Atlanta Beltline Kicks-Off a Digital Inclusion and Smart Cities Initiative Aimed at Addressing Urban Issues and Connecting Communities Through Technology
CIOREVIEW >> Application Management >> NEWS

Atlanta Beltline Kicks-Off a Digital Inclusion and Smart Cities Initiative Aimed at Addressing Urban Issues and Connecting Communities Through Technology

CIO Review

Leveraging BeltLine’s fiber infrastructure, project tackles digital divide

ATLANTA Atlanta BeltLine, Inc. is kicking off its digital inclusion strategy and announcing the partners for the organization’s first smart cities demonstration project. Smart cities projects use digital technologies in urban areas in a strategic and inclusive manner to improve quality of life for residents.

In partnership with Atlanta-based Fortune 500 and local disadvantaged business enterprise (DBE) companies, strategy development and demonstration pilot sites will bring together a wide spectrum of partners. Two interactive pop-up sites along the Atlanta BeltLine will provide Atlantans with tech-driven experiences to help bridge the digital divide by providing free public Wi-Fi, an autonomous grocery store, information on BeltLine activities, smart trash cans, maps and more in the coming months. It’s part of a pilot project with Fortune 500 and local businesses to address urban issues such as access to food, technology and wellness.  

According to the Federal Communications Commission, approximately 19 million Americans—6 percent of the population—still lack access to fixed broadband service at threshold speeds. That number rises as high as 25-33 percent for people living in certain areas along the BeltLine as sourced through the American Community Survey. 

“The Atlanta BeltLine uses public infrastructure as a vehicle for catalyzing economic growth and development. This digital inclusion and smart cities initiative will play a major role in defining our legacy and impact as an organization as we seek to leverage our telecommunication infrastructure to open up new economic opportunities to residents, students, business owners, seniors and the community at-large,” said Clyde Higgs, President and CEO at Atlanta BeltLine, Inc. “The BeltLine itself is a testing ground for innovation. It’s a place where we can use technology to solve problems facing our communities that could be scaled city-wide and as a case study for cities across the country.”

This project will be enabled by eX² Technology’s early investment in a robust fiber optic network installed along the Atlanta BeltLine. The project will engage community feedback and data to help identify the types of technologies that might be considered for implementation on the BeltLine corridor and serve the adjacent residents, businesses and schools. The outcome of the initiative will be to develop a long-term approach to solving urban issues, to enhance the BeltLine user experience, and to create long-term funding sources as well.

Leading the strategy effort is Honeywell, a global leader in the smart cities space, and N-Ovate Business Solutions, a metro Atlanta-based innovation firm focused on creating strategy around digital transformation, data modernization, and cybersecurity. They will seek to identify and collect data to better understand the digital divide in adjacent neighborhoods and how technology could help solve those issues. They will help develop recommendations for addressing the technology gaps that were exacerbated for local residents and business owners, especially on the south and west sides of the Atlanta BeltLine loop, during the pandemic.

The Rocket Community Fund, the philanthropic partner of Rocket Companies, is providing financial support for the development of the strategy and additional resources to support the initiative.

Pop-Up Sites Showcase Innovative Technology

Atlanta BeltLine and eX² Technology will transform two areas along the BeltLine into smart city pop-ups, showcasing pilot technologies. This demonstration project will garner additional community engagement and usage data to inform future BeltLine strategies. Sites on the Southside and Eastside Trails will feature high-speed internet powered by ABI’s fiber network. Moreover, each of the pilot areas will house a Nourish + Bloom Market, a Black-owned autonomous grocery store; a Rove IQ interactive wayfinding kiosk; a Big Belly smart waste bin; and, adjacent to the corridor, a Blink Electrical Vehicle (EV) charging station, among others. Georgia Green Energy Services, a local certified Minority Business Enterprise (MBE) and Small Business Enterprise (SBE) will deploy and manage the electrical needs at both sites. A steering committee of industry experts is helping to provide further insight and opportunities to inform the project. Microsoft is also sharing a fellow with the BeltLine to support this initiative through the Georgia Tech.

