Teach your model what “good” looks like.
Pre-training gives a model knowledge; post-training gives it judgment. OmniTech produces the expert human data — responses, preferences, critiques and rewrites — that aligns generative models to your standard of quality, tone and safety.
The human data behind SFT, RLHF and DPO.
We staff and qualify the contributors, design the guidelines with your researchers, and run the production and QA. You receive clean, structured, measured data.
Expert responses & SFT
High-quality demonstrations and instruction-following responses across domains, written to your specification and format.
Preference ranking
Pairwise and list-wise preference judgments with documented rationale — the backbone of RLHF and DPO reward signals.
Response critique
Structured critiques that identify what a response gets wrong and why — errors, omissions, unsafe content and reasoning gaps.
Response rewriting
Improved, corrected or safer rewrites that turn a weak response into a target — ideal for corrective and contrastive training.
Reasoning & step data
Chain-of-thought, worked solutions and process supervision for math, coding and multi-step reasoning tasks.
Human feedback operations
Ongoing feedback collection tied to your model releases, with calibration so the signal stays consistent over time.
Consistency is the whole game.
Preference and critique data is worthless if two annotators disagree for arbitrary reasons. Our qualification and calibration turn subjective judgment into a repeatable signal — and adjudication resolves the genuinely hard cases so your reward model isn’t learning noise.
- Rubric co-design with your research team
- Calibration against gold examples & peers
- Adjudication on disagreement & edge cases
- Agreement & drift tracked across releases
Illustrative preference record
Pair post-training with evaluation.
Pilot a post-training data program.
Bring a task and a quality bar. We’ll qualify a small cohort, calibrate against your rubric, and show you the data before you commit to scale.