Faber is being built for system integrators and robotics OEMs deploying physical AI today. Engineers and AI agents work side by side to design, validate, deploy, and operate automation systems — and the engineering work compounds across projects, not just within them. Starting with warehouses.
4,500/hr. Confirm peak shift?6% — below SLA. Recommend +2 chargers in zone C.4,820/hr. Bottleneck: zone B aisle 4.$4.18M. Vendor coverage 100%.84d. Chargers → robots → integration. 14 weeks.4,820/hr · buffer 14%. No anomalies.Faber moves with your engineers from the first requirement to the running fleet. Every decision, formula, and assumption lives within connected models — so the knowledge compounds across projects.
Faber is looking for design partners — system integrators and robotics OEMs engineering the platform alongside the teams shipping physical AI today. We want to understand your work and shape Faber around it.
The friction lives in the seams — between systems, between multi-vendor integration, between design intent and operational reality. Five themes recur across the research, in conversations with operators and integrators, and in the engineering work itself.
Across the systems that actually run a warehouse — WMS, WCS, WES, the robotics fleet, the WES-to-fleet connectors — most engineering teams stitch together vendor-specific tools, custom code, and tribal knowledge to make them work as one site. Faber starts upstream: a vendor-agnostic engineering layer that holds the design and the deliverables in one place. We're working toward extending that layer into orchestration — owning the logic that sits across the systems, not just the design that produced them.
Today the layout, the throughput estimate, the fleet size, the BOM, and the financial model live as separate artifacts in separate tools — maintained by separate people, reconciled by hand. By the time a customer asks "what changes if we drop the budget by 15%?", the model that won the deal is too far from the model that's being built. Faber connects them, so what-if scenarios and impacts are visible immediately instead of six months later.
A finished design isn't a deployable site. Survey packages, layout variants for site realities, commissioning deliverables — the work between "design approved" and "robots running" is where weeks disappear, today scattered across PDFs, emails, and one-off engineering. Faber is being built to produce these deliverables from the same model that holds the design, so the path from CAD to physical world is engineered, not improvised.
The simulation that closed the project becomes a slide deck the day commissioning starts. SKUs shift, volumes diverge from forecast, peak shifts find bottlenecks the design assumed away. Faber keeps the engineering model live, fed by what the site is actually doing — so the engineers responsible for the project can re-optimize continuously, with the same model they used to design it.
Each new customer means re-translating their requirements, re-configuring fleets for their building, re-integrating with their WMS. None of this engineering work compounds — the model from the last project doesn't make the next one faster. Faber is being built so it does. What's learned on one deployment becomes the starting point for the next.
As agents take on more of the engineering work, the human role shifts from doing to deciding. Faber is being built as a forward-deployed engineering platform — where engineers and agents collaborate on modeling, translation, and configuration, and engineers make the judgments that matter.
Faber starts with warehouse automation — the segment we know best, and where the latest wave of robotics is being deployed at scale. The roadmap extends outward into manufacturing, factories, and data centers, wherever physical AI gets deployed.
Full lifecycle — concept through operations. MFC, fulfillment, dark stores.
Cells, lines, inline QC, robotic work-cells.
Multi-process facilities, brownfield retrofits, greenfield builds.
Robotic provisioning, cooling systems, physical infrastructure.
If you operate physical infrastructure that doesn't fit the categories above — let us know. We want to understand where the engineering work of deploying physical AI is hardest.
Faber is being built for the engineers shipping physical AI today. We're partnering with a small number of system integrators, robotics OEMs, and operator-integrators to shape the platform around the work you actually do.
Teams designing and delivering automation projects. Faber is being built around the workflow we've seen at integrators — from first customer conversation through commissioning.
Teams selling robotic systems into integrators and operators. Faber is being built so the application engineering on every new customer compounds across deployments instead of starting over.
Internal automation teams running brownfield and greenfield projects in-house. Faber is being built so engineering rigor scales across projects without scaling team size.
Tell us about your work and what you're trying to ship. We'll be in touch.