When it comes to AI nowadays, everyone is very familiar with the upstream and downstream. When hyping storage, we know to look at optical modules, materials, and equipment; when hyping computing power, we know to look at NVIDIA, power consumption, and heat dissipation.

However, when it comes to robots, most people's understanding still remains at the level of the humanoid robots that can take a few steps on the Spring Festival Gala, thinking it's cool. But beyond that, many people can't pinpoint the upstream and downstream of the robot industry or identify the "sage leaves" of the robotics industry.

Actually, the robotics industry also has its own supply chain and is increasingly diverging into more specialized tracks. While some work on hardware and others on models, there is a part that is rarely noticed by ordinary people but may affect the upper limit of robot capabilities: data. More precisely, it is the data that provides the "hands-on experience" for robot production.

The rise of large-scale models has a simple premise: a massive amount of text, images, code, and videos has already been accumulated on the Internet. The initial challenge for model companies was how to ingest this data, how to scale computing power, and how to train larger models.

Robots are different.

Robots do not have a natural internet corpus. Trajectories that can be directly used for robot control learning usually include observations, actions, and robot states. Depending on the task, they may also include object states, contact information, success conditions, task semantics, and control frequencies. Real-world data collection is slow, expensive, and dangerous, and highly dependent on the specific robot.

And this is the background of Axis Robotics' emergence. On July 27, this Physical AI data engine company announced the completion of a $12 million seed round of financing, led by Hack VC, with participation from Nomad Capital, Pi Network Ventures, 10K Ventures, and several angel investors. This investment is not in yet another team building robots but in a team dedicated to feeding experience to robots.

Against this backdrop, BlockBeats interviewed Axis Robotics founder Chris, who has rich experience in investment and consulting.

From Data Business to Robot Data Gap

BlockBeats Question: What were your main entrepreneurial and professional experiences before starting Axis? How did you come into contact with robotics, Physical AI, and the direction of robot data? Was there a specific opportunity that made you realize there was an entrepreneurial opportunity here?

Chris: Prior to Axis, my previous entrepreneurial experience had already given me a strong realization: data may not be sexy, but it is often the variable that determines the ceiling, deciding how far a company can ultimately go.

The beginning of 2024 was a critical moment for me. At that time, artificial intelligence was very hot, with everyone talking about models, parameters, and computational power. However, I increasingly felt that the industry was transitioning from being "model-driven" to "data-driven." While models are certainly important, what often determines whether a model can continue to advance is the data.

What really struck me was Surge AI. Founded in 2020, the company had surpassed $1 billion in revenue in just four years, outpacing even Scale AI, and with minimal fundraising. I kept thinking: Why could a data company grow so fast? Had we underestimated the value of data in the age of intelligence?

During that time, I often chatted with friends from Nanyang Technological University, UC Berkeley, and NVIDIA, listening to their observations on the changes happening at the intersection of academia and industry. We gradually reached a clear consensus: while models iterate quickly, data is the foundation, the most challenging, least standardized, and easily overlooked layer. From the pretraining of large language models to the data requirements for fine-tuning after an agent emerges, data is not becoming less important; on the contrary, it is becoming increasingly complex.

So I began to wonder, would Physical AI follow the same path? The physical world is much more complex than the world of text. The variability of the real environment, sensor noise, various edge cases, and different user habits all significantly increase the demand for data. The amount of data required for Physical AI and the scenarios used for error correction during fine-tuning may be a hundred times that of today's artificial intelligence, or even more.

However, at that time, if you were to ask, "Who is working on Physical AI data?" hardly any name could be immediately mentioned. A track that could potentially produce a hundred billion dollar company in the future had no clear mind occupying it. For entrepreneurs, this signal was already very clear: it was worth diving in with full dedication.

So, starting in the summer of 2025, I, along with friends from NTU and UCB, began in-depth discussions on the technical roadmap and product strategy, and Axis Robotics truly took off at that stage.

BlockBeats Question: When the outside world discusses the robotics industry, the focus is often on hardware and models. Why does the Axis team believe that data is the "sage leaf" most likely to hinder the scalability and implementation of Physical AI in this cycle?

Chris: This is a very good question. The development of large models gives us a very direct inspiration: once a technical roadmap is validated, the next stage of capability enhancement often comes from the simultaneous expansion of data, computing power, and model scale. From GPT-1 to GPT-3, we have seen that large-scale pre-training can bring about significant leaps in capability.

