Multilingual SFT data and QA for production AI teams
LILT helps enterprise teams design and operate supervised fine-tuning workflows across languages and domains. It focuses on data quality, governance, and localization consistency rather than model hosting or agent execution.
Agenticness = how independently a tool can take action, scored across 9 dimensions. Scored independently by David Kooi, Skylark Creations — see full rubric → Last re-evaluated 31 July 2026.
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What's happened with Lilt lately
- Score changeRubric upgrade v3_0 → v3.1: score 10/32 → 7/3610 → 7/36(-3)
Rubric upgrade: agenticness v3.0 (8 dims, /32) → v3.1 (9 dims, /36). Adds Dim 9 (Operator Sovereignty), splits Dim 6 into 6a/6b lenses, tightens Dim 4 autonomous-retry distinction. Not a product change — score shift reflects new dimension + recalibrated rubric, not a change in the tool. Fanout suppressed.
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News mentions sourced from our news feed; score changes from periodic re-evaluations.
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About
LILT’s Supervised Fine-Tuning (SFT) offering is a data-production service for teams training AI models across multiple languages and domains. It is aimed at enterprise buyers who need reliable instruction-following, domain grounding, and safety data without losing localization quality.
This is best understood as an enterprise data and localization service, not a general-purpose AI agent or chatbot. The strongest signal in the content is quality control: LILT highlights task design, multilingual normalization, calibration,...
Executes tasks you assign, one step at a time, within narrow domains.
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- Enterprise: Pricing not publicly available; contact sales.
When Lilt changes its pricing, we catch it the next morning
We recrawl every tool in this directory daily. AI Insider is the 3-minute brief on what actually changed — pricing, features, score moves on our 36-point rubric — often before any press coverage. Sent daily. Skipped when nothing happened.