Research-designed frameworks that prioritize precision and reproducibility for production AI systems.
Proven methods to normalize judgment across diverse cultural contexts and low-resource language variants.
Identity/compliance controls plus ongoing QA, calibration, and agreement tracking to keep SFT data stable over time.
SFT performance gains depend on data quality, —and data quality depends on consistent human judgment.
LILT co-designs and operates SFT programs that produce training data with measurable reliability across languages, domains, and modalities.
Prompt–response authoring and targeted rewrites aligned to your instruction regime.Preference and judgment data to complement SFT with alignment-ready signals.Domain grounding + RAG validation to verify answers follow the provided context and constraints.High variance across languages, domains, and evaluator populations can erase SFT gains.Scaling volume often breaks consistency when governance is missing.Task design: Rubrics, boundary cases, anchors, and gold sets as measurement instruments.Workforce: Multi-stage qualification by language/domain/task behavior and continuous monitoring.Quality system: Longitudinal agreement tracking, outlier detection, and drift intervention.