Seniiors, the comprehensive cloud platform for managing senior-care facilities, announced the global rollout of its AI-powered suite—an innovation that integrates predictive analytics, intelligent automation, and real-time alerts directly into daily caregiving operations. This launch positions Seniiors at the forefront of an accelerating transformation in the global AgeTech market, which is currently valued at $2 trillion. At the heart of Seniiors’ mission is a simple but powerful goal: give time back to caregivers and improve every resident’s quality of life. The platform unites all aspects of care—planning, communication, and oversight—into a single intelligent dashboard. Its new AI suite analyzes resident patterns, predicting risks such as falls or sudden changes in well-being before they happen. Routine tasks, scheduling, and documentation are automated, freeing staff from paperwork to focus on personal interaction and empathy. Dynamic visual dashboards provide instant clarity across an organization—highlighting resident wellness trends, staffing balance, and emerging needs. Integrated sentiment tracking captures both resident mood and caregiver morale, while machine-learning models offer personalized housing or care recommendations. Together, these tools transform care homes from reactive to predictive environments. The upcoming Family Experience App will allow relatives to communicate with staff in real time, review updates, sign documents digitally, and participate in their loved ones’ daily moments from anywhere. Integrations with Apple Watch and Alexa will soon provide continuous wellness insights—heart rate, sleep, mobility—creating a seamless ecosystem of data and compassion.
Ascendo AI launches Knowledge Agent to generate persona-driven technical documentation with diagrams, flow charts and media from enterprise sources; reducing knowledge creation time by up to 80% and onboarding time by 70%
Ascendo AI announced the launch of the Ascendo AI Knowledge Agent, a Product Intelligence capability that automates enterprise technical documentation and knowledge creation. The Knowledge Agent converts fragmented case notes, knowledge bases, chat transcripts and files into structured, persona-driven documentation, complete with schematic diagrams, flow charts, images, video embeds, rich text and tables, using simple templates and customizable personas. Key features: Template-driven document generation (diagrams, flow charts, media, tables); Persona-driven outputs: Yoda the Teacher (Instructional), Martian the Technical Mark (Technical), Carraway the Summarizer (Key Points), Bourne the Brief (Concise), Spock the Analyst (Analytical); Ingests enterprise sources including case notes, knowledge bases, Slack/Teams transcripts, manuals, files and databases to create searchable knowledge assets; New UI to add Knowledge functionality across enterprise workflows with minimal configuration. Early customer impact – Large telecom customer: ~50% reduction in field service bulletin creation time. Leading AI company: ~80% reduction in knowledge creation time for onboarding and customer documentation. Large healthcare organization: ~70% reduction in training and onboarding time with automatically generated training modules. Day Knowledge Challenge – Ascendo AI is running a 7-Day Knowledge Challenge. Participants who create and publish knowledge using the Knowledge Agent will be spotlighted and eligible for prizes.
OpenAI creates an Expert Council on Well‑Being and AI to define healthy AI interactions, advise on sensitive scenarios and guide guardrails alongside clinical input from its Global Physician Network
OpenAI has formed a council to help it define and monitor “what healthy interactions with AI [artificial intelligence] should look like.” The Expert Council on Well-Being and AI is composed of eight researchers and experts focused on how technology affects mental health. OpenAI has consulted with many of these experts in the past, as when the company was developing parental controls and notification language for parents whose teen may be in distress. Moving forward, the council will monitor the company’s approach and will explore topics like how AI should behave in sensitive situations, what kinds of guardrails can support people using ChatGPT, and how ChatGPT can have a positive impact on people’s lives. “Alongside the Expert Council on Well-Being and AI advising on our broader approach to well-being, we’re also working with a multidisciplinary subset of mental health clinicians and researchers within the Global Physician Network to shape our model behavior and policies, and to test how ChatGPT responds in real-world situations. This work spans psychiatry, psychology, pediatrics, and crisis intervention, helping ensure our systems are grounded in clinical understanding and best practices,” the company said.
API‑first Obligo platform fully outsources deposit handling for property managers with embedded workflows, digital refunds, auditability and flexible payment options across leading property management systems
Obligo has launched its end-to-end deposit management solution. Obligo’s API-first platform integrates seamlessly with property management and financial systems, earning recognition for its scalability, security, and flexibility. Cash deposit handling has long been a costly and cumbersome process for property managers. Traditional manual steps – from certified checks to ledger updates and refund compliance – drive up operational costs, increase compliance risk, and provide an antiquated renter experience. Obligo renters enjoy a seamless move-in with digital payments and refunds – no checks, no snail mail. Alongside the full deposit option, renters can choose from Obligo’s existing products to best fit their financial needs, including No Deposit, Reduced Deposit™ or Deposits-in-Installments. “Deposit management solutions are not a new concept, but they’ve always come with a clear compromise,” said Roey Dor, CEO of Obligo. “It’s understandably difficult for property managers to trust new entrants in the deposit space – especially when those solutions aren’t embedded into their property management systems. Our approach delivers what the industry has been waiting for: a fully embedded, flexible, and trusted solution that truly rids both properties and renters of the burden of security deposits.”