However, the robotics industry is still in an earlier stage. Today's hardware and algorithms are actually progressing quite well, with various robotic arms and humanoid robots constantly emerging. The model roadmap is also gradually converging, such as VLA connecting vision, language, and robot action; World Models learning how the environment may change after an action; and WAM further integrating prediction of future states and action generation. People are working in these directions.

The issue is that in order for a robot to not only perform in the laboratory but also work in a real environment, achieving a leap similar to the "GPT-1 moment" of language models, a much larger amount of real-world interactive data is needed.

For example, today's robots are a bit like someone who has only learned to swim in a textbook, understands the theory, but has never been in the water. If you want it to truly know how to swim, you have to let it practice in different pools, different water temperatures, and different waves repeatedly. This "swimming experience" is the data.

But currently, the scale of publicly available data is still very small.

Open X-Embodiment is considered one of the representative open datasets, integrating 21 institutions, 22 types of robots, and over 1 million trajectories. While this may sound like a lot, compared to internet-scale data, it is not even a fraction. Many other public datasets are still at the level of a few hundred hours.

Therefore, we believe that what is currently hindering the basic models of robots is not necessarily the model structure itself, but rather that the scale and diversity of the data have not kept pace. Without a sufficient amount of real-world data across scenarios and industries, even the best models and hardware can only spin in the laboratory. At this stage, data is not a mere icing on the cake but the key variable determining whether Physical AI can cross the inflection point.

BlockBeats Question: Can you use the simplest, most straightforward, and least roundabout way to tell the ordinary person what Axis does?

Chris: Put simply, Axis is helping robots continuously accumulate "hands-on experience".

You can think of a robot as a newly hired intern. It's not dumb, but it hasn't done anything yet. You need to turn it into an experienced worker. Just showing it the operation manual won't work; it has to start doing things on its own, make mistakes, receive corrections, do it again, and practice repeatedly. What we do is systematically produce these experiences.

From preparation before training, task design, simulation exercises, to real-world data collection, and then data cleaning, error correction after model training, and continuous optimization, we have streamlined the entire process. We have also built a web-based simulation platform and a mobile data collection tool, allowing the general public to participate. By folding clothes and tidying up at home, these daily actions can all become learning materials for robots.

In a nutshell: Axis is a continuous training data supplier for Physical AI, constantly providing error-correcting data to help robots learn faster and make fewer mistakes.

Turning "Hands-On Experience" into Robot Training Fuel

BlockBeats asks: What do you think is the ultimate solution to the current embodiment intelligence data gap? Among first-person data, teleoperation data, and simulation data, which one is better?

Chris: Today, the biggest challenge in the industry is that it is difficult to simultaneously achieve data scale, scene diversity, and real-world physical alignment. Real-world physical alignment means that the action patterns in your data must be consistent with the real world. You can't be proficient in a virtual environment but "clumsy" in the real world.

Let me give you an example. Simulated teleoperation data is like practicing driving in a driving simulator. The barrier to entry is low, no real car is needed, and people from around the world can remotely control virtual robots through a webpage to generate a large amount of training data. By randomly combining robot forms, objects, scenes, and tasks, diversity can quickly be achieved. However, the drawbacks are also obvious: no matter how good the simulator is, it is not the real road conditions. Irregularities, sudden situations, and uneven road surfaces in reality are hard to completely replicate in a simulator. The model may learn in a virtual environment, but may still struggle on a real machine, which is what everyone refers to as the "reality-virtuality gap".

First-person real human data is like fitting a dashcam on an experienced driver. Ordinary people can use their phones to collect real-world operations at home or in the office, with the footage containing visual and verbal information to help the model understand how humans actually work. However, the downside is that the footage does not provide precise joint data for the robot, which may not be sufficient for training delicate actions directly.

Real-world Remote Operation Data, is when you truly take the model out for a drive. It offers the highest trajectory accuracy, closest to real-life physical interaction. Especially when the model fails to operate on its own, human intervention to correct the residual trajectory is particularly valuable for model improvement. However, it also comes at a high cost, requiring a real-world setup and human involvement. The production capacity is limited and cannot be infinitely scaled.

