Google AI Mode Update: PDF Summaries, Canvas Planner & Live Lens Now Rolling Out to Android and iOS Users

Reading Time: 3 minutesGoogle AI Mode’s latest features include PDF summarization, Canvas planning, and real-time visual search powered by Project Astra, now rolling out to Android and iOS.

Google • Tech News

Google AI Mode Update- PDF Summaries, Canvas Planner & Live Lens Now Rolling Out to Android and iOS Users

Written by:

July 31, 2025

Reading Time: 3 minutes

Earlier this month, Google added a new function to its Search application, now available to users of both Android and iOS in India. This feature, powered by generative artificial intelligence, goes by the name AI Mode. It marks another small but deliberate step in the broadening role of machine intelligence in daily affairs.

PDF Uploads Now Supported & Deep Research Capabilities

The company has since introduced further updates. Once the app has been brought to its latest version, users will gain the ability to upload PDF files. These can then be read in full by the AI, which will produce a shortened version, stripped of excess and reduced to its essential points. For pupils grappling with science texts, this may offer a real advantage. What might have taken an hour to work through becomes something clear and quickly understood.

Also Read:  Best Buy Black Friday in July sale: best anti-Prime Day tech deals

But the capacity does not end there. Should the reader wish to look further into a topic, the system can seek out more detail, pulling from science sources that are known to be credible. In this way, the tool becomes more than a convenience; it offers a quiet shift in how people come to know things, not by search, but by suggestion.

New Canvas Tool Helps Plan and Organize Thoughts with AI Support

Another addition is something called the Canvas, a tool meant to help users set out their intentions for the days or weeks ahead. It is simple in appearance, but its purpose is plain enough: to bring order to a disordered routine. For students and office workers alike, it offers a way to keep one’s mind on the task at hand. A student, for example, need only type a short phrase “Create a Canvas” into the search bar while AI Mode is active. In an instant, panels appear, and from there the user may shape the guide to suit their own ends, drawing upon course notes, syllabi, or other files at hand.

There is also a second feature of note: Search Live, now with the use of video input. Here, too, the change is quiet but far-reaching. It draws directly from Project Astra, a system built on Google’s most advanced speech technologies. For those unfamiliar with the name, Astra is designed to listen, to hold long and varied conversations, and to carry out a series of commands without delay or confusion. The idea, it seems, is not merely to assist but to anticipate, to reduce the friction of everyday tasks until they are almost forgotten.

Also Read:  Types of digital technology

Real-Time Visual Problem Solving

Take, for example, a case where the user points their camera at a page covered with a difficult maths problem. The system can break it down at once, offering each step in turn, so that the answer is not merely given but explained. To begin, one need only open the Lens tool in the Google app, tap on the Live symbol, and speak aloud whatever question comes to mind while keeping the camera fixed on the subject.

The conversation need not be stiff or limited. With AI Mode active, the user may speak freely, and the machine will reply in kind, drawing on the images before it, whether still or in motion, from different sides or at different distances. In this way, the tool becomes almost like a companion, able to see and think at once. These features, taken together, form the latest part of the AI Mode expansion. They will arrive by means of an app update, first to users in the United States, where they are expected by the end of the week. Other countries, India among them, will receive the changes shortly after.

Final Words

The spread of AI Mode in Google is not merely another technological upgrade but the next step of the corporation in its effort to be our digital butler, therapist, and study buddy in one. Google is basically saying, “We got this.” PDF summarization that would leave CliffsNotes in tears, a Canvas feature that will help us to get our lives together and Search Live that can solve math problems before most students can start panicking about them, this is Google. It is yet to be determined whether this is a glorious procession of mankind into an easy life of productivity or a gradual descent into AI addiction. 

Also Read:  Concerns to Address Before Deciding on an SAP Solutions Provider

There is one thing that is definite, and that is that students all over the world are soon going to find out how much their textbooks actually needed to say in the first place. With these features being implemented around the world, we must ask ourselves whether Google is interested in making us smarter, or whether they just want to make thinking optional. In any case, our cameras will at least have something to look at other than our lunch.