The BeltLine’s smart cities demonstration sites also will feature the first U.S. deployment of the Honeywell City Suite, an artificial intelligence-enabled IoT platform serving over 75 cities globally and improving the lives of over 100 million people worldwide. The sites’ technology will be integrated and driven by Honeywell’s City Suite Software, acting like the central hub of the project, which seamlessly aggregates information from multiple city systems including environment, emergency services, safety and security, and utilities, among other areas – in a single, unified view. Through the Honeywell platform, the BeltLine can collect information to make data-driven decisions through analytic technology to improve trail services, enhance the user experience, and monitor the flow of disposable and recyclable materials, supporting a more resilient BeltLine and sustainable environment.

eX² Technology designed and installed a 15.7-mile multi-duct, fiber optic network on the Atlanta BeltLine in 2021 to create a new funding source through the sale of fiber to support the long-term economic viability of the BeltLine and enable high-speed technology on the corridor. eX² Technology manages and maintains the communications system as well as serves as the BeltLine’s exclusive commercialization partner. “Our dark fiber commercialization efforts have supported the development of this Smart City Pilot as well as promoted economic development and digital inclusivity,” said Jay Jorgensen, Chief Operating Officer at eX² Technology. “Our company has a long history of partnering with communities and organizations like the BeltLine and bringing multiple partners together to develop innovative, technology-rich programs. We continue to seek additional partners who want to establish their services within the Atlanta market and have worked with the Atlanta BeltLine to reduce the price of the dark fiber to further promote new partnerships.”

For Honeywell, this initiative offers a chance to showcase its technology in an area where it has a major corporate presence.  “Our Atlanta office is just one mile from the Atlanta BeltLine, so we are not only professionally invested in the success of the BeltLine but also personally as many of our employees use the BeltLine every day,” said Matthew Britt, General Manager, Smart and Sustainable Cities, Honeywell. “We’re looking forward to growing our relationship with the eX2 team and are thrilled that our first project together is supporting the Atlanta BeltLine’s mission of improving social equity along the corridor and improving the community long term.”

As a leader in the digital equity space, the Rocket Community Fund is committed to ensuring their efforts are moving the needle across the country to bridge the digital divide. "At the Rocket Community Fund, we firmly believe that digital connectivity is foundational to success in every aspect of life," said Rob Lockett, Team Leader, National Housing Stability at the Rocket Community Fund. "We’re thrilled to partner once again with the Atlanta BeltLine team to transform the way residents and visitors at the BeltLine connect digitally with employment, education, healthcare, and community.”

The Rocket Community Fund’s commitment expands its partnership with the BeltLine. Previous collaborations focused on affordable housing providing residents with direct connections to key housing stability resources and funding for the Legacy Resident Retention Program.

The Atlanta BeltLine has more dark fiber available and is looking for additional service providers who can provide technology to end users. For more information, please visit https://ex2technology.com/atlantabeltlinefiber.