Oura smart ring’s fastest‑growing segment is women in their early twenties, as the company emphasizes sleep, stress and women’s health over gym‑centric metrics
Health tech company Oura’s fastest-growing user segment isn’t tech billionaires or wellness-obsessed execs. It’s women in their early twenties. The question isn’t whether Oura is winning right now – with 80% of the smart ring market, clearly, it is. The question is whether it can maintain that lead as the wearables market splinters across demographics and use cases, and behind that, whether Oura even needs to capture every demographic to succeed. At Airbnb, 90% of the company’s revenue ties directly to people raving about their vacations, she suggested; at Oura, it’s people raving about their sleep scores. That organic enthusiasm is particularly strong among so-called corporate athletes, or high-performing professionals trying to optimize their health to stay sharp. These are people who’ve realized that running on fumes isn’t actually a sustainable career strategy or, as founder Dorothy Kilroy described them on stage, “people who are trying to be the best at their game. They want to make sure their sleep is dialed in. They want to know how to exercise. They want to look after their metabolic health.” It’s a demographic – largely millennials and Gen Xers with disposable income – that has made Oura wildly successful. The company has said it doubled its revenue last year and is on track to double it again this year. More impressive, says Kilroy, Oura’s retention at the 12-month mark hits the high 80s, while other wearables languish in the low 30s. Kilroy shrugs off the concern that Oura will lose customers to price-sensitive buyers. “Our members are getting a lot of value from [our product] and [are] happy to continue to pay.” In fact, Kilroy doesn’t seem particularly worried about capturing every demographic. Instead, she’s focused on keeping Oura’s core users happy while organically attracting new segments. And young women are becoming part of that core market – a trend that she credits to a broader shift, though Oura is also mindful of the opportunity it presents. It’s a smart play. The market for people wanting to optimize sleep, manage stress, and generally not feel terrible is arguably a lot bigger than the market for athletes obsessing over training load.
Walmart gives “Golden Tickets” to more than 100 entrepreneurs at its Open Call 2025; giving small businesses a path into Walmart and Sam’s Club stores and online
More than 500 entrepreneurs showed up to pitch their products at Walmart’s 12th annual Open Call, which was held last week at the retailer’s new headquarters in Bentonville, Ark. More than 100 small businesses received “Golden Tickets,” which gives suppliers the opportunity for their products to be sold in Walmart and Sam’s Club stores and online — joining the more than 60% of Walmart U.S. suppliers that are small businesses. In addition to product pitches, Open Call 2025 included presentations from 13 companies developing technologies that support U.S. manufacturing. These innovations ranged from shelf-life extension and yield optimization to alternative materials and advanced production techniques aimed at improving efficiency and reducing costs. Open Call also equips small businesses with practical tools to grow, compete and deliver for customers across the country. Entrepreneurs took part in intensive training and mentorship sessions focused on scaling production, improving packaging and strengthening financial and operational readiness — hearing directly from Walmart and Sam’s Club merchants, sourcing experts and past Open Call alumni about what it takes to succeed at national scale.
Zilch launches Intelligent Commerce using first‑party spend data and AI agents to deliver real‑time retail personalisation, boosting ROAS 20–50% and driving 55% higher average spend in beta
Zilch has launched two product innovations, Zilch Intelligent Commerce and Zilch Pay, marking the next phase of its rapid, data-driven growth, to gain full visibility across the customer journey, both online and offline. Leveraging one of the most comprehensive sets of first-party spend data in the market, the platform transforms customer engagement into actionable, real-time insights. Customers interact with the Zilch app more than 25 times a month and make payments almost 60 times a year, providing retailers with a rich and continuous flow of data. By harnessing this insight, Zilch Intelligent Commerce allows brands to target the right customers at the right time with personalised offers, dramatically improving Return on Ad Spend (ROAS) while reducing wasted media spend. Early results from the platform’s beta launch have demonstrated its transformative potential, with one major grocer reporting a 55% increase in average customer spend, another retailer growing market share by more than 15% in just 30 days, and a global travel brand achieving nearly 52% of new-customer conversions powered by Zilch, almost meeting its annual acquisition targets in six months. Across participating brands, the platform has driven 20 to 50% improvements in ROAS, all automatically optimised by Zilch’s AI agents. Alongside this intelligence platform, Zilch is preparing to launch Zilch Pay in the first half of 2026, offering a seamless one-click checkout that integrates the app, digital wallet, and card. The innovation is designed to enhance the consumer experience, reduce friction at payment, and improve conversion rates while lowering cart abandonment for retailers, ensuring a smoother and more efficient path from engagement to transaction.
