Four case studies, fully decomposed
Each case study is a real industry × occupation pair from atlas_transformations. The structure is identical across all 23,797 rows: a before-scenario narrative grounded in OEWS wage and employment data, a with-daedarch after-scenario, a generic-AI counterfactual, and a daedarch pitch. The contrast across the three scenarios is what the methodology disclosure is for.
Elementary and secondary schools · Preschool, Elementary, Middle, Secondary, and Special Education Teachers
NAICS 611100 × SOC 25-2000 · Median wage $64,320 · Tier 1
After (with daedarch)
Ms. Chen's morning: 7:45 AM, she opens her Semali workbench. Overnight, the system ingested her students' prior assessments, IEP goals, and reading levels. The Adaptive Differentiation Engine has generated today's lesson: three differentiated math pathways (on-level, intervention, extension), complete with scaffolded worksheets, video explanations, and practice problems—all aut…
Pitch: DaedArch eliminates the administrative and grading burden that consumes 70% of teachers' time, freeing them to do what they're actually trained for: teach and connect with students. By automating less…
Elementary and secondary schools · Educational Instruction and Library
NAICS 611100 × SOC 25-0000 · Median wage $60,330 · Tier 1
After (with daedarch)
Maria opens her Semali workbench at 7:30 AM. The overnight Student State Harvester has already flagged three students: Jaylen's engagement score dropped below threshold for the 8th consecutive day — an Early Warning Agent drafted a counselor referral and a parent message, both waiting in her clearance queue. She approves both in 90 seconds. The Differentiation Engine generated …
Pitch: Your teachers are spending 60% of their time as data clerks and content photocopiers — tasks that your most expensive asset, human relational intelligence, is completely wasted on. DaedArch's contract…
Restaurants and other eating places · Food Preparation and Serving Related
NAICS 722500 × SOC 35-0000 · Median wage $31,200 · Tier 1
After (with daedarch)
Maria opens Semali at 7:30 AM. The overnight exception queue shows three items: a walk-in cooler that drifted to 42°F at 3 AM (auto-corrected, flagged for her verification), a romaine inventory level that triggered a supplier reorder via the connector, and one open shift for Sunday that the labor agent couldn't fill after two automated texts — it's escalating to her for a manua…
Pitch: Your restaurant operation is running on paper clipboards, group texts, and manual math — and every compliance gap, scheduling failure, and inventory variance is costing you margin you can't see until …
Restaurants and other eating places · Food and Beverage Serving Workers
NAICS 722500 × SOC 35-3000 · Median wage $29,710 · Tier 1
After (with daedarch)
Maria clocks in at 10:45 AM and opens her Semali mobile interface. The system has already ingested the reservation book, predicted lunch volume (16 customers expected), and optimized staffing (she's one of 3 servers needed; 2 others are scheduled). As customers arrive, small cameras above each table capture their body language and hand signals. When a customer at table 3 raises…
Pitch: DaedArch transforms food service from a labor-intensive, error-prone operation into a precision customer experience engine. By automating order-taking, kitchen coordination, payment processing, and ta…