Inference Labs took the stage in Dubai and came away with the audience vote. Big thanks to the Daos Hub Dubai team for bringing together founders, investors, and operators, and to @colingagich for representing us and presenting the pitch.

Computer vision is moving from object detection to understanding scenes, relationships, motion, and context. That opens much richer operational workflows. Sertn is built for that shift from “what is here?” to “what is happening?”. sertn.ai/

Crowd problems usually become visible only once they are already disruptive. Sertn can monitor density at choke points and detect unusual stillness or posture, helping airport teams respond to congestion or a person down before someone has to report it manually.

An AI policy can say what a system should do. Runtime evidence shows what actually happened. That distinction is becoming important enough that new standards are emerging around independently verifiable AI governance and execution records.

Once computer vision can trigger actions, the useful question changes. It is no longer only “what did the model detect?” but “what happened because of it?”. Sertn helps connect inference, action, and evidence in one workflow.

Most enterprise integrations expose an API. The next generation may also expose evidence. Input accepted. Model executed. Output produced. Proof attached. At Inference Labs, we think verification can become an interface between AI vendors, customers, auditors, and machines.

Ports are full of mixed traffic: vessels, launches, bulkers, cargo ships, and navigation markers all moving at once. Sertn can classify and track activity, turning live camera feeds into structured operational data for traffic monitoring, safety, and port operations.

The model is becoming only one component inside a much larger AI runtime. Memory, tools, routing, actions, and state all matter too. Inference Labs is building for that broader system, where trust has to survive beyond the model call. inferencelabs.com/

It’s time for a new chapter for Inference Labs and SN2. The developers and community behind SN2 have produced more than 6 billion cryptographic proofs and found new ways to improve zkML performance. Thank you to everyone who helped build and push this work forward. Dan joined our team from OTF over a year ago. His applied research helped us scale DSperse, and many of you already know his work. We’ve now entered into an agreement for Dan to take over SN2 as @danilabsai. He’ll lead its direction and development, focused on fast, affordable AI inference. We’re proud of what we’ve built together and look forward to seeing what Dan and the community build next. Follow @danilabsai and visit danilabs.ai for updates.

Baggage congestion becomes expensive long before it becomes obvious. Sertn can monitor bag accumulation and clearance rates at each make-up position, helping airport teams see bottlenecks forming early and identify exactly where the pressure is building.

When a port or industrial site depends on a few critical access routes, disruption quickly becomes a problem. Sertn can track vehicle flow, queue length, stoppages, and recovery in real time, turning camera feeds into an operational record of where access is breaking down.

Content provenance is expanding from images into audio and API-based verification. That trend matters beyond media. Once users get used to asking “where did this come from?”, the same expectation will reach enterprise AI decisions and model outputs.

In manufacturing, milliseconds matter. Vision models often need to run beside the production line, not wait for the cloud. Sertn is focused on making those real-time decisions easier to deploy, inspect, and verify.

Robotics is moving toward shared standards for capabilities, interfaces, and deployment. That is what happens when a technology matures. At Inference Labs, we think evidence and verification need the same treatment.