Last modified: July 31, 2025

New CMF Watch 3 Pro Debuts Featuring ChatGPT and Up to 13 Days of Battery Backup Previous Story:
New CMF Watch 3 Pro Debuts Featuring ChatGPT and Up to 13 Days of Battery Backup
How Insurance Companies are Using NLP to Streamline Application Approvals Applying for insurance can be a tedious process. Between long forms, complex documentation, and the need for quick decisions, insurers face the challenge of balancing speed with accuracy. For insurance companies operating in a competitive B2B environment, the pressure is even higher. Delays in approvals can mean lost clients and declining satisfaction scores. To address this, leading firms are now adopting AI in insurance application workflows, with NLP in insurance emerging as a central enabler of this shift. Natural Language Processing (NLP) is a branch of artificial intelligence that is highly effective in automating key parts of the insurance application lifecycle. By understanding and extracting meaning from human language in documents, emails, and chats, NLP is reducing manual workloads and bringing much-needed clarity and speed to application approvals. The Traditional Approval Bottleneck Traditionally, the insurance application process has required human reviewers to go through lengthy paperwork. For instance, when a small business applies for commercial insurance, underwriters often sift through tax forms, financial statements, contracts, and prior insurance history. This not only slows down turnaround times but also increases the chances of human error, especially when information is buried in unstructured formats. In addition, every application may have unique nuances that require back-and-forth communication between the insurer and applicant. Handling these interactions manually further delays the process. Automating Document Review with NLP One of the most valuable uses of NLP in insurance is automating the review of unstructured text data. NLP algorithms can scan through documents submitted by applicants, such as business licenses, income statements, or even handwritten notes, and extract relevant details like coverage amount requested, past claims, or business categories. For example, a mid-sized insurer in the Midwest used an NLP model trained on historical underwriting documents to automatically extract business classification codes (NAICS codes) from client descriptions. The model improved classification accuracy by 23% and reduced processing time per application from 45 minutes to under 10 minutes. By doing the “reading” automatically, NLP frees up underwriters to focus on high-risk or complex cases, improving both efficiency and decision quality. Speeding Up Communication and Follow-Ups Another area where AI in insurance application workflows has brought measurable improvements is client communication. Insurers receive large volumes of emails and messages daily, often containing key details that can impact policy decisions. With NLP models integrated into their CRM and email systems, insurance providers can now triage incoming communication automatically. For instance, if an applicant emails additional documents or asks about policy exclusions, the system can categorize the message and extract key updates in real time. A California-based property insurance company implemented NLP-driven email parsing to process 2,000+ customer emails daily. The result: 92% of messages were routed to the correct team automatically, and average response time dropped from 24 hours to under 6. Pre-Filling Forms Using NLP Manual data entry remains one of the most time-consuming tasks for both clients and insurance agents. NLP in insurance is reducing this burden by reading submitted documents and pre-filling application forms. For instance, when a small manufacturing company applies for a workers’ compensation policy, it may submit an employee roster, payroll data, and workplace safety records. NLP can extract names, salaries, and job roles, and populate the relevant application fields. This not only saves time but also reduces entry errors that could otherwise delay the approval process. A Fortune 500 insurer piloted this feature in its online application portal, resulting in a 37% reduction in form abandonment rates, an important metric in B2B customer acquisition. Risk Scoring and Decision Support Beyond streamlining inputs, NLP in insurance contributes to smarter decision-making by enabling early risk detection. By analyzing the language used in client-submitted documents or phone transcripts, NLP models can flag potential risk indicators, such as prior lawsuits, hazardous operations, or non-standard coverage requests. This is particularly valuable for B2B insurance providers that handle complex cases such as cyber liability, product liability, or specialized professional indemnity policies. With AI in insurance application processing, underwriters are not just faster, they are better informed. One global insurer that deployed NLP in its professional indemnity product line reported a 12% drop in loss ratios within 18 months of implementation, citing better initial risk scoring as the key driver. Real-World Example: NLP