Vonage’s Agentforce Identity Insights for contact centers flags recent or multiple SIM swaps, validates number type, carrier, caller ID and auto‑verifies leads to prioritize valid accounts and secure interactions
Vonage announced the launch of Vonage Agentforce Identity Insights and Fraud Detection, which provides insights for contact center agents, leveraging AI to help detect fraud risks, verify customers and validate effective communications channels in real time. With Identity Insights, including SIM Swap check powered by Vonage’s Network APIs, Vonage Agentforce Identity Insights and Fraud Detection helps users mitigate fraud across a number of use cases, such as identifying and flagging numbers that have had their SIM recently swapped as potentially fraudulent; validating mobile numbers before sending outbound SMS or making voice calls; and auto verifying phone numbers during lead creation to ensure lead quality. With this solution, organizations can simplify customer engagement and outreach by ensuring number validity so they can prioritize valid accounts, as well as verify number type to target customers with the most effective channel for communications. This rich phone intelligence – including number type, carrier, validity, caller ID name, and SIM swap status – enables contact centers to: Flag potential fraud risks by detecting numbers with recent or multiple SIM swaps and escalating suspicious transactions; Verify customer identities seamlessly by matching caller ID against CRM records, and securing interactions without added friction; Optimize outbound engagement by automating SMS/WhatsApp for mobile devices, while specialist sales teams call landline-only customers; Enhance lead quality by verifying numbers at lead creation to eliminate invalid or outdated details; Deliver proactive notifications by triggering reminders and alerts only on verified numbers, improving engagement rates
Walmart partners with OpenAI to enable Instant Checkout in ChatGPT; ushering in “agentic commerce” where customers chat, plan and buy as AI learns, predicts and automates everyday shopping
Walmart announced a new partnership with OpenAI that will start with allowing customers and members to soon shop Walmart through ChatGPT using Instant Checkout. Whether planning meals, restocking household essentials, or finding something new, customers can simply chat and buy, and Walmart will handle the rest. Walmart described the collaboration as an example of “agentic commerce,” where AI shifts from a reactive tool to a proactive system that learns, plans and predicts, helping customers anticipate their needs before they do. The concept aligns with OpenAI’s goal of enabling agents to assist users across everyday tasks from organizing information to completing purchases all within one interface. This partnership builds on the multiple ways Walmart and Sam’s Club are already using AI, like enhancing product catalogs, improving customer care resolution times and promoting AI literacy among associates. From enhancing the product catalog to reduce fashion production timelines by up to 18 weeks, to ensuring a more seamless shopping journey and cutting customer care resolution times by up to 40%, the company is applying AI so every shopping experience is more convenient and more rewarding. As Walmart defines what’s next for retail, its approach remains the same: people-led and tech-powered, helping people save money and live better.
Starburst debuts AI‑ready lakehouse with model‑to‑data support, multi‑agent interoperability and open vector access; adding observability, guardrails and visualizations to operationalize the agentic workforce
Starburst announced at AI & Datanova, a new set of capabilities designed to operationalize the Agentic Workforce—a paradigm where humans and AI agents collaborate seamlessly across workflows to reason, decide, and act faster and with confidence. With new, built-in support for model-to-data architectures, multi-agent interoperability, and an open vector store on Iceberg, Starburst delivers the first lakehouse platform that empowers AI agents, with unified enterprise data, governed data products, and metadata, empowering humans and AI to reason, act, and decide faster while ensuring trust and control. To further strengthen enterprise confidence in AI, Starburst is introducing advanced observability and visualization features for its agent framework. Organizations can now monitor usage of LLM interactions, set guardrails with usage limits, and view activity through intuitive dashboards. In addition, Starburst’s agent can visualize responses into charts and graphs giving teams not only accurate answers but also clear, actionable insights. These capabilities provide a new level of transparency, governance, and usability as enterprises scale AI adoption. Starburst’s new AI capabilities are built upon the core principle of flexibility, giving organizations the freedom to choose between model-to-data and data-to-model architectures. This approach enables enterprises to scale AI securely, while preserving sovereignty, reducing infrastructure costs, and ensuring compliance. These enhancements include: Multi-Agent Ready Infrastructure: A new MCP server and agent API allows enterprises to create, manage, and orchestrate multiple AI agents along-side the Starburst agent. This enables customers to develop multi-agent and AI application solutions that are geared to complete tasks of growing complexity. Open & Interoperable Vector Access: Starburst unifies access to vector stores, enabling retrieval augmented generation (RAG) and search tasks across Iceberg, PostgreSQL + PGVector, Elasticsearch and more. Enterprises gain flexibility to choose the right vector solution for each workload without lock-in or fragmentation. Model Usage Monitoring & Control: Starburst offers enterprise-grade AI model monitoring and governance. Teams can track, audit, and control AI usage across agents and workloads with dashboards, preventing cost overruns and ensuring compliance for confident, scalable AI adoption. Deeper Insights & Visualization: An extension of Starburst’s conversational analytics agent enables users to ask questions across different data product domains and provide back a natural language response in natural language, a visualization, or combination of the two. The agent is able to understand the user intent and question to do data discovery to find the right data before query processing to answer the question.