More in News

A software engineering and AI analytics purchase can fail long before model accuracy becomes a concern. The bigger challenge is often the handoff between existing infrastructure and new analytics, especially when camera networks and edge devices were never designed to share context. Replacing everything may simplify architecture on paper, but it can strain capital requirements and stretch deployment timelines. Buyers need to know whether a platform can work across existing technology boundaries without turning modernization into a wholesale infrastructure project.  Integration depth is therefore more revealing than the number of AI features on a product sheet. A useful platform should accept heterogeneous inputs and expose their data through a common control layer without forcing every piece of existing hardware to conform to one technical standard. It should also preserve the usefulness of legacy assets while making their information accessible to newer analytics. That matters in distributed environments where hardware replacement may be slow or economically unjustified. The real question is whether modernization can proceed around installed infrastructure rather than requiring a clean slate.  Real-time analytics creates an attention problem of its own. Video feeds and sensor events can overwhelm staff when every detection is treated as equally important. Buyers should examine how a platform separates routine activity from events that merit human review, and then look at how quickly those events reach the people responsible for validation. The difference between raw monitoring and useful analytics lies in this filtering step. A system that produces more alerts without improving prioritization merely transfers workload from observation to triage.  Architecture becomes more consequential as data volumes rise. Sending every high-definition stream to a centralized cloud environment can create avoidable bandwidth costs and response delays. Edge processing can reduce that burden when analytics are performed close to the source and only selected information moves upstream. Yet edge deployment introduces management demands. Devices and application services still need a coherent way to exchange information and support later investigation. Buyers should also examine how much infrastructure complexity is added when analytics move closer to the source.  “ CFBD ’s hybrid architecture connects legacy environments to newer computing infrastructure through hyperconvergence and virtualization.” Searchability deserves equal importance. Once video or sensor data has been transformed into structured metadata, teams should be able to move from live detection to later investigation without manually reviewing hours of footage. Useful systems retain descriptive attributes and behavioral context in forms that can be queried quickly. The buying question is whether that context survives across live monitoring and retrospective analysis rather than being trapped in separate workflows. Fast retrieval matters because delayed investigation can erase much of the advantage gained from real-time detection.  CFBD merits consideration for buyers facing this mix of integration pressure and analytics workload. Its AZOR Ecosystem, a real-time data orchestration platform, centralizes video and sensor information. AZOR Panel, a centralized monitoring interface, receives AI-filtered events for human validation. AZOR Analytics, a video analytics component, supports real-time and forensic analysis, while edge gateways can process video near the source and pass lighter event data upstream. Its hybrid architecture connects legacy environments to newer computing infrastructure through hyperconvergence and virtualization. The fit is strongest where replacement costs and operator overload are material constraints. For organizations that need AI analytics without discarding usable infrastructure, CFBD is a practical choice.  ...Read more
Customer outreach is moving faster than many compliance programs were designed to govern. A campaign assembled in hours can pass through several applications before a call, text, email or prerecorded message reaches a customer, while autonomous agents compress that cycle further. The exposure is no longer limited to whether a record appeared on a suppression list. Consent status, channel permissions, time-of-day rules and state or federal restrictions can change the answer at the moment of contact. A platform that checks too late leaves legal teams reconstructing decisions after the communication has already occurred. Static controls also create a quieter commercial problem. Large enterprises often carry separate customer records across business units, and a broad opt-out can be applied far beyond the product or channel the customer intended. Conservative suppression may reduce legal exposure, yet it can also remove legitimate audiences from campaigns and weaken the return on CRM or marketing technology investments. Effective governance needs enough context to distinguish a prohibited contact from an allowable one without forcing every business unit to maintain its own interpretation of the rules. The harder test is whether those distinctions survive as consent records move between systems and outreach programs change. “Gryphon’s deterministic rules-based decisioning evaluates contact permissions in real time, while automated evidence capture gives legal and compliance teams a defensible record of why each decision was made.” Speed matters at the decision point, not merely during campaign preparation. List scrubbing and periodic audits remain useful for certain tasks, but neither is designed to govern communications that originate across contact centers, individual employees, enterprise applications and autonomous agents. Decisioning should sit inside the existing workflow and evaluate the applicable permissions before outreach proceeds. The answer also needs to return quickly enough that compliance does not become a queue. Enterprise scale is equally important. A control layer that works only for one channel or one application recreates the same fragmentation it was purchased to remove. Policy changes also need to propagate without campaign teams waiting for separate rule updates in each downstream application, especially when restrictions take effect quickly. Defensibility separates governance from simple blocking. Executives should expect a clear record of the rule applied, the evidence used, the policy version and the reason a communication was allowed or stopped. Those records need to remain searchable as regulations and internal policies change. Deterministic decisioning has particular value where an organization must later explain exactly why a contact was permitted. The same discipline helps compliance teams identify oversuppression rather than treating every uncertain record as unusable. Buyers should also examine how readily the platform connects to existing CRM, contact-center, marketing automation and governance systems, since a long replacement project can undermine the speed advantage that real-time controls are meant to provide. Gryphon  is the premier choice for enterprises that need contact governance embedded directly into customer engagement rather than added as a later review. Its platform applies real-time controls across voice, SMS, email and interactions generated by AI agents while integrating with existing enterprise applications. Gryphon’s deterministic rules-based decisioning evaluates contact permissions in real time, while automated evidence capture gives legal and compliance teams a defensible record of why each decision was made. Compliance Hub extends that visibility into audit research and reporting. The platform also identifies contacts suppressed too broadly, helping organizations preserve legitimate reach without relaxing policy enforcement. For buyers balancing regulatory exposure against legitimate customer contact, point-of-contact enforcement paired with documented decision logic makes Gryphon a practical recommendation. ...Read more