Style Deployed in Axis

So the answer is not about which one is better, but each of the three data types has its own role: simulation scales up the volume, first-person view helps the model understand common sense in the real world, and real-world remote operation achieves precision calibration and closed-loop error correction. Only by integrating these three types of data into the same system is it possible to truly fill this gap.

This is also why we are implementing a "hybrid data source, bidirectional closed-loop engine." We use simulation remote operation data and first-person view human data for dual-supply, combined with Human-Gated DAgger. Its approach is straightforward: after the model fails, humans intervene to correct it, and then the correction results are sent back for training. This way, data is collected, cleaned, and enhanced, enters a unified visual, language, and action model training, then goes through virtual-real deployment, failure feedback loop, and data supplementation, forming a self-propelling cycle.

BlockBeats Question: Why did Axis initially choose to start with simulation data and is now laying out first-person view data? Has there been a strategic shift?

Chris: This is an extension of the same data strategy. Physical AI must be data-driven, and the core metric of data is not volume, but diversity, as diversity determines generalization and robustness. We started with simulation because it is controllable, repeatable, and easy to evaluate. We can design tasks, adjust scenarios and robot morphology, collect data, and then replay, validate, train, and test. It itself is like a "world model," a virtualization of the real world.

First-person view data can complement the breadth of the real world. Recently, research including DreamDojo has shown the industry the potential of large-scale first-person view videos in learning human behavior and world rules. This type of data records how people use tools, handle objects, and complete tasks, and these behaviors are scattered across different countries, industries, and life scenarios, making it difficult for a few laboratories to cover.

Simulation data is easier to produce by a few platforms, but first-person view data is inherently dispersed and requires global contributors to participate. As top model companies like NVIDIA and DeepMind increase their demand for this type of data, the ability to continuously access, process, and validate first-person view data globally will become a critical capability.

More importantly, for Axis, the contributor network, task distribution, and data processing and validation pipelines are reusable. From simulation expansion to first-person view data, it's not a change in direction, but a completion of a more comprehensive robot data infrastructure.

BlockBeats Question: Can you walk us through the entire process of embodied intelligence data collection, processing, and training in Axis?

Chris: In Axis, data collection and processing are not fragmented pieces; the entire process is an end-to-end closed loop. The recently released Axis V2 was a very crucial upgrade for us: previously, Axis was more like a one-way data collection station, and now we have integrated task generation, data collection, model training, evaluation, and optimization into a system.

The first step is question setting. In a simulation environment, we use algorithms to combine robot entities, target objects, positions, visual conditions, and other variables. For example, by combining 10 actions, 10 objects, and 10 placement methods, the diversity of tasks can be exponentially amplified.

The second step is question answering. Global contributors can complete simulation tasks through a simple web or mobile interface or use a mobile first-person view application to capture real-world operating processes.

The third step is grading and processing, which is also the most critical part. Raw data often contains jitter, invalid actions, and unnatural operations. We first clean, smooth, and resample the data. Then, using RoboVerse, the same batch of data circulates between different simulation environments, migrating simulation data collected from lightweight web interfaces to Isaac Sim for replays and data augmentation, simultaneously replaying, modifying scene materials, lighting, camera angles, and physical parameters. It's like taking the same recipe, using different pots, fires, and ingredients to cook again; one piece of raw data can expand into many training samples.

After processing, the data is not fed directly to the large model. It must undergo success condition checks, anomaly filtering, and format standardization before entering model training. After a training cycle, we have the model execute tasks in simulation and evaluation environments to observe where it is prone to failure in certain scenarios and states, transforming these weak points into new targeted collection tasks.

The model first autonomously performs the task, and once it deviates from the correct path, contributors take over and provide corrective actions. This corrective data is reintroduced into the training process, allowing the model to gradually learn how to deal with situations it couldn't handle before. This flywheel keeps spinning, pushing the capabilities of data, the model, and the robot forward together.

Therefore, the significance of Version 2 is not just the addition of several features, but transforming Axis from a system that merely "receives data" into a complete system that "helps the model get smarter."

BlockBeats Question: Axis recently released Dataset V1. Why is this dataset important, and what does it specifically demonstrate?

Chris: The most important aspect of Dataset V1 is not just the addition of a batch of data, but it initially validates one thing: simulated operations from a large number of ordinary contributors can, after task design, success checking, filtering, smoothing, and data augmentation, form useful signals for pre-training the robot.

