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Gravitate Publishes Research Series on AI Search Visibility as Answer Engine Optimization Becomes a Board-Level Priority

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Four new guides from the Portland agency cover LLM SEO, AI readability, budget reallocation, and 33 daily methods for ranking in ChatGPT, Perplexity, and Google AI Overviews.

PORTLAND, Ore., Aug. 21, 2026 /PRNewswire/ — Gravitate, a 27-year-old Portland-based digital marketing and web design agency, today published a series of research-backed guides establishing a practice framework for Answer Engine Optimization (AEO): the discipline of earning citations in AI-generated search results across Google AI Overviews, ChatGPT Search, Perplexity, and Bing Copilot.

The guides arrive as AI-generated answers displace traditional organic traffic at a measurable rate. Ahrefs tracked a 58% drop in clicks for top-ranking pages when a Google AI Overview appears, up from 34.5% just eight months prior. Semrush found 83% of AI Overview searches end without a single click. For businesses that built revenue pipelines on organic search, the existing playbook no longer produces the same return.

“The brands getting left behind are not losing because their product is worse,” said Rory Piecuch, SEO and AI Search Specialist at Gravitate. “They are losing because AI engines are not finding their content citable. That is a structural problem, and it has a structural fix.”

Gravitate’s research covers four interconnected areas of AI search visibility

The agency’s AI Readability Optimization guide details nine steps for structuring content so large language models can process, extract, and cite it. The framework includes answer-first formatting, sentence-level clarity rules, and entity attribution practices that make content retrievable by AI systems. Gravitate’s own content ranked number one in Google AI Overviews within a day of applying the framework.

“AI Search SEO: How to Rank on ChatGPT, Perplexity, and Gemini,” authored by Matt Malone, Gravitate’s VP of Growth, covers LLM SEO from a structural standpoint: schema markup, entity-rich content, prompt optimization for brand visibility, and how to measure citation rate and share of model as primary KPIs. The guide also includes a free GEO Audit Checklist.

The budget guide introduces a 70/30 allocation model for search spend. Gravitate recommends keeping 70% to 85% on core SEO and ring-fencing 15% to 30% for AI search visibility work, broken into six fundable line items: content refreshes, digital PR, original research, entity and author authority, structured data, and citation tracking tools.

A fourth guide, covering 33 practical AI methods the team uses daily in client SEO work, rounds out the series with applied tactics across keyword research, content optimization, and competitive analysis.

The business case is now supported by conversion data, not just traffic figures. Forrester research found 94% of B2B buyers use AI during their buying process and that 69% of B2B marketers surveyed called AI visibility a top CEO priority in 2026. Semrush data shows AI-referred visitors convert at 4.4 times the rate of traditional organic traffic. Ahrefs puts that ceiling at 23 times on high-intent queries.

Gravitate has applied this methodology across client work spanning B2B technology, e-commerce, legal, and healthcare, producing 723% organic traffic growth on its own site using the same approach.

Businesses looking to benchmark their current standing can access Gravitate’s AI Readiness Audit, which scores pages across 36 AI visibility factors before any budget is allocated.

Read the research series:

AI Search SEO: How to Rank on ChatGPT, Perplexity, and Gemini: gravitatedesign.com/blog/ai-search-seo/AI Readability Optimization: The Key to AI Search Traffic: gravitatedesign.com/blog/ai-readability-optimization/How to Adapt Your SEO Budget for AI Search: gravitatedesign.com/blog/how-to-adapt-seo-budget-for-ai-search/How to Use AI for SEO: 33 Easy Methods I Use Daily: gravitatedesign.com/blog/ai-for-seo/Start with an AI Readiness Audit:
https://www.gravitatedesign.com/service/ai-seo/

Frequently Asked Questions

What is Answer Engine Optimization (AEO)?

AEO is the practice of structuring content so AI-powered search tools, including Google AI Overviews, ChatGPT Search, Perplexity, and Bing Copilot, can retrieve, summarize, and cite it in their responses. Where traditional SEO targets a ranked link on a results page, AEO targets the answer itself. The goal is to be the source AI cites, not a result a user has to scroll past.

How is AEO different from traditional SEO?

Traditional SEO earns rankings. AEO earns citations. The underlying signals overlap: technical health, authoritative content, and topical depth matter in both. The difference is in execution. AEO requires answer-first formatting, entity attribution, structured data, and digital PR that builds the verifiable footprint AI systems look for when choosing sources. Brands that treat AEO as a separate strategy from SEO are making a false distinction. It is the same foundation optimized for where search is going.