Disconnected data work rarely begins at the pipeline itself. The delay often appears earlier, when a proposed data product moves from a business idea into requirements, architecture decisions, access controls and a development environment. Each handoff can introduce another tool or approval path, while product context becomes harder to preserve. By the time engineering begins, teams may already be reconciling mismatched project names, duplicated documentation, fragmented ownership and inconsistent setup across systems. Portfolio-level visibility also matters before engineering starts. A platform that preserves business cases alongside product definitions can help leadership compare proposed work, assign teams and select technology stacks without separating prioritization from the delivery path that eventually executes those decisions. That fragmentation becomes expensive when orchestration is purchased as another isolated layer. Data teams commonly work across cloud infrastructure, code repositories, ticketing systems and specialist data platforms, while product managers and architects need continuity across the same work. Replacing that estate is rarely the practical objective. A stronger platform coordinates existing environments while preserving product identity and approved technology choices throughout delivery. Integration depth matters less as a feature count than as a way to remove repeated setup and cross-tool reconciliation. “Calibo can establish access to selected technology stacks and generate CI/CD pathways for controlled movement between development and production environments.” Self-service also needs boundaries. Provisioning development environments, granting access, creating repositories and triggering infrastructure changes can remove substantial waiting time, but only when those actions follow established controls. The useful distinction is whether routine requests can execute from approved templates and policies rather than pass through manual service tickets. That changes the role of platform and architecture teams. Instead of completing repetitive setup on demand, they can establish guardrails that engineering teams use independently. Traceability becomes harder once a project leaves experimentation and enters controlled delivery. Changes to requirements can alter pipeline work, while release movement creates dependencies across development, test, staging and production. Executives need a clear line from the original business case to the technical work that follows, particularly when multiple data products compete for budget or shared engineering capacity. Visibility into status, resource use, dependencies and release progress helps management identify where work is waiting without rebuilding the picture from separate tools. It can also expose queueing between teams before delayed approvals become late-stage release problems. Release control should be treated as part of orchestration rather than an adjacent DevOps concern. Creating a pipeline is only part of the purchase decision. The harder question is whether code and data products can move through governed environments without custom coordination each time. Automated CI/CD setup, reusable templates, policy-based promotion and dependency visibility can make that movement repeatable. This becomes more important for AI-related data work, where experiments can appear quickly but production use depends on controlled access, governed data movement, documented lineage and consistent release practices. Calibo merits recommendation for enterprises that want data orchestration tied directly to the broader delivery lifecycle. Its Data Fabric Studio supports reusable data pipelines. The wider platform carries product context into the development toolchain while automating environment setup. Calibo can establish access to selected technology stacks and generate CI/CD pathways for controlled movement between development and production environments. Its Release Orchestration capability extends that model into deployment governance and dependency management. This gives data teams a self-service framework that reduces manual handoffs while keeping technical work connected to the product context and enterprise controls that initiated it. ...Read more
As software development becomes more reliant on AI, companies have started to focus more on the process of coding, changing, and attributing. Code attribution systems for AI are becoming common for helping engineering professionals know the source of code, differentiate human contributions from that done by AI, and remain visible in the development environment. Their importance goes beyond that of mere tracking since attribution can affect intellectual property management, security assessments, compliance procedures, and engineering performance. However, the implementation of these platforms poses some problems that organizations need to solve before they can effectively use attribution. How Can Organizations Maintain Accurate Code Attribution? An important issue here is the question of establishing accurate attribution in a complicated process of software development. Nowadays, software development includes many repositories, development environments, libraries, automated systems, and cooperation models. The suggestions generated by the artificial intelligence can be accepted, modified, mixed with the existing code, or completely rewritten by the developer. As a result, it becomes difficult to distinguish what part was done with the help of AI and what was developed independently by humans. Data quality also poses a challenge. Attributing authorship requires the availability of development history, history of code changes, prompts, suggestions, and modification patterns. Poor data quality can lead to erroneous findings, especially where organizations employ different methods for software development or use a different set of tools for development. It is imperative for businesses to come up with data standards for the consistency of findings. Issues related to privacy and intellectual property rights make the scenario even more complicated. The source code may include business logic, proprietary processes, and customer data. Companies that choose to implement the attribution platforms need to pay special attention to the way the development data will be gathered, analyzed, stored, and made available. What Makes AI Attribution Difficult Across Enterprise Development? The other challenge is that of incorporating the attribution process into the current engineering process. Companies typically have heterogeneous technology stacks and development processes. This implies that any attribution solution has to be compatible with the source code repositories, issue tracking platforms, code reviews, security mechanisms and so forth. If not, fragmentation and extra manual processes arise. Interpretation is just as crucial. Metrics related to attribution should not be automatically assumed to reflect developer productivity or the quality of code written. While AI can alter how engineers spend their time, more generated code does not imply a better result. Business context regarding maintainability, reliability, reviews, security, and business needs should also be factored in, along with attribution. Attributing AI code is going to be contingent upon transparency, interoperability, and good governance. Enterprises are going to require established processes for attributing contributions made by AI and also explaining how the information is to be utilized. Software systems capable of creating traceable evidence, easy integration, and clear reporting can enable enterprises to create more trust when it comes to AI-enabled development efforts. Taking a good approach towards these considerations will enable enterprises to get visibility regarding the use of AI in coding without having to risk their intellectual property rights and engineering accountability. ...Read more