Nasdaq has partnered with Nasdaq Private Market (NPM) to provide greater price transparency and valuation visibility into private, pre-IPO companies, including unicorns and startups. The Tape D private company dataset, available through API integration via Nasdaq Data Link, addresses critical transparency challenges by helping investors evaluate private holdings with greater confidence, enabling banks to structure private transactions more effectively, supporting wealth advisors and shareholders in managing liquidity needs, and equipping private companies with valuable insights for capital raises and tender offers. The comprehensive data product delivers real-time private market pricing by integrating primary round data, secondary market transactions, and accounting data. The launch of this data partnership marks the latest step in Nasdaq’s commitment to enhancing transparency, access, and portfolio management capabilities across the public-to-private investment spectrum.
FINNY unveils intent search to help advisors pinpoint high-intent prospects faster based on real-time online behavior
FINNY the AI-powered prospecting and marketing platform built specifically for financial advisors, has launched Intent Search, a feature that allows advisors to identify and engage with prospects actively seeking financial guidance. Powered by 1.8 billion proprietary intent signals that are updated daily, it enables advisors to surface high-intent prospects based on real-time online behavior. Advisors can select keywords related to their services. FINNY identifies prospects who have recently researched those topics, pinpointing what they’re interested in and when they were actively searching. FINNY has also released its Prospect Enrichment and AI Voicemails features. Prospect Enrichment enables advisors to upload external contacts and automatically matches them to FINNY’s database. Meanwhile, AI Voicemails allow advisors to deliver ringless, personalized voicemails at scale. They can select from multiple voice options to suit their preferences, and messages are able to circumvent spam filters. Each voicemail can be paired with a follow-up email to create efficient outreach that retains a human touch. Since its launch, the Prospect Enrichment feature has already led to the upload and enrichment of more than 8,000 prospects, signaling strong demand and immediate value.
Startup Rime’s text-to-speech model can quickly generate “infinite” new voices of varying genders, ages, demographics and languages just based on a simple text description of intended characteristics; while incorporating social context, individual speech habits and non-verbal conversational cues
Startup Rime is tackling this challenge with Arcana text-to-speech (TTS), a new spoken language model that can quickly generate “infinite” new voices of varying genders, ages, demographics and languages just based on a simple text description of intended characteristics. The model has helped boost customer sales — for the likes of Domino’s and Wingstop — by 15%. Rime’s multimodal and autoregressive TTS model was trained on natural conversations with real people (as opposed to voice actors). Users simply type in a text prompt description of a voice with desired demographic characteristics and language. Rime’s Mist v2 TTS model was built for high-volume, business-critical applications, allowing enterprises to craft unique voices for their business needs. “The customer hears a voice that allows for a natural, dynamic conversation without needing a human agent,” said Lily Clifford, Rime CEO and co-founder. Rime’s model generates audio tokens that are decoded into speech using a codec-based approach, which Rime says provides for “faster-than-real-time synthesis.” Rime’s data incorporates sociolinguistic conversation techniques (factoring in social context like class, gender, location), idiolect (individual speech habits) and paralinguistic nuances (non-verbal aspects of communication that go along with speech). Rime intends to give customers the ability to find voices that will work best for their application. They built a “personalization harness” tool to allow users to do A/B testing with various voices. After a given interaction, the API reports back to Rime, which provides an analytics dashboard identifying the best-performing voices based on success metrics. Another KPI customers are maximizing for is the caller’s willingness to talk to the AI. They’ve found that, when switching to Rime, callers are 4X more likely to talk to the bot.
