Most distribution centers have more data than they know what to do with. Warehouse management system (WMS) alerts firing, congestion warnings piling up, idle time flags appearing on dashboards. What they often lackis the answer to the most important question: why is this happening?
Brooks Thompson, Director of Emerging Technology and Partnerships at Telaid, sat down
with Bryan Kim, VP of Business Development and Strategic Partnerships at Superb AI, to
discuss how computer vision is changing the way distribution center operators understand
and act on what is happening inside their facilities.
The Problem No Planning System Can Solve Alone
Distribution centers have invested heavily in sophisticated planning systems. WMS, MES,
conveyor orchestration layers: these tools tell operators what machine signals are saying.
What they cannot tell you is what is actually happening in the physical space.
Kim describes the pattern his team encounters repeatedly. Operators receive congestion
alerts, idle time flags, and stall event notifications. But tracing those alerts back to a root
cause means hours of manually reviewing footage, cross-referencing machine data, and
burning through man-hours that could be spent elsewhere.
The result is a meaningful gap between what operators think is happening and what is
actually occurring on the floor. Are forklifts following efficient paths? Are workers idle because
of process design or because product is not arriving at their stations? Are the picking lines
running below capacity because of a spatial bottleneck that nobody has identified yet?
That tacit knowledge, as Kim calls it, exists somewhere inside the facility. The challenge is
that without computer vision, there is no systematic way to surface it.
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What Computer Vision Actually Delivers
Superb AI's platform approaches this from two directions depending on the client. For
organizations with internal AI and data science teams, it functions as an MLOps layer that
dramatically accelerates model development, compressing the path from concept to
production-grade vision model from six to nine months down to four to eight weeks. That
speed translates directly into faster ROI decisions and more ideas tested per year.
For the larger group of operators who simply want a working solution without building an
internal AI capability, Superb AI delivers the model itself, tuned for the specific use case and
deployed on-site.
In distribution centers specifically, the measurable impact shows up in a few key areas. Defect
detection rates improve from the seventy to eighty percent range, or lower, up to
approximately ninety-eight percent. For Fortune 100 retail and CPG brands managing
hundreds of DCs, even marginal improvements in yield throughput across that network
translate to hundreds of millions of dollars in recoverable value. Congestion, idle time, and
spatial bottlenecks on inbound and outbound lines, conveyors, picking stations, and mixing
areas all become visible and addressable.
Bridging the OT/IT Gap
One of the central ideas in the conversation is what Kim calls bridging the OT/IT gap: the
divide between operational technology (the physical systems running the DC floor) and
information technology (the planning and data systems managing the operation from above).
These two layers have historically existed in parallel. A DC might have hundreds of cameras
and sensors generating continuous footage, alongside a WMS generating continuous
machine signals. In most cases, those two systems do not talk to each other. Operators are
left trying to reconcile them manually, which is slow, incomplete, and inconsistent.
The next phase of what Superb AI is building is a direct answer to this problem: merging
telemetry data from WMS systems with computer vision data and feeding the combined
output into an orchestration layer. From there, operators can deploy purpose-built agents for
safety, logistics, defect detection, and operational efficiency. Agents that previously ran only
on machine signals would now have the spatial context to understand what forklifts were
actually doing, what cameras were actually seeing, and why the numbers do not match the
plan.
Thompson's reaction to this captures the broader significance: "Blending that together with
the injected agent side, making decisions with confidence. I think that's a dream for a lot of
people."
How to Know If This Is a Fit
Kim is candid that computer vision comes with baggage. Many operators have been burned
before by vendors who overpromised and underdelivered, leaving them skeptical of the whole
category.
Superb AI's response to that skepticism is a rapid feasibility assessment. The process starts
with a few videos of the specific use case and a conversation about what KPIs matter most.
Within forty-eight hours, the team can produce a working demonstration using object
detection and vision language model reasoning, giving the operator a tangible, visual sense of
what the solution would look like in production.
If the demonstration hits the mark, the engagement moves to a full pilot with an on-site
deployment team, hardware assessment, and model tuning to meet the latency and accuracy
requirements of the client's environment. From there, the Superb AI team handles ongoing
refinement and optimization continuously.
Thompson drew an important parallel from the Telaid side: this kind of proof-of-concept
approach, validate before you deploy, aligns directly with how Telaid approaches technology
implementations for its clients. Seeing the solution working on your actual problem, before
committing to a full rollout, is the right foundation for any enterprise technology decision.
The Partnership in Practice
The Telaid and Superb AI partnership brings together two complementary capabilities. Telaid
provides the infrastructure, networking, and deployment services that give vision solutions a
stable foundation to run on. Superb AI provides the computer vision platform, the model
development tooling, and the ongoing refinement that turns raw camera data into operational
intelligence.
For distribution center operators looking to close the gap between what their systems report
and what is actually happening on the floor, that combination addresses both sides of the
problem: the infrastructure that makes deployment possible and the intelligence layer that
makes it valuable.
Ready to take the next step? Contact Telaid Solutions Group below to see how we can partner on your next project.
