Forward, formerly Forward Networks, launched Forward Predict, a new capability designed to show the impact of network changes before engineers deploy them in production. The Santa Clara-based company said the feature runs proposed changes against its mathematically accurate network digital twin, giving operations teams deterministic evidence of how a change will affect connectivity, security, and compliance.
The launch targets a long-standing operational problem: most enterprise networks still rely on production environments as the ultimate test bed for change. Even with labs, change advisory boards, and method-of-procedure reviews, teams often discover routing, policy, segmentation, or access-control issues only after deployment. Forward Predict aims to move that validation step earlier by testing proposed changes against a production-equivalent model spanning multi-vendor networks and major cloud environments.
Forward said the capability also lays groundwork for autonomous networking by giving AI agents a way to propose, test, receive failure feedback, and iterate on network changes before execution. Forward Predict will be available in fall 2026, and the company plans demonstrations at Cisco Live Las Vegas.
• Forward Predict validates proposed network changes before deployment.
• The capability runs changes against Forward’s Network Digital Twin.
• The model spans multi-vendor environments and major clouds, including AWS, Azure, Google Cloud, and IBM.
• The platform checks for connectivity, security, and compliance impacts.
• Forward said beta users already use the capability to accelerate design-to-deployment workflows and reduce change risk.
• Forward Networks also rebranded as Forward, reflecting a broader focus on autonomous networking.
“When we founded Forward more than a decade ago, we set our sights on the future of autonomous networking,” said David Erickson, CEO and co-founder of Forward. “Everything we have built since, including the world’s first network digital twin, was working backwards from that goal. An incredible team, advances in compute, and 12 years of deep collaboration with the world’s largest and most complex networks have made this moment possible. Forward Predict is the next critical step on that journey, and we built it for every organization that cares about their network.”
| Company | Forward, formerly Forward Networks |
| Headquarters | Santa Clara, California |
| Founded | 2013 |
| Founders | David Erickson, Peyman Kazemian, Nikhil Handigol, and Brandon Heller |
| Leadership | David Erickson, CEO and co-founder; Brandon Heller, CTO and co-founder; Nikhil Handigol, Chief AI Officer and co-founder |
| Core Technology | Network Digital Twin platform that creates a mathematically accurate model of production network behavior |
| Platform Scope | Multi-vendor enterprise networks across on-premises infrastructure and major clouds including AWS, Azure, Google Cloud, and IBM Cloud |
| Key Capabilities | Network search, path analysis, intent verification, security exposure analysis, compliance checks, historical snapshots, and change validation |
| New Product | Forward Predict, a capability for testing proposed network changes against the digital twin before production deployment |
| Primary Use Cases | Troubleshooting, change management, audit and compliance, segmentation verification, cloud migration, and autonomous networking readiness |
| Customers Cited | Goldman Sachs, PayPal, S&P Global, IBM, Dell, and other large enterprises and government agencies |
| Investors | A. Capital, Andreessen Horowitz, Goldman Sachs, MSD Partners, Omega Venture Partners, Section 32, and Threshold Ventures |
| Recent Milestone | Rebranded from Forward Networks to Forward as the company expands its focus on autonomous networking |
🌐 Analysis: Forward Predict fits a broader shift in enterprise networking from observability toward pre-change assurance. As AI-based network operations tools move from recommendation engines toward closed-loop automation, deterministic validation becomes a prerequisite because autonomous agents need a trusted model of intended and unintended consequences before they touch production. Forward’s positioning also puts it in the same operational conversation as intent-based networking, digital twins, AIOps, and network-as-code workflows, where the main challenge remains proving that policy, topology, and application reachability will behave as expected after change.
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