Walmart bets on AI assistant Sparky to ignite sales
Walmart is expanding its AI offerings by introducing a new shopping assistant. Customers can now access the Gen AI-powered “Sparky” on the Walmart app. “Sparky helps customers search to find items, synthesize reviews, and offers insights to prepare for any occasion — from looking up current sporting events and finding the right jersey to planning celebrations and picking out the perfect toy,” Walmart said. In addition to making recommendations, Sparky also provides instant and comprehensive answers to product-related questions, helping customers quickly understand specific features, compare items and make informed choices. Soon, Sparky will do even more — giving customers the power to customize their experience, from automatically reordering household essentials to booking services that simplify even the most complex shopping tasks. Walmart said the assistant will be multi-modal (able to understand text, images, audio and video), “seamlessly weaving into customers’ lives to unlock instant access to the products and services they need, whenever and however they shop.”
HubSpot’s ChatGPT connector can help marketers find the highest-converting cohorts from their recent contacts and create a tailored nurture sequence to boost engagement while ensuring role-based data access
HubSpot is the first CRM to launch a deep research connector with ChatGPT. HubSpot customers who have admin controls can enable the connector for their organization by going to ChatGPT and turning on the HubSpot deep research connector function, selecting HubSpot as a data source, and authenticating their account. From there, any user in the organization can toggle it on, sign in, and start asking questions. In addition to being easy to use, the HubSpot deep research connector is also easy to trust. We built it to ensure users only see the CRM data they’re allowed to access in HubSpot. For example, individual sales reps will only see pipeline data for deals they own or manage. With the HubSpot deep research connector, customer data is not used for AI training in ChatGPT. Within ChatGPT, for example: Marketers can ask “find my highest-converting cohorts from recent contacts and create a tailored nurture sequence to boost engagement,” then use the insights to launch an automated workflow in HubSpot. Sales teams can find new opportunities by asking, “segment my target companies by annual revenue, industry, and technology stack. Based on that, identify the top opportunities for enterprise expansion,” then bring them back to HubSpot for prospecting. Customer success teams can say, “identify inactive companies with growth potential and generate targeted plays to re-engage and revive pipeline,” then take those actions in HubSpot to drive retention. Support teams can say, “analyze seasonal patterns in ticket volume by category to forecast support team staffing needs for the upcoming quarter,” and activate Breeze Customer Agent in HubSpot to handle spikes in support tickets.
Walmart’s Sparky helps customers search to find items, synthesize reviews, offers insights, instant and comprehensive answers to product-related questions
Walmart is expanding its AI offerings by introducing a new shopping assistant. Customers can now access the Gen AI-powered “Sparky” on the Walmart app. “Sparky helps customers search to find items, synthesize reviews, and offers insights to prepare for any occasion — from looking up current sporting events and finding the right jersey to planning celebrations and picking out the perfect toy,” Walmart said. In addition to making recommendations, Sparky also provides instant and comprehensive answers to product-related questions, helping customers quickly understand specific features, compare items and make informed choices. Soon, Sparky will do even more — giving customers the power to customize their experience, from automatically reordering household essentials to booking services that simplify even the most complex shopping tasks. Walmart said the assistant will be multi-modal (able to understand text, images, audio and video), “seamlessly weaving into customers’ lives to unlock instant access to the products and services they need, whenever and however they shop.”
Walmart optimizes social customer engagement with HGS AI and automation technology for filtering and tagging social media mentions, analytics and to reply to and engage with more customers online
Walmart Inc. is effectively managing millions of mentions per year across disparate social media networks. Part of the volume is generated by local Walmart store pages on Facebook, where the retailer wants the customer experience to match that of their neighborhood store. Walmart turned to AI-based business process improvement platform provider HGS to help improve its end-to-end social customer care, brand-building, and customer engagement. HGS provided Walmart with AI and automation technology for filtering and tagging social media mentions, analytics, a crisis management solution coordinated with a public relations firm, and a customizable social media management platform to reply to and engage with more customers online. In addition, Walmart was able to leverage a team of HGS social brand ambassadors, and a social media playbook. As a result, Walmart saved more than $12 million by filtering non-actionable mentions via enhanced automation and AI. The retailer also obtained more than 1.2 million customer insights, removed more than 220,000 spam mentions (such as hate speech and bullying), engaged 180,000 customers with personalized responses across four social media platforms, and generated 200,000 e-commerce site visits from reactive comments.
