Our Solutions

Agentic Training Datasets

Specialised multi-step reasoning traces, tool-use demonstrations, and decision trajectories crafted by domain experts. We capture the full decision path — not just the final output — so models learn to reason, not just respond.

• Long-horizon reasoning chains with explicit step-by-step annotation
• Tool-call sequences: web search, code execution, file management, API calls
• Partial-trajectory annotation for credit assignment research
• Failure trace datasets — what the wrong path looks like and why
• Available as SFT data, DPO pairs, or reward model training sets

How we build it

Red-Teaming & Safety Evaluation

Adversarial prompts, safety benchmarks, and structured evaluation suites. Identify model vulnerabilities before they reach production. Our red-teamers think like attackers — not testers — producing genuinely novel, high-risk inputs.

• 50,000+ adversarial prompts across harm categories: deception, manipulation, CSAM, extremism, CBRN
• Jailbreak and prompt injection datasets with creative variation
• Structured risk taxonomies tailored to your model's threat model
• Held-out evaluation suites — never leaked to the model under test
• Annotator calibration on severity scores for consistent labelling

Coverage by harm category

STEM & Scientific Data

Expert-annotated datasets spanning mathematics, biology, chemistry, and physics — built with researchers who hold advanced degrees in each domain. We cover notation diversity, proof styles, error taxonomy, and solution diversity so models develop genuinely flexible reasoning.

• Multi-notation coverage: LaTeX, natural language, symbolic, step-by-step prose
• Proof diversity — formal, informal, and worked-example styles
• Curated error taxonomy: arithmetic slips, logical gaps, sign errors, wrong theorem application
• Multiple correct solution paths per problem to build generalisation
• PhD-level contributors verified by domain credential review

RLHF & Preference Data

High-signal human preference data and reward model training sets, annotated by carefully vetted experts aligned to your model's goals and values. We screen for consistency, bias patterns, and articulation quality — not just speed.

• Pairwise preference annotation with structured rationale capture
• Multi-dimensional scoring: helpfulness, safety, factuality, tone
• Annotator calibration rounds to align with your rubric before production
• Screening for consistency bias (length preference, confidence preference)
• Available as Bradley-Terry pairs, scalar rewards, or Constitutional AI critiques

What makes our RLHF data different

Multilingual & Cross-cultural Data

High-quality original content in 30+ languages — never machine-translated. Native-speaker contributors with deep cultural fluency ensure your model understands not just words, but meaning, context, and register.

• Original human-generated content — zero MT post-edit
• Native speakers with cultural and dialectal fluency in every target language
• Support for low-resource and underrepresented languages via in-region partnerships
• Cross-lingual preference annotation and translation quality evaluation
• Culturally-sensitive content review for safety and localisation accuracy

Current coverage includes: English, Swahili, and more. Contact us to confirm your target language.