LLM Fine-Tuning

LLM fine-tuning services & human feedback that align your models.

LLM fine-tuning services done right: RLHF, SFT, prompt engineering and expert evaluation — with STEM and domain specialists on the hard prompts, so your model learns what good actually looks like. We supply the human feedback that turns a capable base model into a reliable, fine-tuned product.

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Meet Graveiens AI

A quick look at how Graveiens AI partners with teams to deliver human data for AI models.

700+
Experts & SMEs
25+
Languages
4-stage
QA workflow
100%
Human-in-the-loop
Why fine-tuning

Turn a capable base model into a reliable product

Base models are broadly capable but rarely production-ready. Fine-tuning aligns them to your task, tone and safety bar using human data — RLHF preference rankings, SFT demonstrations and targeted evaluation. Graveiens AI brings a STEM and domain SME bench to the fine-tuning work most data vendors cannot staff, and pairs it with LLM evaluation and data annotation services so every iteration is measured, not guessed.

Scope a fine-tuning pilot
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What we do

LLM fine-tuning services: from preference data to fine-tuned models

We supply the human judgment that turns a capable base model into a reliable product — ranking, demonstrating, prompting and stress-testing across domains and languages.

RLHF & Preference Data

  • Pairwise & ranked comparisons
  • Reward-model training data
  • Helpfulness, harmlessness, honesty
  • Multi-turn dialogue rating

Supervised Fine-Tuning (SFT)

  • High-quality demonstrations
  • Instruction-response pairs
  • Domain & task-specific data
  • Style and format control

Prompt Engineering

  • Prompt design & optimization
  • Chain-of-thought curation
  • Template & system-prompt libraries
  • Few-shot example sets

Red-teaming & Safety

  • Adversarial prompt discovery
  • Jailbreak & misuse testing
  • Toxicity & bias probing
  • Policy-aligned labeling

STEM & Domain Trainers

  • Math, science & code review
  • Medical, legal & finance SMEs
  • Curriculum-grade validation
  • Reasoning-quality grading

Fine-Tuning & Optimization

  • Dataset curation & cleaning
  • Pipeline & tooling support
  • Python, TensorFlow, PyTorch
  • Evaluation-driven iteration
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How it looks

Preference feedback in practice

RLHF

Rank, compare, improve

Trained raters and domain experts compare and rank model responses against your rubric, producing the preference data your reward model needs — with quality controls to keep labels consistent throughout the fine-tuning loop.

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LLM fine-tuning data

Instruction, domain and preference data to specialise your model

Fine-tuning turns a general model into one that knows your domain and your voice. Graveiens AI builds the datasets that make that possible — instruction-response pairs, domain corpora, preference data and evaluation sets — produced by trained annotators and subject-matter experts and delivered ready for supervised fine-tuning and RLHF.

We design data to your task and taxonomy, weave in the edge cases your model struggles with, and hold every batch to a measured accuracy bar through our four-stage quality workflow.

Talk to our LLM data team
Graveiens AI labelsentitiesforsentimentandintent
Capabilities

Fine-tuning data we deliver

From instruction data to domain corpora and evaluation.

Instruction & SFT data

High-quality instruction-response pairs across tasks, tones and formats.

Domain & expert data

STEM, medical, legal and finance data curated by qualified SMEs.

Preference data

Rankings and comparisons for RLHF and DPO-style tuning.

Evaluation sets

Held-out, rubric-scored data to measure fine-tuning gains.

Use cases

Where fine-tuning data is used

Representative LLM programmes we support.

Domain assistants

Specialising models for healthcare, finance and law.

Multilingual tuning

Instruction and preference data across 25+ languages.

Task-specific models

Data for extraction, summarisation and reasoning tasks.

Built for measurable gains

Data designed to move your evals

Good fine-tuning data is targeted, clean and measurable. We align on the behaviours you want to change, curate data that addresses them, and pair it with evaluation sets so you can see the lift. Consistent QA and pay-on-approval delivery keep a first fine-tuning pilot low-risk.

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Compare

Graveiens AI vs other LLM fine-tuning companies

How our LLM fine-tuning and RLHF services compare with other providers on focus, expertise and QA.

ProviderCore focusModalitiesQA / accuracy approachEngagement model
Graveiens AIUsLLM fine-tuning with RLHF, SFT and expert evaluationText, dialogue, code, domain promptsSTEM and domain SME reviewers, ISO 9001:2017Pay-on-approval pilots, managed programs
MacgenceRLHF and SFT data for LLMsText, dialogue, promptsManaged crowd with human QAProject-based managed teams
Cogito TechLLM data and prompt annotationText, dialogueHuman-in-the-loop QAManaged teams
ShaipDomain LLM data and evaluationText, audio, domain promptsDomain-expert QAOff-the-shelf datasets plus services
iMeritLLM data operations and evaluationText, dialogue, multimodalExpert-in-the-loop QADedicated managed teams
SamaGenerative AI data and alignmentText, image, multimodalSamaAssure QAManaged workforce
Surge AIRLHF and LLM data labelingText, dialogueExpert human ratersAPI plus managed service
Why Graveiens AI

Why teams choose Graveiens AI for model fine-tuning

Compliance-first delivery and a pay-on-approval model that de-risks every engagement.

Real subject-matter depth

A STEM SME bench inherited from our education business — rare for a data vendor.

Multilingual feedback

RLHF and SFT data across 25+ languages, not just English.

Consistent quality

Rubric-based grading with calibration and four-stage QA.

Pay on approval

Invoiced only for approved deliverables.

FAQ

Questions, answered

What does LLM fine-tuning with Graveiens AI involve?
We provide the human data for LLM fine-tuning end to end: RLHF preference and reward data, SFT demonstrations, prompt engineering, red-teaming and expert evaluation, across domains and 25+ languages.
Do you provide RLHF and SFT data?
Yes — pairwise and ranked preference data for reward modeling, plus high-quality demonstrations for supervised fine-tuning, across domains and languages.
Can your experts handle STEM and specialist prompts?
Yes. Our SME bench includes math, science, code, medical, legal and finance specialists for prompts that need genuine domain judgment.
Do you support red-teaming and safety work?
Yes — adversarial prompt discovery, jailbreak and misuse testing, and policy-aligned safety labeling.
How do we pilot this?
Start with a small rated batch against your rubric; you approve the output before we scale.

Related services

Align your model with expert feedback

Send us a sample task. You only pay for deliverables you approve.

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