V1 includes 207 operational tasks, over 50,000 trajectories, and over 60,000 task and scenario variations. In the publicly available LIBERO-Plus evaluation, after continuing pre-training with the complete AXIS data, the overall success rate of π0.5 increased from 83.9 to 88.8, a 4.9 percentage point improvement. The RoboCasa control group with the same amount of data was at 57.5. This at least suggests that, under these evaluation conditions, model improvement depends not only on the quantity of individual data points but also on the diversity of tasks, scenarios, perspectives, and perturbation conditions.

This year, we will release Dataset V2, which will further expand in scale and cover more robot morphologies, tasks, and scenarios.

BlockBeats Question: How does Axis determine the value of a piece of data? What are the general criteria for judgment?

Chris: We don't just look at whether a piece of data has been collected; we continuously ask several questions: Is it clean? Does it bring something new? Can it be useful in the real world? And does it help the model overcome its real weaknesses?

The first checkpoint is usability. Has the operation been completed? Was there any freezing or disconnection? Can the action be replayed? Are there any device lags, operation drifts, or situations where objects pass through in violation of physical laws? We will also use this batch of data to train a lightweight model for a quick check-up. If it can't pass this checkpoint, the data will not enter the training pool.

Check the second level for any new information. The biggest fear for robots is to keep repeating questions they already know. We will examine how different this trajectory's scene, objects, and interactions are compared to existing data. If there are rare scene layouts, special materials, or unusual operation methods, then the data is valuable. While a significant amount of repetitive "pick up and put down" actions are useful for building a foundation, the real focus should be on complex tasks where multiple objects obstruct each other, requiring several consecutive steps to complete. Only this type of data can force the model to generalize.

Proceed to the third level to see if we can transition from simulation to reality. More simulation data does not necessarily mean better quality; the key is whether real-world variations are considered. Will the model still work if the lighting changes? What about the material? Different friction levels? Or if the camera angle is off? The more these variables are disturbed, the less likely the model will fail in a real-world scenario. If a set of simulation data always occurs under the same ideal conditions, it can only serve as basic pre-training material. Its value for transitioning to the real world is limited.

Lastly, assess the contribution to long-term iteration. When the model fails on a real system and a human takes over to correct a small segment of the trajectory, that specific correction is often extremely valuable as it precisely indicates where the model lacks knowledge. Similarly, trajectories from new tasks or new robot models can significantly help expand the model's capabilities. Conversely, if the model already performs well on a simple task, providing more similar data will result in diminishing returns.

Therefore, good data is more than just being "formatted correctly." It needs to be clean, innovative, applicable to the real world, and able to drive the model's ongoing improvement. We aim to invest our limited collection resources in data that can genuinely enhance the robot's intelligence.

BlockBeats Question: Since Axis is a robotics company, why does it need blockchain technology? Why did Axis choose the Base chain, and could you elaborate on the reasons?

Chris: This is a very sharp and crucial question. Our logic is simple: Axis is primarily addressing the data bottleneck in Physical AI, and to tackle this, a large number of global ordinary contributors need to participate. Blockchain technology is not the star of the show; it is more like an efficient basic tool used for traceability, verification, incentivization, and distribution.

In the era of large language models, training data is often a "black box": it's hard to understand where the data comes from, how it's processed, or how contributors' value is recognized. However, Physical AI directly involves a robot's actions in the real world, where data quality impacts safety. Therefore, this kind of black box cannot be easily accepted.

We have migrated part of the data production process of Physical AI to Base in order to make this process more transparent. Base is a low-cost blockchain network built on Ethereum. Specifically, task IDs, data traceability IDs, user IDs, and their relationships will all be recorded. This way, every action trace and every contributor can be traced, audited, and receive corresponding incentives.

Furthermore, we need a network with broad community coverage and low participation barriers. The Base community is highly globalized, and the Base chain has low usage costs, fast transaction confirmations, making it easy for us to record contributions and distribute incentives.

More importantly, we reward not just quantity, but high-quality contributions. For a robot model, garbage data is not only useless but may even be harmful. We use a dual scoring and point system to determine whether a piece of data has actually helped the model. The better the contribution, the higher the score, and the greater the reward. The efficiency and low cost of the Base chain perfectly match our global collaborative need for high-frequency, small incentives.

