology proteoforms assay is in development, also targeting general availability in mid-2027. We expect a third oncology proteoform assay and continued expansion of the pipeline with additional kits reaching general availability expected in late 2027. On enabling capabilities, we expect to bring a roughly 100-fold reduction in required sample input into early 2027 and to enable cerebrospinal fluid for our tau assay in 2028. You should expect a rolling series of announcements, as new content and capabilities come online roughly every 6 months, increasingly driven by customer demand. Anna will connect that roadmap plan, including the instrument timeline and the revenue outlook. Turning to broadscale, the fundamentals of the program are solid. Our second quarter work reinforced the core premise that iterative mapping with trimer-based probes can decode the broad proteome, but that same testing made clear that our current assay configuration does not yet deliver the performance we need on the timeline we had targeted. Therefore, we have concluded it will not support a 2027 general availability at our target specifications. The remaining work concentrates in 3 areas. The first is our probe library. We have more than enough performant probes to move forward, but we need to increase their diversity through affinity maturation and inherently long lead time activity. The second is the machine learning layer, which keeps improving, as we feed it more on-platform data. The third is the assay architecture behind our configuration change, where we are working with our partners to stabilize the new assay platform so that we can detect true positive binding events more reliably. We are advancing these work streams in parallel. I am not going to put a new timeline on broadscale today, but the underlying science is sound, and the program keeps moving forward even as the bulk of our development effort now goes to proteoforms. One critical area to discuss is how we fit into the world of AI for bio. The field is racing to apply AI to biology, but that work is only as good as the data underneath it. The genome is a blueprint, but proteins are the machines that carry out the work, and their functional state is what determines how a drug binds, whether it is toxic, and which patients respond. That is precisely the layer today's models are missing because the data has never existed at the resolution and scale a model needs. We believe that the data that is needed is the data our platform produces. I'll leave it there for now, but it is a meaningful part of why the proteoform work excites me so much, well beyond any single assay. The world is desperate for differentiated data at scale. Before I hand it to Anna, let me place this in the broadest context I can. Every major advance in medicine has been unlocked by a new ability to read or write biology. Sequencing the genome gave us the foundation to identify genetic diseases faster than ever before. Our growing command of gene editing through tools like CRISPR has begun to deliver real relief from once intractable diseases. I believe the proteoform resolution that iterative mapping uniquely provides is the next of those key breakthroughs. And then over time, it will translate into meaningful advances in how we understand and treat human disease. With that, I'll turn the call over to Anna.