How do I know if my content is showing up in AI search results?

Most businesses do not know, and that is the problem. Citation rate and share of model are the two primary metrics to track: how often your brand appears in AI answers, and how often it appears relative to competitors across the same prompts. Tools like Otterly AI, Peec AI, and Semrush’s AI Toolkit track citations across major AI platforms. Gravitate’s AI Readiness Audit scores pages across 36 visibility factors to establish a baseline before any tracking or budget decisions are made.

How does Gravitate help businesses with AI search visibility?

Gravitate’s AI SEO service covers the full stack: content audits and answer-first rewrites, technical optimization, schema implementation, entity and author authority building, digital PR, and citation tracking. Engagements typically begin with an AI Readiness Audit, which scores existing pages and surfaces the highest-impact opportunities before any execution begins. Most clients see first citation gains within 60 to 90 days of optimization work.

About Gravitate

Gravitate is a full-service digital marketing and web design agency based in Portland, Oregon. Founded in 1999, the agency has launched more than 500 websites and built growth marketing programs across SEO, paid media, CRO, and analytics for clients in B2B technology, e-commerce, legal, healthcare, and consumer brands. Gravitate does not report on impressions, followers, or clicks unless they are tied to revenue. More at gravitatedesign.com.

Media Contact
Matt Malone
VP of Growth, Gravitate
matt.malone@gravitatedesign.com 
https://www.gravitatedesign.com/service/ai-seo/

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Beghou Expands Pune Office, Strengthening Data, Analytics, and AI Capabilities

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The expanded office brings together multidisciplinary teams to solve increasingly complex life sciences commercialization challenges 

PUNE, India, Nov. 11, 2026 /PRNewswire/ — Beghou, a life sciences commercialization consulting and technology company, today announced the expansion of its Pune office. Opened on October 7 to foster cross-team collaboration, the new space reflects Beghou’s growing global delivery and innovation model. The Pune office is home to consulting and product teams with expertise spanning analytics, data engineering, technology, and AI. Beghou has also expanded its offices in Hyderabad and Bengaluru this year as its workforce continues to scale. The larger Pune space makes room for that growth, along with expanded leadership opportunities, specialist capabilities, and global client exposure for teams based there. 

“There’s a real sense of momentum at Beghou right now, and it’s exciting to see that reflected in our global expansion. We’re growing across markets, expanding the capabilities we bring to clients, and investing in the people and technology behind that work,” said Adrienne Lovink, CEO, Beghou. 

Commercialization challenges rarely sit within a single discipline, and Beghou’s model is built around that reality. Teams work together to move from identifying a business question to designing, building, and implementing the solution. The new office gives them dedicated space to collaborate, share knowledge, and learn from one another. 

“It is incredibly fulfilling to see the growth of our India organization and the world-class talent and capabilities we have built over the years. I see this growth as a stepping stone to something much bigger – not just scaling, but driving innovation, developing differentiated offerings, and solving increasingly complex commercialization challenges for our clients. Our new office reflects this ambition, bringing together diverse expertise and creating more opportunities for our teams to collaborate, innovate, and build meaningful solutions,” said Sonam Dubey, India Head, Beghou. 

The office is also a key hub for Beghou’s AI and product innovation, with teams working to embed AI in the solutions clients use across planning, analysis, and execution. They are also helping to develop new products that pair Beghou’s commercialization expertise with modern data capabilities. 

“We are at an exciting point in Beghou’s journey. I see this office as a melting pot of deep commercial expertise, technology, product thinking, and AI, the capabilities that will shape our next phase of growth. The opportunity is to combine those strengths to build differentiated solutions that solve real client problems and scale our impact,” said Abhigya Jain, Partner, Beghou. 

About Beghou 

Beghou drives commercialization that works for life sciences. For over 30 years, Beghou has been a long-term consulting and technology partner to more than 250 life sciences clients. We connect the dots between strategy, data, technology, and teams, helping clients accelerate launches, deepen customer engagement, and advance patient access. Whether a company is pre-commercial and building from scratch or refining an existing commercial strategy, Beghou provides fresh ideas and pragmatic, technology-enabled solutions to help organizations adapt and thrive in an increasingly complex environment. 

We build enduring partnerships by remaining engaged and accountable beyond project delivery to help ensure solutions take hold and deliver results. Combining strategic thinking, technical expertise, and hands-on execution, we help clients achieve faster outcomes and lasting impact. 

Find out more at www.beghou.com.