Who Will Foot the Bill for Robot Data Loop

BlockBeats Question: What is Axis' current commercialization path? In terms of robot data collection, model training, and actual deployment, what products or services do you mainly provide to customers? What feedback have existing customers given on this model?

Chris: Our commercialization path is quite clear, focusing on serving three types of customers and providing end-to-end solutions tailored to their respective needs.

The first type includes robot hardware and embodiment companies, such as Booster Robotics, Feagine Robotics, and AgiBot. They have their own hardware but often require more customized data and deployable models to complement their operational capabilities. We can assist them from task design, data collection, model training, to deployment, and also provide data solutions for their downstream customers.

The second type consists of companies developing vision-language-action models or world models, such as Manycore Tech and SomaStacks. The role of a world model is to help machine learning understand how the environment will change. They need a large amount of high-quality operational data to train universal models, and we provide them with high-quality task sets and datasets and can also engage in joint training.

The third type includes vertical industry enterprises, such as Lotus and Geely Automotive. They are not concerned about having the most advanced models but how to truly integrate robots into the production line. Therefore, we offer an end-to-end automation solution based on machine learning methods.

We value system capability highly, so our delivery is not just a data dump. We can adjust according to what the customer needs and where the internal capability boundaries lie: we can not only provide data but also deliver data-driven solutions. This approach enhances our competitiveness in serving customers and more directly helps customers improve efficiency and quality.

BlockBeats Question: Who are Axis's partners and customers? When different customers come to Axis, what are their typical needs?

Chris: The types of companies mentioned earlier are all our representative customers. They all seem to say they "lack data," but what they actually lack is different.

For example, a hardware manufacturer may have just developed a great robotic arm, but when it arrives at the customer site, as soon as the lighting changes or the items are rearranged, the robot loses its accuracy. They come to Axis not to just get a bunch of random videos but to address the real issue: how to make the robot work stably in more environments.

What we provide them with is not a pile of messy videos but structured data that has been generated, cleaned, and semantically annotated across multiple dimensions and diversities. When necessary, we also directly help them train strategies. The ultimate deliverable is to make their hardware more stable and generalizable in unstructured environments.

BlockBeats Question: From the platform's launch to now, can you share some real operational data that has not been publicly disclosed yet, such as the number of active registered contributors, task publication quantity, cumulative trajectories, and the activity and contribution of high-quality users?

Chris: Fifteen weeks since the platform's launch, we have accumulated over 80,000 registered contributors. This number has been deduplicated to exclude invalid accounts like bots and represents genuine and valid data. We have released a total of 1600 pre-training machine learning tasks, collected over 2 million data trajectories, which roughly translates to about 3500 hours of high-quality data.

However, we put more emphasis on user quality and stickiness. Currently, there are approximately 20,000 high-quality contributors, accounting for about 30%. In the past 30 days, 7800 of these users have remained active; in the last 7 days, 5300 have been active, with a 68% ratio of weekly active to monthly active users. In the last 30 days and the last 7 days, they have contributed 680,000 and 160,000 trajectories, respectively.

This level of activity and depth of involvement show us that users are interested in the "training is contributing, and contribution is rewarded" model and indicate that our community is forming a relatively mature and stable network of intelligent robot contributors.

4. The True Inflection Point of the Industry and Axis's Position

BlockBeats Question: Over the past two years, various humanoid robots, VLAs, and Physical AI have been hot topics, but most ordinary people have not truly used robots yet. How long do you think it will take for robots to achieve mass adoption? What will be the true inflection point—will it be the decrease in hardware costs, a leap in model capability, or the maturity of data infrastructure?

Chris: We believe that the true inflection point will be the maturity of data infrastructure.

Hardware costs have actually been rapidly declining. The advantage of the Chinese supply chain has made the manufacturing cost of robots no longer an insurmountable barrier. But why haven't ordinary people widely adopted robots yet? The core reason is that robots are not yet smart enough. They lack sufficient common-sense understanding of the physical laws of the real world and human intent.

It's like having a low-cost car with a good engine, but no driver's license and no experience on the road—having only hardware and algorithms without enough "road experience," the car still can't be driven well. Only when we can, like web scraping today, obtain interaction data from the physical world at low cost and scale, and turn it into digestible fuel for models, will robots truly take off.

