AI to Decentralized Artificial Intelligence and Traceability.
Hands-On Workshop: Build a DAI Network That Governs Itself
Understand the technology. Then build a network with it.
Live online · Two Saturdays, 2 hours each · Hands-on · 25 seats
Dates: Saturday, August 1 and Saturday, August 8, 2026 (two sessions)
Time: 8:30 am to 10.30 am EST both mornings Length 2 hours each session, four hours total
Format: Live on Zoom, hands-on Seats 25
Cost: Free
Who this workshop is for?
AI professionals and data analysts who want to understand the infrastructure that makes AI trustworthy, auditable, and traceable, not just how models generate results.
Career changers looking to build practical skills in emerging technologies before they become mainstream and highly competitive.
Healthcare and IT professionals preparing for the arrival of AI and wanting to understand the technologies behind security, compliance, governance, and trust.
Software developers and engineers who want the concepts behind decentralized systems, consensus networks, shared verifiable records, and AI traceability without focusing on any one framework.
Business analysts, project managers, and technology leaders who need to see how AI, traceability, and decentralized technologies fit into enterprise digital transformation.
Non-developers who are curious about emerging technologies and want a hands-on introduction with no programming experience required.
Technology enthusiasts and innovators interested in the future of AI, traceability, IoT, automation, autonomous systems, and the next generation of intelligent infrastructure.
Two industries. The same problem. No one who can answer it.
A hospital. A patient deteriorates overnight. The hospital’s system says the alert fired. The device manufacturer’s log says the firmwarewas current. The insurer’s file shows no notification received. The regulator asks a simple question, what actually happened, and four sophisticated organizations produce four answers that don’t match.
A food supply chain. A batch of romaine is pulled for contamination. The grower says it left the farm clean. The processor says it arrived sealed. The distributor says the cold chain held.The store says it followed the recall. The question, where did this batch actually break and which other batches shared its path, takes days to answer. By then people are already sick.
Nobody in either story is lying. Everybody kept records. The records just don’t agree, and no one has the standing to say which is right.
One problem, two industries: independent parties who don’t trust each other, and no shared record they can all rely on. Solving it is what this workshop is about, and it is more approachable than the jargon around it suggests.
Across two Saturday mornings you will understand what this technology actually is, who genuinely uses it, where it has failed, and how decentralized AI is built on top of it. The first session builds your understanding. The second, you build a working four-agentnetwork yourself and watch it reject a participant trying to rewrite history, with no administrator and no referee.
Why traceability matters
Every AI system is moving toward having to answer questions it currently cannot:
- Where did this data come from?
- Who trained the model, and on what?
- Which version produced this specific decision?
- Can the result be audited after the fact?
- Has the underlying data been tampered with?
- Can a regulator verify the decision independently?
The agenda
Weekend one builds your understanding. Weekend two builds the network. The week in between gives the concepts time to settle, so you arrive at the lab ready to build rather than catching up.
Who’s already doing this
Ten names you will recognize
Real companies that have run Decentralized AI and traceability and provenance on shared, verifiable records:
- Walmart traced leafy greens, mangoes, and pork on a shared record, cutting trace time from days to seconds.
- Nestlé put its Rainforest Alliance coffee brand on a traceability platform so buyers could follow the beans to source.
- Carrefour built shared-record traceability into its Act for Food program across products in dozens of countries.
- Tyson Foods ran farm-to-facility food-safety tracking pilots on the same technology.
- Unilever joined the IBM Food Trust consortium to test supply-chain traceability at scale.
- Kroger took part in the multi-retailer effort to trace fresh produce back to the farm.
- De Beers built Tracr to trace diamonds from mine to retail and prove they were conflict-free.
- Cargill used a shared record to let shoppers trace a Thanksgiving turkey back to the farm.
- Maersk built a global shipping-record platform with IBM, then wound it down when too few partners joined, a useful lesson in when this technology does and does not fit.
- IBM, SAP, Oracle, and VeChain are the platform vendors behind many of these, and the companies whose job postings you will actually find.
- Avaneer Health runs eligibility and prior authorization across payers covering 80 million people, with data staying under the control of whoever created it.
- MediLedger verifies 1.6 billion pharmaceutical transactions a year across manufacturers making 80 percent of US prescription drugs.