How the AI-Powered Suggestions Feature Works
To utilize the AI-powered suggestions feature, simply record your tasks as you typically would. The app's AI model then checks if it can provide a suggestion. For example, when writing "Buy paper napkins", you'll receive suggestions for links to Amazon, Walmart, and eBay with "paper napkins" as the search terms. If you mention a birthday or anniversary, the app will prompt you to add a reminder to your calendar, send a text, or purchase a gift card.Image Credits: Twos
App Integrations and Use Cases
Currently, Twos has 27 integrations with various apps like Amazon, Walmart, eBay, Instacart, Expedia, Google Search, Google Maps, Google Flights, Uber Eats, contacts, calls, messages apps, email, IMDb, Spotify, Apple Music, YouTube, ZocDoc, Ticketmaster, and StubHub. These integrations cover a wide range of use cases including shopping, restaurants, food, music, movies, reminders, and events.Image Credits: Twos
Current Limitations and Future Improvements
The feature generally functions well in most situations. Nevertheless, at present, it mainly suggests U.S.-centric services for categories like shopping, ticket purchases, and food delivery. The company needs to enhance localization for users outside the country. This is a crucial aspect that will determine the app's wider appeal and usability.Twos was founded in 2021 by former Google engineer Parker Klein and Joe Steilberg. With more than 25,000 active users across the web, Android, and iOS, the app is free to use. Users have the option to activate "Plus" features such as adding tags and hyperlinks, creating a custom home screen, utilizing an auto-sorting feature, and leveraging templates. Each feature comes at a one-time cost of $2.
Last year, the startup introduced an AI assistant to assist with list creation. Additionally, other apps like Hypelist are also leveraging AI models to help people build their recommendation lists, highlighting the growing trend in this area.
Unleashing the Power of Dynamic Memory in Enterprise Workflows
Across AI's Mission in the Enterprise World
Across AI is emerging as a key player in this arena, coming out of stealth to develop a “dynamic memory system” specifically tailored for complex enterprise workflows. The startup is targeting chief revenue officers and sales teams, offering a platform that seamlessly connects with all their internal and external enterprise data sources. This creates a shared “agentic memory” that proves invaluable in identifying and qualifying new sales opportunities, spotting risks, and suggesting relevant questions for sales teams to ask their customers.As Steven Mih, the co-founder and CEO of Across AI, explains, “Sales teams often face challenges in obtaining and utilizing the right information at the right time. Critical knowledge gets bottlenecked among a few experts or gets lost in vast amounts of unstructured data, leading to inefficiencies and costly errors. Existing AI solutions often fall short as they lack deep integration and contextual understanding.”
Mih's previous venture, Ahana, was a Google Ventures-backed company that built commercial services on Presto. After selling Ahana to IBM last year, Mih decided to embark on a new journey with Across AI. He joined forces with renowned professors Dr. Niloufar Salehi and Dr. Afshin Nikzad from UC Berkeley and Stanford University respectively, who have been researching ways to improve the efficacy of AI systems in “high-stakes” settings.
Building the Memory: Connecting Enterprise Stack Components
Across AI is designed as a web app and chatbot that integrates with various parts of the enterprise stack, including CRM systems, communication and collaboration tools, and calendars. By doing so, it builds a comprehensive memory and develops contextual understanding. This allows it to provide just-in-time assistance wherever a user is working, without disrupting their workflow.“As the system shows up where users already are, such as in Slack or [Microsoft] Team’s app, it offers seamless support and enhances productivity,” Mih said. The memory continuously adapts, retaining only relevant information and discarding outdated data. This raises interesting questions about how it determines relevance, which Mih attributes to the development of a “deep understanding of the workflow context.”
The system actively tracks, timestamps, and monitors information updates, ensuring that data remains up-to-date and conflicts are resolved. Unlike traditional AI systems that treat all data equally, Across AI's agentic memory system prioritizes information based on its contextual importance. In cases of ambiguity, determinations are escalated to relevant personnel like sales managers or product managers.
Data Security: A Foundational Aspect
Enterprises have been cautious in adopting generative AI due to concerns about data privacy and security. Mih understands this concern and emphasizes that data security is a “foundational aspect” of Across AI's agentic memory platform.“Our memory system operates within the company's secure environment, maintaining strict access control over sensitive information and not exposing data to external models for training,” Mih said. The company plans to offer both SaaS and cloud-premises deployment options to meet the diverse security and compliance requirements of enterprises.
There are subtle synergies between Across AI and Mih's previous startup, Ahana. Ahana focused on enabling users to query vast amounts of data via Presto, handling the complexities of infrastructure setup and maintenance. Across AI is addressing the same problem from a different perspective, aiming to help users analyze large amounts of data quickly.“This experience has deepened my understanding of the challenges enterprises face in navigating complex data ecosystems,” Mih said.