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TIER IV unveils end-to-end autonomous driving adaptable to map information and sensor configurations

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Multiple open-source AI models are available with Autoware

TOKYO, Nov. 11, 2026 /PRNewswire/ — TIER IV, the pioneering force behind Autoware, open-source software for autonomous driving, today announced the development and open-source release of multiple end-to-end (E2E) autonomous driving AI models that support different system requirements, including map information and sensor configurations. The models offer flexible options ranging from camera-only configurations to those incorporating LiDAR, radar and high-definition (HD) maps, enabling developers to select AI models suited to their performance requirements. Through Autoware, TIER IV provides a development environment for optimizing these models and integrating them into vehicle systems, helping automakers and technology partners develop and deploy their own autonomous driving stack.

Overview of the Autoware 2.0 Architecture

TIER IV is leading the development of Autoware 2.0, the next-generation architecture of Autoware. Autoware 2.0 is a scalable and adaptable architecture that flexibly combines AI models with conventional rule-based technologies. By supporting different AI models and sensor configurations, it enables autonomous driving systems to be built for a range of applications and system requirements. The E2E autonomous driving AI models announced today are concrete technological outcomes designed to leverage this architecture.

NVIDIA Alpamayo 2 Edge

As part of its collaboration with NVIDIA announced in March 2026 , TIER IV has been evaluating the performance of NVIDIA Alpamayo, a vision-language-action (VLA) foundation model, and integrating it with Autoware. The latest model, Alpamayo 2 Edge, is an E2E autonomous driving AI model that takes only camera images as input and uses reasoning to generate driving trajectories. With 2 billion parameters, compared with 34 billion in Alpamayo 2 Super, it is substantially smaller and offers promise for practical deployment in vehicles. TIER IV is currently optimizing the model and adapting it to real vehicles, while having completed its integration with Autoware.

GitHub (source code): https://github.com/autowarefoundation/alpamayo-autowareHugging Face (AI model): https://huggingface.co/nvidia/Alpamayo2-Edge

AutoE2E

AutoE2E, whose development is led by the Autoware Foundation (AWF) and TIER IV, is an E2E autonomous driving AI model capable of running on computing platforms beyond NVIDIA hardware. It adopts a Fast–Slow architecture combining a Reactive Model for real-time driving decisions, a World Model for predicting future changes in the surrounding environment, and a Reasoning Model that supports driving decisions. It can accommodate configurations ranging from cameras alone to those incorporating LiDAR and radar sensor data. With 79.9 million parameters, it is lighter than Alpamayo 2 Edge. The Reactive Model has been released, while development of the World Model and Reasoning Model continues, alongside consideration of commercialization for robotaxi applications. Integration of the Reactive Model with Autoware has been achieved.

GitHub (source code): https://github.com/autowarefoundation/auto_e2eHugging Face (AI model): https://huggingface.co/AutowareFoundation/auto_e2e

METEOR

Developed independently by TIER IV, METEOR is a lightweight, multitask E2E autonomous driving AI model primarily intended for privately owned passenger vehicles. Built on a convolutional neural network (CNN) architecture, it uses a bird’s-eye-view (BEV) representation to perform everything from perception of the surrounding environment to driving trajectory generation within a single neural network, without relying on high-definition maps. It supports configurations using images from one to eight cameras and can also incorporate LiDAR and radar sensor data. With 16 million parameters, it is even lighter than AutoE2E and supports operation across different semiconductor platforms, including NVIDIA and Renesas. TIER IV also uses CoMET, the automated labeling platform within Co-MLOps, together with agentic AI to automate data labeling and model training, evaluation, and improvement, eliminating the need for manual coding and labeling. Automakers and component suppliers can optimize the open-source model to meet their own vehicle specifications and performance requirements.

GitHub (source code): https://github.com/tier4/METEORHugging Face (AI model): https://huggingface.co/AutowareFoundation/meteor

OnePlanner

OnePlanner is an E2E autonomous driving AI model that integrates perception of the surrounding environment with motion planning. It currently uses BEVFusion, which generates a BEV representation from LiDAR point-cloud data, with a planned transition to PTv3. Its flexible architecture allows the perception model to be replaced while directly using features extracted during perception for motion planning. Conventional autonomous driving systems convert perception results into information such as object positions and velocities before passing them to the planner. OnePlanner reduces these intermediate conversions, enabling richer use of information about the surrounding environment. It also incorporates HD maps, route information, traffic-signal states, and vehicle motion states to generate driving trajectories for the next six seconds. Integration with Autoware has been achieved.

