GenAI Training & Alignment

Turn raw models into
safe, helpful products.

RLHF, SFT, fine-tuning, alignment and human evaluation — the human-in-the-loop layer that takes a foundation model from clever to production-grade.

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Trusted by
SOC 2 Type II
ISO 27001
GDPR
Constitutional AI
Anthropic-style
120M+
Preference comparisons
18%
Avg. preference-win-rate lift
60+
Languages covered
6k+
Domain expert raters
What's inside

Everything you need to ship production-grade data.

A single platform that covers the full lifecycle — from sourcing through evaluation and deployment.

Pairwise + N-wise preference ranking for reward model training
Supervised fine-tuning (SFT) with gold-standard demonstrations
Constitutional / rule-based AI guidelines + critic models
LoRA / QLoRA / DoRA / full-fine-tune workflows
Domain-expert raters (MD, JD, PhD-CS, finance)
Multilingual preference data across 60+ languages
Reward-model debugging and reward-hacking audits
Eval harness integrations (HELM, MMLU, MT-Bench, custom)
Per-rubric scoring with per-dimension calibration
Sub-engines

Pick the workflow your team needs.

RLHF

Pairwise preference ranking to train your reward model. Calibrated, multilingual, with domain expertise.

  • Pair / N-wise comparison
  • Reward-model debugging
  • Constitutional augmentation

SFT

High-quality supervised demonstration data for instruction-following and tool-use baselines.

  • Gold-standard demonstrations
  • Tool-use traces
  • Multi-turn dialog data

LLM Fine-Tuning

End-to-end fine-tuning with LoRA, QLoRA, DoRA or full-parameter — across HF, Together, Modal, or your cluster.

  • LoRA / QLoRA / DoRA
  • Distillation + merge
  • Per-domain checkpoints

AI Alignment

Constitutional rules, critic models, safety classifiers and red-team feedback wired into training.

  • Constitutional AI
  • Critic models
  • Safety classifier training

Human Evaluation

Multi-rubric human scoring with PhD reviewers, calibrated across helpfulness, accuracy, safety and style.

  • Per-rubric scoring
  • PhD reviewer pool
  • Side-by-side comparisons

Model Evaluation & Benchmarking

Comprehensive benchmarking and evaluation to measure model quality, reasoning, safety, factual accuracy, and production readiness across real-world scenarios.

  • HELM & MT-Bench Evaluation
  • Safety & Hallucination Testing
  • Performance Benchmark Reports
Industry outcomes

Real results, not demos.

Frontier Lab

RLHF at scale across 11 Indic languages

+18% win-rate
Healthcare

Clinical-Q&A SFT with MD reviewers

0.94 accuracy
BFSI

Agent alignment for fraud workflows

-37% harmful actions
FAQ

Questions, answered.

How big a preference dataset do I need?

For a baseline reward model, 10-30k high-quality preferences typically suffice. We can co-design sampling strategy to target weak spots in your current model.

Can you train the model for me?

We deliver the labelled data and reward model. Training runs are best executed by your team or a partner — we integrate with HF TRL, Together, Modal, AWS Bedrock, and on-prem clusters.

What's your approach to constitutional AI?

We co-design a constitution with your team — rules + few-shot examples + counter-examples. Raters reference it during scoring; we provide a critic-model dataset for self-critique training.

How do you avoid reward hacking?

Multiple defences: diverse rater pools, ablation on reward features, adversarial probe sets and reward-model uncertainty calibration.

Can you evaluate an existing model before fine-tuning?

Absolutely. We begin with a comprehensive benchmark covering reasoning, factual accuracy, hallucination rates, safety, latency, and domain-specific performance. This baseline helps identify weaknesses and guides the most effective fine-tuning and alignment strategy.

Related engines

AI Data Services

Multimodal data sourcing and annotation.

Evaluation, Trust & Safety

Independent benchmarks and red-teaming.

Managed AI Operations

24×7 production human-in-the-loop.

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