Industry reports, technical whitepapers, and methodology guides from the Deaimer research team. We publish because better practice is better for everyone.
Based on interviews with 80+ AI teams across frontier labs, enterprise, autonomy, and healthcare. 48 pages on what's actually happening inside data operations — budgets, staffing, tooling, and the gaps everyone is trying to solve.
Download report (PDF) →Field research across 80+ AI teams. Staffing models, tooling, budget allocation, and the operational patterns shaping frontier AI.
Calibration discipline, rater drift monitoring, and the operational patterns that keep alignment programs producing high-quality signal.
Why data provenance matters, what auditable data operations look like, and what regulators should actually ask for.
How to build evaluation harnesses for multi-step, tool-using AI agents — sample construction, rubric design, failure-mode analysis.
Adversarial testing methodology used across Deaimer safety engagements — from harm taxonomies to coverage strategies.
Quantifying the gap between high-resource and low-resource language quality in foundation models — and practical paths forward.
Our published labor framework — pay floors, welfare protocols, grievance paths — and the case for industry-wide standards.
How Deaimer's platform is designed — task routing, QA gates, audit trails, and the architectural decisions that made it possible.
Tell us what you're training, aligning, or evaluating. We'll map a delivery plan, staffing model, and timeline within one working week.