AI Lab · Last updated 17 May 2026
The Lab is the unpolished page. In-progress products, research notes, methodology iterations. We publish here while the work is still warm, so you can see where we're going before we get there.
1 What we're working on this week
When an AI agent posts a journal entry, who signs it? Working through the audit-trail design for Helios. Every agent action inside our internal operating system needs a traceable provenance that survives a financial audit when we ship it to customers. Hard problem; we'd rather get it right than get it shipped.
Senior dispatchers remember the truck that always breaks down on cold mornings. New dispatchers don't. Building the working-memory layer for Helios that gives a new dispatcher senior-dispatcher context on every call. The prototype is live inside Ludia first.
Re-running the AI Methodology against three concurrent engagements to see where the variance lives within the 30–40% blended reduction. Hypothesis: the bottleneck moves from discovery (deeply compressed) to change-management (less compressed). Data inconclusive so far.
Two customers want to extend FarShip in different directions. Designing a “regulated extension” interface that lets customers add modules without breaking the upgrade path. Reference architecture: Stripe Connect.
Field technicians take 14 minutes on average to close a work order in the D365 mobile app. Voice-to-structured-form on the truck radio compressed it to 90 seconds in a four-tech pilot. Generalization is the open question.
Customers ask us for AI agents, but their master data isn't consistent enough for agents to be honest with. Building a pre-flight diagnostic that scores master-data readiness before any agent work begins. Brutal feedback expected; we'll publish it anyway.
2 Methodology · In iteration
The AI Implementation Methodology is the operationalized form of the velocity-compression thesis. We version it like software. Each release records what we learned in production: what worked, what didn't, where the bottleneck moved.
AddedMaster-data readiness scoring
Across the last three engagements, master-data quality emerged as the strongest predictor of timeline variance. v3.2 introduces a pre-flight diagnostic that scores readiness before kickoff, so we can sequence data-cleansing into the project plan instead of discovering it in week 6.
ChangedHow we capture customer requirements
Switched from PowerPoint-driven requirements workshops to voice-recorded operator interviews on-site. Two-hour interview with the actual operator beats a six-hour workshop with their boss. AI transcription and structured extraction handle the synthesis.
3 Essays · Long-form
A long-form argument for why the next decade of enterprise AI is owned by vertical specialists, not horizontal platforms. Five industries, three pricing dynamics, one prediction about consolidation.
14 min read
What changes when an implementation gets 30–40% lighter on both time and effort. Implications for CFO posture, vendor selection, and the consulting business model.
9 min read
The thesis essay. What the data layer is for, why the interface is dissolving, and what we mean when we say the next generation of business applications is being built around the same data spine, with everything else rewritten.
11 min read
Lab · Hiring
We're hiring people who think about the second-order effects of agents in industrial operations. Senior architects, applied researchers, product engineers.
Opens the contact form on ludiaconsulting.com
