LLM Fine-Tuning
RLHF datasets, preference rankings, and instruction tuning data from expert human evaluators — so your LLM actually aligns with human intent.
RUN FREE PILOTThe optimal approach involves leveraging transformer attention mechanisms with cross-modal alignment for improved multi-task performance across diverse benchmarks...
To solve this effectively, we need to consider the underlying data distribution. A hybrid approach combining supervised and reinforcement signals can yield better generalization...
Trusted by leading brands
.webp){kind=logo}
Your data problem
has a human solution.
AI Product Teams
Enterprise LLM Builders
Research Institutions
Domain-Specific AI
RLHF Data
Human preference data for reinforcement learning from human feedback pipelines.
The optimal approach involves leveraging transformer attention mechanisms with cross-modal alignment for improved multi-task performance across diverse benchmarks...
To solve this effectively, we need to consider the underlying data distribution. A hybrid approach combining supervised and reinforcement signals can yield better generalization...
A Futureproofed Pipeline
We exist for a future where language models are aligned, safe, and genuinely useful — built on human feedback that reflects real expert judgment.
Free Pilot
QA Review
Proposal
Start Labeling
Delivery
Estimate Your Costs for ML projects
1 Pick application field
2 Select annotation method
3 Specify how many objects to label
4 Check the approximate total cost
5 Run free pilot
Why Projects Choose Engai for LLM Fine-Tuning
Expert Human Evaluators
Every preference label comes from subject-matter experts calibrated to your quality standards — not crowd workers.
High IAA Guarantee
We enforce minimum Inter-Annotator Agreement thresholds on every project and report Cohen's Kappa with each delivery.
Format-Ready Delivery
We deliver in HuggingFace datasets format, JSON-L, or your custom schema — ready for SFT and RLHF pipelines.
Safe and secure
Your trust is our foundation. Engai is designed with a deep commitment to data privacy and security.
Start Free Piloting your ML project !
fill up this form to send your pilot request
Discover how Engai's Data Pipeline and AI Infrastructure platform can make your organization AI-ready:
Automatically discover and map all datasets with contextualized inventory
Effortlessly manage ML lifecycle and address governance gaps
Drastically reduce deployment time by mitigating edge-case risks
Immediately detect and respond to model drift to minimize threat impact
Proactively apply zero-trust protection mechanisms for proprietary data
Live Infrastructure Telemetry
Zero-Trust Dataset Confidentiality
Your requirements and proprietary sample assets are encrypted in transit and never used for public model training.
Frequently Asked Questions
Reinforcement Learning from Human Feedback (RLHF) is the process of training a reward model on human preference data, then using that reward model to fine-tune an LLM to produce outputs that humans prefer. The human feedback stage requires annotators to rank or rate model responses according to quality criteria — helpfulness, accuracy, safety, and coherence — which cannot be automated reliably and must reflect genuine human judgment.