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Lilt

Agenticness7/36·Guided Assistant

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 →

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Paid
Enterprise
B2B
Usage-Based
For Teams
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Recent activity

What's happened with Lilt lately

  • Score change
    Rubric 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.

    See the news that prompted this

News mentions sourced from our news feed; score changes from periodic re-evaluations.

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About

What It Is

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.

What to Know

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,...

Key Features
Designs supervised fine-tuning workflows for multilingual and multi-domain datasets
Produces prompt-response authoring and targeted rewrites
Generates preference and judgment data alongside SFT data
Supports domain grounding and RAG validation
Uses rubrics, boundary cases, anchors, and gold sets for task design
Use Cases
Training instruction-following models that need consistent labels across languages
Building multilingual SFT datasets for domain-specific assistants
Creating preference and judgment data to complement alignment training
Agenticness: Guided Assistant

Executes tasks you assign, one step at a time, within narrow domains.

High evidence
Last evaluated: May 23, 2026

Dimension Breakdown

Action Capability
Autonomy
Adaptation
State & Memory
Safety
How agenticness works →

Categories

Translation & Localization
Pricing
  • Enterprise: Pricing not publicly available; contact sales.
Details
lilt.com
AddedApril 3, 2026
RefreshedApril 3, 2026
Agenticness
Guided Assistant
Quick Facts
DeploymentCloud-hosted
AutonomyCopilot (human-in-loop)
Model supportSingle model
Open sourceNo
Team supportEnterprise
Pricing modelSubscription
Interfaceweb, api
Sources
WebSupervised Fine‑Tuning Data at Scale | LILT
WLilt (company)
Weblilt on PyPI
Last updated May 23, 2026
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