We believe this inflection point is getting closer, and what Axis wants to do is to push it further ahead.

BlockBeats Question: In the robotics industry, both large model companies and whole-machine companies may build their own data in the future. Faced with these in-house data teams and Axis's current competitors in the same field, what is Axis's long-term moat? Is it a contributor network, task generation capability, data quality control, customer scenarios, or closed-loop training effectiveness?

Chris: This is a very critical and very practical question. Large model companies and whole-machine manufacturers will definitely build their own data teams in the future; this is almost an inevitable outcome of industry development. However, the real competition lies not in who can or cannot collect their own data, but in who can establish a cross-scenario, scalable, and continuously optimized data infrastructure.

In the short term, everyone is competing in data coverage and diversity; in the medium term, it's about converting data into model capabilities efficiently; in the long term, it's about who can truly embed themselves into a customer's intelligent production system and become an irreplaceable part.

Axis's moat will not be just a single point, such as a contributor network, data acquisition efficiency, or data processing capability. Single-point capabilities can all be replicated. What we care more about is connecting these capabilities into an ever-enhancing closed-loop system and slowly integrating them into a customer's training workflow.

For customers, accessing Axis should be straightforward, without the need for complex licensing, allowing for quick task deployment and data acquisition. However, once customers start using our simulation asset system, distributed collection network, and model correction feedback loop, both parties' training processes will gradually integrate. To replace us, customers are not just switching data providers, but they have to rebuild the simulation infrastructure, reconstruct the contributor network, data processing pipeline, and post-training correction process. Our moat is not closed; it is this kind of structural embedding.

We also value the ability to evolve alongside the model. If we were just selling data, we could easily be replaced at any time; but if we can continuously discover model weaknesses, design targeted tasks, provide post-training correction data, and truly help customers improve success rates and robustness, we become part of the model optimization feedback loop. At that point, our relationship with customers is no longer just a vendor and buyer; it is more like co-builders.

In the next 18 months, the industry will face a shortage of large-scale, high-coverage data, where volume and diversity remain key. Looking three years ahead, the industry may need more specialized data for specific domains and scenarios. The assets we truly want to accumulate are not a specific type of data but a programmable task generation system, a schedulable distributed contributor network, and a continuously optimized training loop. It can horizontally expand into more industries and vertically delve into a specific domain.

In summary, in the short term, we strive for scale; in the medium term, we strive for efficiency, and in the long term, we strive for embedding. Axis aims to become part of the Physical AI intelligent production system, rather than a data provider that can be easily replaced.

BlockBeats Question: In the 6–12 months after fundraising, what are Axis's main tasks and goals? Is it to continue expanding the scale of data, validate more real robot tasks, or acquire more paying customers? If we look back a year from now, what keywords do you hope the outside world will use to describe Axis?

Chris: In the next 6 to 12 months, we will concurrently advance product, ecosystem, and commercialization.

On the product side, V2 has transitioned us from a unidirectional collection platform to a complete training loop. The next step is to truly operationalize this system and scale it up. In September, we will further expand the first-person data collection pipeline, having accumulated tens of thousands of hours of data and are in discussions with cutting-edge model labs in North America. Around October, we plan to release the V2 version of the dataset, covering more robot morphologies and atomic capabilities. By the end of the year, we will release the first large-scale Human-Gated DAgger post-training dataset.

In terms of the ecosystem, we will continue to expand our global network, enter the Latin American and European markets. In the next 6 to 12 months, we aim to maintain the stable daily production capacity of first-person data above 500 hours, increase the daily production capacity of simulation data to above 50 hours, and gradually expand the production capacity of post-training data for DAgger.

On the commercial side, our goal is to complete 2 to 3 new paid pilot projects by the end of the year and be listed as a preferred supplier for foundational model companies early next year.

If we look back a year from now, we hope everyone will use the three words "scale, diversity, closed-loop" to describe Axis. Scale represents our ability to consistently provide industry-level data supply; diversity means we truly cover the complexity of the real world; closed-loop means we not only generate data but can continuously optimize around model shortcomings.

More importantly, when the industry mentions Axis, we hope they will say: "This is a system that accelerates the evolution of robotic models." We are addressing not only the issue of data quantity but also the efficiency of model evolution. Once customers integrate with Axis, faster model iteration, higher success rates, and broader scenario coverage are where our value lies.

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