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Industry solution

AI training data for Foundation models

RLHF, preference data, SFT demonstrations, and red-teaming for LLM alignment: multilingual, high-agreement, at a plannable cadence.

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Overview

Data operations tuned to Foundation models

Frontier models need alignment data at a steady cadence across many languages. Corpshore AI produces ranked comparisons, preference pairs, SFT demonstrations, and reward-model data with native, in-region annotators in 35+ languages, then red-teams the model against real-world and policy risks. Employed teams under one SLA give you a plannable pipeline rather than a variable crowd.

Preference pairs and ranked comparisonsSFT demonstrations and instruction dataReward-model training dataMultilingual alignment and evaluation
AI training data for Foundation models at Corpshore AI
The challenge

Where the data breaks down

  • Preference and demonstration quality hinges on annotator judgment, which a crowd cannot hold consistent.
  • Alignment has to hold across languages, not just English, or the model regresses outside its core locale.
  • Frontier programs need a steady weekly cadence, which spot crowds cannot guarantee.
  • Red-teaming needs skilled adversarial testers, not volume clickers.
Our approach

How Corpshore AI delivers

  • Employed, calibrated annotators produce preference and demonstration data against a shared rubric.
  • Native, in-region annotators cover 35+ languages so alignment holds beyond English.
  • A staffed pipeline delivers a plannable weekly cadence rather than variable crowd throughput.
  • Skilled red-teamers probe for jailbreaks, unsafe outputs, and policy violations under a defined taxonomy.
Delivery workflow

From scope to delivery, in foundation models

Step through how a foundation models engagement runs under one operator and a single SLA.

1. Scope

We agree the rubric, task types, and language mix, and calibrate annotators against gold examples before the pipeline opens.

Stage 1 of 5
Data types

What we deliver

Ranked comparisons and preference pairs
SFT and instruction-following demonstrations
Reward-model training data
Multilingual prompts and responses in 35+ languages
Adversarial and red-teaming transcripts
35+
languages for multilingual alignment
Calibrated
employed annotators on a shared rubric
Cadence
plannable weekly delivery, not spot crowds
Red-team
adversarial testing against a policy taxonomy
Why Corpshore AI

The operator advantage for foundation models

  • Employed teams hold judgment consistent across weeks, which a marketplace crowd cannot.
  • Native coverage in 35+ languages keeps alignment from regressing outside English.
  • Annotation, RLHF, and red-teaming run under one SLA, so the alignment loop stays coherent.
Relevant services

Services behind this solution

Annotation & LabelingEvery modality, 97%+ accuracyRLHF & Preference DataLLM alignment at scaleRed-Teaming & EvaluationAI safety & model evaluation
FAQ

Foundation models, answered

Ranked comparisons, preference pairs, SFT and instruction-following demonstrations, and reward-model training data. Work is done by employed, calibrated annotators against a shared rubric.

Ready to scope a pilot?

Tell us your modality, volume, and languages. We'll return an indicative scope, timeline, and cost band.

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