Autonomy unbridled. Governed by math, not blind faith. https://nitter.gq/t.co/iDkNXZRf5m
Hamilton, Ontario
Joined March 2023
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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.
Air-gapped AI is becoming more relevant in defense, manufacturing, and regulated environments. When data stays on-site, trust cannot depend on a remote service.
Sertn is built for verifiable AI that can operate where the data lives.
sertn.ai/
Self-hosted inference is becoming more common as teams want lower latency, more privacy, and more control. But local deployment should not mean less accountability.
Sertn is built to keep inference verifiable even when it runs close to the edge.
sertn.ai/
1/ Operational dashboards are good at averages.
Airports often care about the exception.
The one aircraft that entered the wrong stand. The one person who stopped moving. The one baggage position that suddenly backed up.
2/ Sertn is designed around those moments.
Models can watch continuously, surface the event when a defined condition breaks, and route it into the next operational step without someone watching every feed.
sertn.ai/
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.
1/ Traditional enterprise auditing is retrospective.
Collect logs, interview people, reconstruct events, then decide whether controls behaved as expected.
AI operating at machine speed makes that model increasingly expensive.
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.
1/ Enterprise AI often has two things it cannot freely expose:
customer data and proprietary models.
That creates an awkward trust problem. A third party may need assurance that the right computation happened without being allowed to inspect everything inside it.
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.