NEW YORK--(BUSINESS WIRE)--Jul 7, 2026--
ThetaRay, a leader in AI infrastructure for financial crime compliance, today announced the appointment of Luis Pinedo as Chief Strategic Customers Officer. Previously Pinedo was Group VP of Compliance at Santander Bank, where during his 16-year tenure, he led global Financial Crime Compliance (FCC) transformation initiatives across operating models, processes, and technology platforms.
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In his new role, Pinedo will bring key perspectives from tier-one banking operational challenges to ThetaRay’s product development and design. He will lead strategic customer engagement as well as product strategy across ThetaRay’s entire compliance platform, including Transaction Monitoring, Screening, Customer Risk Assessment, and Agentic Investigations.
The appointment comes at a critical time in the RegTech industry, when regulators worldwide are strictly scrutinizing financial institutions’ anti-money laundering (AML) practices, while simultaneously updating their compliance expectations. In 2026 alone, FinCEN has announced a plan to judge programs based on effectiveness, while the UK’s Financial Conduct Authority is integrating generative AI across its internal supervisory workflows, and the EU AML Package will require member states to implement ownership registry rules under AMLD6. As a result, financial firms face an urgent mandate to deploy proven, institutional-grade AI architecture.
“After many years working inside a global bank, I believe financial crime compliance is reaching an inflection point,” said Luis Pinedo, Chief Strategic Customers Officer at ThetaRay. “Traditional rules-based approaches are no longer sufficient as regulators themselves become heavily data-led. I joined ThetaRay because AI has been part of the company’s DNA for more than a decade. The use of LLM-based agents is a natural evolution of that journey, and I want to help shape the company’s path to helping banks make that transition in a practical, scalable way.”
"To combat sophisticated financial crime at scale, and meet new effectiveness standards of global regulators, technology solutions must be proven in production,” said Brad Levy, CEO of ThetaRay. "Luis has spent nearly two decades inside one of the world’s largest banking institutions, spearheading the exact enterprise transformations our clients require to satisfy global regulators. His domain expertise will strengthen ThetaRay’s foundational AI infrastructure, which already monitors over $20 trillion in transactions annually of the world’s leading financial institutions."
By placing Pinedo at the helm of strategic customer and product strategy, ThetaRay reinforces its commitment to delivering an integrated, cross-functional compliance platform that eliminates traditional operational silos, while drastically improving risk reporting to prove effectiveness to modern examiners.
About ThetaRay
ThetaRay is a leading provider of AI infrastructure for financial crime compliance. Its proprietary AI mimics human intuition to detect hidden criminal networks and deliver actionable insights. The platform’s solutions overcome the limitations of traditional rule-based systems by shortening long implementation lifecycles, enabling efficient, risk-aware compliance operations. Ray, the agentic AI investigation platform enables financial institutions to autonomously investigate alerts with explainable, audit-ready case files. By transforming compliance from a regulatory obligation into a driver of growth, ThetaRay allows firms to scale faster and expand confidently. Implemented at leading institutions including Santander, Clear Bank, Mashreq Bank and Payoneer, ThetaRay fosters trust across the global financial ecosystem. For more information, visit www.thetaray.com.
Luis Pinedo joins ThetaRay as Chief Strategic Customers Officer.
SHANGHAI--(BUSINESS WIRE)--Jul 7, 2026--
Robbyant, an embodied AI company within Ant Group, today announced the launch of LingBot-Depth 2.0, a next-generation spatial perception model, alongside its foundational visual model, LingBot-Vision. This release marks a significant leap in robotic spatial perception, empowering robots to accurately understand and navigate the physical world.
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From "Seeing" to "Seeing Accurately"
Building upon the success of LingBot-Depth, which pioneered the Masked Depth Modeling (MDM) technique to resolve depth sensing challenges for transparent and reflective surfaces, LingBot-Depth 2.0 represents a massive scale-up in training data and performance.
Trained on 150 million samples, the new model achieves top rankings in 12 out of 16 depth completion benchmarks. Most notably, in the most demanding indoor scenarios with massive depth loss, LingBot-Depth 2.0 halves the depth error compared to its predecessor, reducing RMSE from 0.132 to 0.062. Furthermore, it demonstrates particularly outstanding performance in traditional failure cases for conventional depth cameras, such as glass, mirrors, and transparent objects.
This breakthrough is driven by LingBot-Vision, a novel visual foundation model and the first in the industry to use "boundary structure" as a pre-training objective. With sub-pixel-level boundary localization and spatial structure understanding, LingBot-Vision provides the precise visual representations required for robust spatial perception.
Remarkably, the pre-training corpus for LingBot-Vision consists of only 160 million images, which is an order of magnitude smaller than that of DINOv3. Moreover, it brings highly stable object boundary determination capability, enabling continuous tracking of object boundaries within videos.
Beyond supporting LingBot-Depth 2.0, LingBot-Vision is a versatile foundation model capable of diverse downstream tasks.
Collaboration with Orbbec on Applications
For commercial applications, LingBot-Depth 2.0 has been professionally certified by the Depth Vision Laboratory of Orbbec, a leading provider of robotics and AI vision. Real-world testing using chip-level depth data from Orbbec’s Gemini 330 series stereo 3D cameras demonstrates significant improvements in edge clarity, object contour integrity, small object recognition, long-range depth estimation, and robustness in complex lighting and material conditions.
Moreover, within the product matrix of Orbbec's newly released Robot-Free Data Collection Hardware Platform, the RGB-D EGO device will integrate a customized LingBot-Depth model optimized for data collection. Moving forward, it will further integrate an advanced commercial version to continuously optimize missing depth completion, object edges, and spatial structures, delivering a precise, stable, and highly usable real-world data foundation for embodied AI training.
In collaboration with Robbyant, Orbbec will launch new products integrating LingBot-Depth 2.0:
Additionally, Robbyant has open-sourced the model weights of LingBot-Vision. The company remains committed to building the robotic vision foundation together with industry partners, resolving the critical bottlenecks of "seeing, seeing accurately, and seeing stably" in the real physical world, and accelerating the commercial deployment of embodied intelligence.
For more information about LingBot-Depth 2.0 and LingBot-Vision, please visit:
GitHub: https://github.com/Robbyant/lingbot-vision
Hugging Face: https://huggingface.co/collections/robbyant/lingbot-vision
About Robbyant
Robbyant is an embodied intelligence company within Ant Group, dedicated to advancing embodied intelligence through cutting-edge software and hardware technologies. Robbyant independently develops foundational large models for embodied AI and actively explores next-generation intelligent devices, aiming to create robotic companions and caregivers that truly understand and enhance people’s everyday lives and deliver reliable intelligent services across key use cases, such as elderly care, medical assistance, and household tasks.
To learn more about Robbyant, please visit: www.robbyant.com
Comparison of object boundary and spatial structure recognition across major models. LingBot-Vision demonstrates superior clarity and stability.
LingBot-Depth 2.0 leads in real-world sensor depth completion benchmarks.
LingBot-Depth 2.0 reconstructs complete and planar 3D structures in challenging scenarios involving mirrors and glass