in Small Business Insurance Consider a small IT services firm applying for business interruption coverage. The application includes a PDF with operational workflows, past claims data, and an open-ended explanation of risk mitigation strategies. Without automation, an underwriter might take hours to read and interpret this information. But with NLP in insurance, the system can automatically extract: The number of employees and locations Previous business interruptions and resolution time Key clients and contract terms Cybersecurity protocols are in place. The extracted insights are then used to calculate risk scores, suggest coverage limits, and even highlight missing information that needs to be clarified, cutting the approval timeline from days to hours. Compliance and Data Consistency Another major advantage of NLP in insurance is improved compliance. Many B2B insurance applications need to follow regulatory guidelines that vary by state or policy type. NLP systems can cross-check application inputs against regulatory requirements, flag missing clauses, or ensure that standard exclusions are included. This minimizes legal risk and improves audit readiness, both critical in the highly regulated insurance domain. Integration with Legacy Systems A common concern among B2B insurers is how well these technologies integrate with existing systems. Many carriers still rely on legacy policy administration platforms, making the adoption of new tools complex. Leading solution providers are now offering NLP platforms with APIs that plug into existing CRMs, document repositories, and application portals. This enables insurers to implement AI in insurance application flows without overhauling their entire tech stack. An East Coast insurer that initially ran on a 20-year-old mainframe system successfully integrated a lightweight NLP engine to process agent-submitted PDFs. The pilot project paid for itself in less than 6 months through saved man-hours. What to Consider Before Adopting NLP While the benefits are tangible, insurance firms must approach NLP adoption thoughtfully. Here are a few key steps: Data Quality: NLP models rely on clean, labelled data. Firms need to invest in document digitization and categorization. Model Training: Pre-trained models can help, but insurers should tailor models using their data to improve relevance. Ethical Use: Transparent decision logic is essential, especially when NLP is used for risk scoring or approval recommendations. Having data science teams work closely with underwriting teams is critical to ensuring that technology aligns with business objectives. Final Thoughts As the insurance sector continues to modernize, NLP in insurance is becoming more than just a tool; it’s a competitive differentiator. Whether it’s reducing manual review, accelerating turnaround times, or improving compliance, NLP is helping B2B insurers deliver better experiences to their clients. For companies that handle large volumes of business applications, claims, or communications, adopting AI in insurance application processes isn’t just about efficiency. It’s about building a smarter, more agile operation that’s ready for scale. By taking a focused, practical approach to NLP adoption, insurance firms can unlock measurable value, both for their teams and their customers. At Mu Sigma We believe the purpose of AI, machine learning, and computer vision is to improve decision making and intelligent automation. How Insurance Companies are Using NLP to Streamline Application Approvals Applying for insurance can be a tedious process. Between long forms, complex documentation, and the need for quick decisions, insurers face the challenge of balancing speed with accuracy. For insurance companies operating in a competitive B2B environment, the pressure is even higher. Delays in approvals can mean lost clients and declining satisfaction scores. To address this, leading firms are now adopting AI in insurance application workflows, with NLP in insurance emerging as a central enabler of this shift. Natural Language Processing (NLP) is a branch of artificial intelligence that is highly effective in automating key parts of the insurance application lifecycle. By understanding and extracting meaning from human language in documents, emails, and chats, NLP is reducing manual workloads and bringing much-needed clarity and speed to application approvals. The Traditional Approval Bottleneck Traditionally, the insurance application process has required human reviewers to go through lengthy paperwork. For instance, when a small business applies for commercial insurance, underwriters often sift through tax forms, financial statements, contracts, and prior insurance history. This not only slows down turnaround times but also increases the chances of human error, especially when information is buried in unstructured formats. In addition, every application may have unique nuances that require back-and-forth communication between the insurer and applicant. Handling these interactions manually further delays the process. Automating Document Review with NLP One of the most valuable uses of NLP in insurance is automating the review