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Forge and webAI cover different halves of the same problem. webAI brings the model to the data rather than moving data to the model—the architecture that makes true sovereignty possible. Forge brings the deployment discipline that puts it into production inside a complex enterprise. Neither half delivers the outcome alone, which is why the two are built to work together.
Conventional AI deployment inverts the security model most enterprises spent twenty years building. Data that is carefully governed at rest gets copied into a prompt and sent to infrastructure the organization does not own, govern, or audit.
webAI's platform is built the other way around. The model runs where the data already lives, under the access controls that already exist. Nothing sensitive has to move for the system to be useful, which means the sovereignty question is answered by the architecture rather than by a contract clause.
Public egress: 0 — prompts and context stay inside the boundary. webAI states that customer data never leaves the operator’s control and that it does not see or store customer inputs; Forge configures and validates the boundary that enforces it.
Each component runs inside your environment. Forge deploys, integrates, and operates them as one system.
Domain experts built from what only you know. Rather than one general-purpose model, each Persona focuses on a specific role, workflow, or domain and reflects the knowledge and judgment inside your teams—and the more it is used in real workflows, the more valuable it becomes.
The connective layer for intelligence across people, teams, and systems. Intelligence runs close to the data, systems, and people it supports; models and Personas connect across devices and environments; and what one team solves compounds across the others—without centralizing sensitive data.
One workspace for your team and the experts you build—shared spaces, projects, and Personas in one thread, on the network where your intelligence runs and improves.
The documentation you own, turned into an expert on an iPad. Cited answers, fully offline—so a technician gets the answer where the work happens, without waiting on a workstation or the cloud.
webAI Frontline turns large technical document collections into an on-device knowledge system: 25,000+ pages per collection and 50+ collections on a single iPad, fully offline, with under a second to the first citation. What used to require server infrastructure now runs on one device, built on webAI-ColGemma, a retrieval model designed for visually complex enterprise documents.
Models run on the silicon you own rather than assuming a datacenter is available. On Apple Silicon, webAI reports inference, training, and tracking all accelerated—which is what makes a frontier-class model practical on a device at the edge instead of only in a rack.
Figures published by webAI: the search-time figure from webAI's Frontline one-pager, measured in real-world testing with a major airline during heavy maintenance procedures; the performance-per-dollar and vision figures from webAI's business platform page, re-checked against the live source on 4 September 2026. Actual results vary with workload, model, and hardware; we validate expected performance against your specific environment during architecture assessment rather than asking you to take a benchmark on faith.
One accountable partner for the deployment. You are not left integrating a platform on your own.
What technical buyers ask us before an assessment.
webAI is a sovereign AI platform built to run inside the customer's own environment rather than as a hosted API. Its organizing idea is that the model comes to the data instead of the data going to the model, so the model executes where the data already lives, under the access controls that already exist. The platform comprises Personas (domain experts built from what only you know), the Intelligence Delivery Network (the connective layer for intelligence across people, teams, and systems), the webAI App (one workspace for your team and the experts you build), and Frontline (your documentation turned into an expert on an iPad, with cited answers, fully offline).
A platform license does not produce a working system. The integrator covers the distance between capable software and a production deployment: identity integration, network segmentation, hardware sizing, data access patterns, evaluation, and change control. Forge architects, hardens, and validates the full system, then owns it operationally after go-live and transfers knowledge to your team—so there is one partner accountable for the deployment rather than a platform you are left to integrate alone.
No. Forge AI Deployment is an independent systems integrator and an official webAI integrator. The two companies cover different halves of the same problem: webAI builds the sovereign AI platform and its local-first architecture, and Forge deploys, integrates, and operates it inside complex enterprise environments. Neither half produces a working system on its own.
Yes, and Forge treats restricted networks as a normal deployment target rather than an exception. Because the model runs inside your boundary, nothing sensitive has to move for the system to be useful. webAI's Frontline is built for exactly this case: cited answers from documentation you own, fully offline, so a technician gets an answer where the work happens without waiting on a workstation or the cloud.
webAI states that customer data never leaves the operator's control and that it does not see or store customer inputs. Architecturally, prompts and context stay inside your boundary because that is where inference happens—there is no step that ships them elsewhere. Forge's role is to configure and validate the boundary that enforces it, so the property is something you can demonstrate in your own environment rather than something you accept on assurance.
Models run on silicon you already own rather than assuming a datacenter is available. On Apple Silicon, webAI reports inference, training, and tracking all accelerated, which is what makes a frontier-class model practical on a device at the edge instead of only in a rack. Hardware sizing for your specific workload is part of the architecture assessment—Forge validates expected performance against your environment rather than quoting a generic specification.
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