AI Data & Model Intelligence
Human Feedback
Reinforcement learning from human feedback and related techniques depend on careful, consistent human judgment. We operate preference ranking, rewriting and demonstration programs with calibrated reviewers.
Challenges
What we help solve
- Reviewer disagreement on preference tasks
- Difficulty sourcing expert-level demonstrations
- Feedback that rewards style over substance
- Throughput limits on internal teams
Capabilities
Preference ranking and demonstration data for model alignment.
Preference ranking
Pairwise and multi-response ranking with justification capture.
Demonstrations
High-quality reference responses written by qualified contributors.
Response rewriting
Corrections and improvements to model outputs against defined criteria.
Reviewer calibration
Ongoing calibration sessions and agreement tracking.
Delivery approach
How the work runs
- 1Discover
- 2Define
- 3Design
- 4Build
- 5Validate
- 6Deliver
- 7Improve
Deliverables
What you receive
- Preference datasets
- Demonstration datasets
- Calibration and agreement reports