of unstructured text data. NLP algorithms can scan through documents submitted by applicants, such as business licenses, income statements, or even handwritten notes, and extract relevant details like coverage amount requested, past claims, or business categories. For example, a mid-sized insurer in the Midwest used an NLP model trained on historical underwriting documents to automatically extract business classification codes (NAICS codes) from client descriptions. The model improved classification accuracy by 23% and reduced processing time per application from 45 minutes to under 10 minutes. By doing the “reading” automatically, NLP frees up underwriters to focus on high-risk or complex cases, improving both efficiency and decision quality. Speeding Up Communication and Follow-Ups Another area where AI in insurance application workflows has brought measurable improvements is client communication. Insurers receive large volumes of emails and messages daily, often containing key details that can impact policy decisions. With NLP models integrated into their CRM and email systems, insurance providers can now triage incoming communication automatically. For instance, if an applicant emails additional documents or asks about policy exclusions, the system can categorize the message and extract key updates in real time. A California-based property insurance company implemented NLP-driven email parsing to process 2,000+ customer emails daily. The result: 92% of messages were routed to the correct team automatically, and average response time dropped from 24 hours to under 6. Pre-Filling Forms Using NLP Manual data entry remains one of the most time-consuming tasks for both clients and insurance agents. NLP in insurance is reducing this burden by reading submitted documents and pre-filling application forms. For instance, when a small manufacturing company applies for a workers’ compensation policy, it may submit an employee roster, payroll data, and workplace safety records. NLP can extract names, salaries, and job roles, and populate the relevant application fields. This not only saves time but also reduces entry errors that could otherwise delay the approval process. A Fortune 500 insurer piloted this feature in its online application portal, resulting in a 37% reduction in form abandonment rates, an important metric in B2B customer acquisition. Risk Scoring and Decision Support Beyond streamlining inputs, NLP in insurance contributes to smarter decision-making by enabling early risk detection. By analyzing the language used in client-submitted documents or phone transcripts, NLP models can flag potential risk indicators, such as prior lawsuits, hazardous operations, or non-standard coverage requests. This is particularly valuable for B2B insurance providers that handle complex cases such as cyber liability, product liability, or specialized professional indemnity policies. With AI in insurance application processing, underwriters are not just faster, they are better informed. One global insurer that deployed NLP in its professional indemnity product line reported a 12% drop in loss ratios within 18 months of implementation, citing better initial risk scoring as the key driver. Real-World Example: NLP in Small Business Insurance Consider a small IT services firm applying for business interruption coverage. The application includes a PDF with operational workflows, past claims data, and an open-ended explanation of risk mitigation strategies. Without automation, an underwriter might take hours to read and interpret this information. But with NLP in insurance, the system can automatically extract: The number of employees and locations Previous business interruptions and resolution time Key clients and contract terms Cybersecurity protocols are in place. The extracted insights are then used to calculate risk scores, suggest coverage limits, and even highlight missing information that needs to be clarified, cutting the approval timeline from days to hours. Compliance and Data Consistency Another major advantage of NLP in insurance is improved compliance. Many B2B insurance applications need to follow regulatory guidelines that vary by state or policy type. NLP systems can cross-check application inputs against regulatory requirements, flag missing clauses, or ensure that standard exclusions are included. This minimizes legal risk and improves audit readiness, both critical in the highly regulated insurance domain. Integration with Legacy Systems A common concern among B2B insurers is how well these technologies integrate with existing systems. Many carriers still rely on legacy policy administration platforms, making the adoption of new tools complex. Leading solution providers are now offering NLP platforms with APIs that plug into existing CRMs, document repositories, and application portals. This enables insurers to implement AI in insurance application flows without overhauling their entire tech stack. An East Coast insurer that initially ran on a 20-year-old mainframe system successfully integrated a lightweight NLP engine to process agent-submitted PDFs. The pilot project paid for itself in less