GitHub (source code): https://github.com/autowarefoundation/autoware_oneplannerHugging Face (AI model): https://huggingface.co/AutowareFoundation/oneplanner

LatentDrive

LatentDrive is an E2E autonomous driving AI model that uses a world model to integrate predictions of the surrounding environment with motion planning. Using V-JEPA, a self-supervised learning approach, it represents features of the surrounding environment extracted from camera video in a latent space and uses these features to generate vehicle trajectories. Its distinguishing feature is that it directly leverages information from video for motion planning without intermediate processes such as object detection and tracking. It does not require HD maps and generates trajectories for the next four seconds using camera video, route information, and vehicle motion states. It supports configurations with either one or three cameras, and work is underway on integration with Autoware and validation in an in-vehicle environment.

GitHub (source code): https://github.com/autowarefoundation/autoware_latent_driveHugging Face (AI model): https://huggingface.co/AutowareFoundation/latent_drive

“Advances in E2E autonomous driving are fundamentally changing how autonomous driving systems are developed.” said Shinpei Kato, founder and CEO of TIER IV. “For automakers and component suppliers to incorporate AI technology into their own products, however, it is essential that they be able to internalize technologies according to their applications and performance requirements, without depending on a particular AI model or semiconductor platform. By releasing multiple AI models developed under the leadership of TIER IV and the Autoware Foundation, alongside the results of our collaboration with NVIDIA, we have significantly expanded those choices. We will continue to advance the open-source ecosystem centered on Autoware and work with partners around the world to democratize autonomous driving.”

“Advancing autonomous driving requires AI that can reason through complex situations and run efficiently in the car.” said Ali Kani, vice president of automotive at NVIDIA. “TIER IV’s integration of NVIDIA Alpamayo 2 Edge with Autoware gives developers an open foundation to bring that intelligence from research into their own vehicles.”

“The advancement of autonomous driving technology depends on an ecosystem in which diverse AI models can be developed and evaluated openly, then applied according to their respective use cases.” said Muhammad Zain Khawaja, President of the Autoware Foundation. “The AI models released today are an important demonstration of how Autoware is evolving into a flexible development platform that is not tied to any particular technology or hardware. We welcome TIER IV’s continued technology development and its collaboration with partners including NVIDIA. The Autoware Foundation will continue working with the global open-source community to make advanced autonomous driving technology accessible to everyone.”

Next Steps

TIER IV will continue improving the performance of the AI models released today and optimizing them for in-vehicle environments, while promoting the development and commercialization of E2E autonomous driving technology by automakers and partners through Autoware. TIER IV will also strengthen collaboration with the global open-source community and work to expand the adoption of autonomous driving systems across diverse vehicle types and applications.

About TIER IV

TIER IV stands at the forefront of deep tech innovation, pioneering Autoware, open-source software for autonomous driving. With a comprehensive suite of platforms and services built around Autoware, TIER IV provides everything from software development and vehicle procurement to operational support. Through the Autoware ecosystem, TIER IV works with partners worldwide to shape the future of intelligent vehicles with open-source software, aiming to create mobility that is safer, more sustainable, and accessible to all.

The word marks and logos of TIER IV are trademarks or registered trademarks of TIER IV, Inc. in Japan and other countries. All other company names, product names, service names, and logos referenced herein are trademarks or registered trademarks of their respective owners.

Autoware is a registered trademark of the Autoware Foundation.

Media contact
pr@tier4.jp 

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SOURCE TIER IV, Inc.

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Dingdong to Report Third Quarter 2026 Financial Results on November 12, 2026

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SHANGHAI, Nov. 10, 2026 /PRNewswire/ — Dingdong (Cayman) Limited (“Dingdong” or the “Company”) (NYSE: DDL), a leading fresh grocery e-commerce company in China, with advanced supply chain capabilities, today announced that it will report its unaudited financial results for the third quarter ended September 30, 2026, before U.S. markets open on November 12, 2026.

About Dingdong (Cayman) Limited

Dingdong is the leading fresh grocery e-commerce company in mainland China, with sustainable long-term growth. The Company directly provides users and households with fresh groceries, prepared food, and other food products through delivering a convenient and excellent shopping experience supported by an extensive self-operated frontline fulfillment grid. Leveraging its deep insights into consumers’ evolving needs and its strong food innovation capabilities, Dingdong has successfully launched a series of private label products spanning a variety of food categories. Many of Dingdong’s private label products are produced at its own production plants, allowing it to more efficiently produce and offer safe and high-quality food products. Dingdong aims to be the first choice for fresh and food shopping.

For more information, please visit: https://ir.100.me.

 

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SOURCE Dingdong (Cayman) Limited

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