Preference data, ranking tasks, and alignment workflows designed for real enterprise deployment.
Into23 helps AI teams build the human feedback layer that model performance depends on in production. We support multilingual preference data, rubric design, rater calibration, and domain-aware feedback workflows so RLHF programs can move beyond generic English judgments.
Starting from $25,000 per feedback batch · Structured preference and adjudication programs are scoped by rubric complexity, calibration, and reviewer mix.
We gather ranked comparisons, pairwise judgments, and rubric-based feedback aligned to your target behaviours and policies.
Feedback programs can run across major APAC languages and other target markets so alignment is not limited to English.
Where needed, we combine native speakers with subject familiarity in legal, financial, technical, or regulated content.
Guidelines, training rounds, and adjudication help maintain judgment consistency across distributed human feedback teams.
We can support classical RLHF tasks as well as adjacent preference-learning and direct-optimisation workflows where the feedback design still matters.
Clients receive visibility on throughput, disagreement patterns, and where the schema or instructions may need refinement.
We work with you to define what good, safe, and useful model behaviour looks like in context.
Ranking tasks, preference rubrics, and reviewer guidance are tailored to the model objective and target languages.
Human feedback collection is monitored for agreement, drift, and instruction clarity before volume ramps.
You receive structured outputs, QA observations, and recommendations for future feedback cycles.
Into23 is positioning RLHF through scoped pilot programs and strategic delivery support where clients need multilingual preference data, rubric design, and reviewer calibration before a larger launch.
Human preference data remains a core input for alignment regardless of the specific training method. The quality and diversity of that feedback directly affects model behaviour, especially in multilingual and culturally varied contexts.
English-only reviewer pools produce alignment data that reflects English cultural norms and language patterns. Multilingual RLHF requires native speakers who can judge quality in context, with rubrics calibrated for each target market.
Yes. Into23 positions RLHF through scoped pilot programs where clients can validate rubric design, reviewer calibration, and data quality before committing to larger volumes.
Get a custom quote for your RLHF / human feedback project. Our team typically responds within 24 hours.