than 6 months through saved man-hours. What to Consider Before Adopting NLP While the benefits are tangible, insurance firms must approach NLP adoption thoughtfully. Here are a few key steps: Data Quality: NLP models rely on clean, labelled data. Firms need to invest in document digitization and categorization. Model Training: Pre-trained models can help, but insurers should tailor models using their data to improve relevance. Ethical Use: Transparent decision logic is essential, especially when NLP is used for risk scoring or approval recommendations. Having data science teams work closely with underwriting teams is critical to ensuring that technology aligns with business objectives. Final Thoughts As the insurance sector continues to modernize, NLP in insurance is becoming more than just a tool; it’s a competitive differentiator. Whether it’s reducing manual review, accelerating turnaround times, or improving compliance, NLP is helping B2B insurers deliver better experiences to their clients. For companies that handle large volumes of business applications, claims, or communications, adopting AI in insurance application processes isn’t just about efficiency. It’s about building a smarter, more agile operation that’s ready for scale. By taking a focused, practical approach to NLP adoption, insurance firms can unlock measurable value, both for their teams and their customers. At Mu Sigma We believe the purpose of AI, machine learning, and computer vision is to improve decision making and intelligent automation. How Insurance Companies are Using NLP to Streamline Application Approvals Applying for insurance can be a tedious process. Between long forms, complex documentation, and the need for quick decisions, insurers face the challenge of balancing speed with accuracy. For insurance companies operating in a competitive B2B environment, the pressure is even higher. Delays in approvals can mean lost clients and declining satisfaction scores. To address this, leading firms are now adopting AI in insurance application workflows, with NLP in insurance emerging as a central enabler of this shift. Natural Language Processing (NLP) is a branch of artificial intelligence that is highly effective in automating key parts of the insurance application lifecycle. By understanding and extracting meaning from human language in documents, emails, and chats, NLP is reducing manual workloads and bringing much-needed clarity and speed to application approvals. The Traditional Approval Bottleneck Traditionally, the insurance application process has required human reviewers to go through lengthy paperwork. For instance, when a small business applies for commercial insurance, underwriters often sift through tax forms, financial statements, contracts, and prior insurance history. This not only slows down turnaround times but also increases the chances of human error, especially when information is buried in unstructured formats. In addition, every application may have unique nuances that require back-and-forth communication between the insurer and applicant. Handling these interactions manually further delays the process. Automating Document Review with NLP One of the most valuable uses of NLP in insurance is automating the review of unstructured text data. NLP algorithms can scan through documents submitted by applicants, such as business licenses, income statements, or even handwritten notes, and extract relevant details like coverage amount requested, past claims, or business categories. For example, a mid-sized insurer in the Midwest used an NLP model trained on historical underwriting documents to automatically extract business classification codes (NAICS codes) from client descriptions. The model improved classification accuracy by 23% and reduced processing time per application from 45 minutes to under 10 minutes. By doing the “reading” automatically, NLP frees up underwriters to focus on high-risk or complex cases, improving both efficiency and decision quality. Speeding Up Communication and Follow-Ups Another area where AI in insurance application workflows has brought measurable improvements is client communication. Insurers receive large volumes of emails and messages daily, often containing key details that can impact policy decisions. With NLP models integrated into their CRM and email systems, insurance providers can now triage incoming communication automatically. For instance, if an applicant emails additional documents or asks about policy exclusions, the system can categorize the message and extract key updates in real time. A California-based property insurance company implemented NLP-driven email parsing to process 2,000+ customer emails daily. The result: 92% of messages were routed to the correct team automatically, and average response time dropped from 24 hours to under 6. Pre-Filling Forms Using NLP Manual data entry remains one of the most time-consuming tasks for both clients and insurance agents. NLP in insurance is reducing this burden by reading submitted documents and pre-filling application forms. For instance, when a small manufacturing company applies for a workers’ compensation policy, it