Walmart optimizes social customer engagement with HGS AI and automation technology for filtering and tagging social media mentions, analytics and to reply to and engage with more customers online
Walmart Inc. is effectively managing millions of mentions per year across disparate social media networks. Part of the volume is generated by local Walmart store pages on Facebook, where the retailer wants the customer experience to match that of their neighborhood store. Walmart turned to AI-based business process improvement platform provider HGS to help improve its end-to-end social customer care, brand-building, and customer engagement. HGS provided Walmart with AI and automation technology for filtering and tagging social media mentions, analytics, a crisis management solution coordinated with a public relations firm, and a customizable social media management platform to reply to and engage with more customers online. In addition, Walmart was able to leverage a team of HGS social brand ambassadors, and a social media playbook. As a result, Walmart saved more than $12 million by filtering non-actionable mentions via enhanced automation and AI. The retailer also obtained more than 1.2 million customer insights, removed more than 220,000 spam mentions (such as hate speech and bullying), engaged 180,000 customers with personalized responses across four social media platforms, and generated 200,000 e-commerce site visits from reactive comments.
RavenDB’s new feature allows developers to run GenAI tasks directly inside the database and use any LLM on their terms without requiring any middleware, external orchestration, or third-party services
RavenDB, a high-performance NoSQL document database trusted by developers and enterprises worldwide, has launched its new feature, bringing native GenAI capabilities directly into its core database engine, eliminating the need for middleware, external orchestration, or costly third-party services. RavenDB’s new feature supports any LLM (open-source or commercial), allowing teams to run GenAI tasks directly inside the database. Moving from prototype to production traditionally requires complex data pipelines, vendor-specific APIs, external services, and significant engineering effort. With this feature, RavenDB removes those barriers and bridges the gap between experimentation and production, giving developers complete control over cost, performance, and compliance. The result is a seamless transition from idea to implementation, making the leap to production almost as effortless as prototyping. What sets RavenDB apart is its fully integrated, flexible approach: developers can use any LLM on their terms. It’s optimized for cost and performance with smarter caching and fewer API calls, and includes enterprise-ready capabilities such as governance, monitoring, and built-in security, designed to meet the demands of modern, intelligent applications. By collapsing multiple infrastructure layers into a single intelligent operational database, RavenDB’s native GenAI capabilities significantly upgrade its data layer. This enhancement accelerates innovation by removing complexity for engineering leaders. Whether classifying documents, summarizing customer interactions, or automating workflows, teams can build powerful features directly from the data they already manage, with no dedicated AI team required.
Flinks Open Banking solution partners Trask’s Connector to enable full rollout of data-in capabilities in just three months; delivery of new digital banking products in as little as eight months; loan approval time reduced from days to seconds
Flinks, a financial data connectivity and Open Banking solution provider, has announced a partnership with Trask Solutions. Trask helps data recipients build data-in infrastructure more efficiently, delivering business solutions that fully leverage Open Banking data. Data recipients can use Trask’s Data-In Connector that has been successfully deployed for numerous clients across Europe and that is now fully localized for use in Canada. The solution supports the FDX standard and comes pre-integrated with Flinks. Trask provides assets, accelerators, and platforms that use Open Banking data for digitizing banking products and services such as: online loans, transaction-based loan adjudication mechanisms, online account opening for retail and commercial clients, mortgage process automation, financial management and advisory, investment dashboards and multi-banking. With Trask’s technology stack, European financial institutions have already achieved significant benefits, including: Full rollout of data-in capabilities in just three months; Delivery of new digital banking products in as little as eight months; Loan approval time reduced from days to seconds; Increased the quality of new-to-bank loans to be on-par with the loans to existing clients; Processing cost of invoice factoring lowered to almost zero enabled factoring offerings to SMEs; Doubled conversion rates and increased sales by 30% by offering hyper-personalized offers to both existing and new clients, leveraging Open Banking data sources.