may submit an employee roster, payroll data, and workplace safety records. NLP can extract names, salaries, and job roles, and populate the relevant application fields. This not only saves time but also reduces entry errors that could otherwise delay the approval process. A Fortune 500 insurer piloted this feature in its online application portal, resulting in a 37% reduction in form abandonment rates, an important metric in B2B customer acquisition. Risk Scoring and Decision Support Beyond streamlining inputs, NLP in insurance contributes to smarter decision-making by enabling early risk detection. By analyzing the language used in client-submitted documents or phone transcripts, NLP models can flag potential risk indicators, such as prior lawsuits, hazardous operations, or non-standard coverage requests. This is particularly valuable for B2B insurance providers that handle complex cases such as cyber liability, product liability, or specialized professional indemnity policies. With AI in insurance application processing, underwriters are not just faster, they are better informed. One global insurer that deployed NLP in its professional indemnity product line reported a 12% drop in loss ratios within 18 months of implementation, citing better initial risk scoring as the key driver. Real-World Example: NLP in Small Business Insurance Consider a small IT services firm applying for business interruption coverage. The application includes a PDF with operational workflows, past claims data, and an open-ended explanation of risk mitigation strategies. Without automation, an underwriter might take hours to read and interpret this information. But with NLP in insurance, the system can automatically extract: The number of employees and locations Previous business interruptions and resolution time Key clients and contract terms Cybersecurity protocols are in place. The extracted insights are then used to calculate risk scores, suggest coverage limits, and even highlight missing information that needs to be clarified, cutting the approval timeline from days to hours. Compliance and Data Consistency Another major advantage of NLP in insurance is improved compliance. Many B2B insurance applications need to follow regulatory guidelines that vary by state or policy type. NLP systems can cross-check application inputs against regulatory requirements, flag missing clauses, or ensure that standard exclusions are included. This minimizes legal risk and improves audit readiness, both critical in the highly regulated insurance domain. Integration with Legacy Systems A common concern among B2B insurers is how well these technologies integrate with existing systems. Many carriers still rely on legacy policy administration platforms, making the adoption of new tools complex. Leading solution providers are now offering NLP platforms with APIs that plug into existing CRMs, document repositories, and application portals. This enables insurers to implement AI in insurance application flows without overhauling their entire tech stack. An East Coast insurer that initially ran on a 20-year-old mainframe system successfully integrated a lightweight NLP engine to process agent-submitted PDFs. The pilot project paid for itself in less than 6 months through saved man-hours. What to Consider Before Adopting NLP While the benefits are tangible, insurance firms must approach NLP adoption thoughtfully. Here are a few key steps: Data Quality: NLP models rely on clean, labelled data. Firms need to invest in document digitization and categorization. Model Training: Pre-trained models can help, but insurers should tailor models using their data to improve relevance. Ethical Use: Transparent decision logic is essential, especially when NLP is used for risk scoring or approval recommendations. Having data science teams work closely with underwriting teams is critical to ensuring that technology aligns with business objectives. Final Thoughts As the insurance sector continues to modernize, NLP in insurance is becoming more than just a tool; it’s a competitive differentiator. Whether it’s reducing manual review, accelerating turnaround times, or improving compliance, NLP is helping B2B insurers deliver better experiences to their clients. For companies that handle large volumes of business applications, claims, or communications, adopting AI in insurance application processes isn’t just about efficiency. It’s about building a smarter, more agile operation that’s ready for scale. By taking a focused, practical approach to NLP adoption, insurance firms can unlock measurable value, both for their teams and their customers. At Mu Sigma We believe the purpose of AI, machine learning, and computer vision is to improve decision making and intelligent automation. Next Story:
How Insurance Companies are Using NLP to Streamline Application Approvals

About the Author /

Amit Gupta is an experienced digital marketer, expert writer, and founder of Tech Magazine. With 5+ years in the industry, he specializes in creating in-depth content on Technology Updates, IoT, Gaming, Gadget, Web Development, and Artificial Intelligence. Connect on Facebook and Linkedin.