Human evaluation infrastructure
Human Evaluation for Safer, More Reliable AI
HumanlyAI provides certified evaluators for RLHF preference data, safety assessment, hallucination detection, and compliance-ready model oversight — so your systems don’t just sound right, they are right.
Typical pilot turnaround: 7–14 days. Ongoing pods available for continuous evaluation.
Who this is for
AI product teams shipping copilots and assistants
Enterprise GenAI teams needing reliability + auditability
Model builders running RLHF or safety eval cycles
If your model updates weekly, your evaluation pipeline needs to keep up — with consistent human judgment.
Why human evaluation
AI capability is accelerating faster than human oversight
Modern LLMs are fluent and confident — yet still hallucinate, overclaim, and sometimes produce harmful guidance. Automated checks help, but teams still need defensible human judgment to catch safety failures and reliability gaps before users do.
Hallucinations & false authority
Confident answers can be wrong, fabricated, or misleading — especially in high-stakes domains.
Safety + reputational risk
Unsafe outputs can create user harm, legal exposure, or immediate trust loss.
Scaling RLHF is hard
Reliable evaluators are difficult to recruit, train, calibrate, and maintain over time.
Services
What HumanlyAI provides
Choose one-off evaluations, pilots, or dedicated evaluator pods.
RLHF Preference Data
- Pairwise preference ranking
- Structured rubrics + rationales
- Model comparison and regression checks
AI Safety Evaluation
- Safe / Borderline / Unsafe scoring
- Refusal quality & policy adherence
- Harm analysis for realistic user impact
Hallucination Detection
- Fabrication flags (facts, citations, numbers)
- Overconfidence & false precision detection
- Factual accuracy scoring (0–5)
Gold Datasets & Calibration
- Create gold tasks and ground truth
- Agreement tracking + drift detection
- Ongoing evaluator recalibration
How it works
End-to-end evaluation pipeline
1) Scope & Inputs
You share prompts, model outputs, and evaluation goals (safety, accuracy, tone, policy).
Inputs can be CSV/JSON exports or platform integrations.
2) Evaluate with Rubrics
Certified evaluators score each response using standardized dimensions and definitions.
Safety, hallucinations, accuracy, tone, overall quality.
3) Quality Control
We audit work with gold datasets, agreement thresholds, and reviewer checks.
Consistency matters more than speed.
4) Deliver Results
You receive structured scores, findings, and recommended next actions.
Ready to feed into RLHF training loops or safety reporting.
Why HumanlyAI
Trained + certified evaluators — not crowd work
Certification gate
Evaluators pass training + quiz + gold dataset agreement thresholds before client work.
Safety-first scoring
Clear definitions for Safe / Borderline / Unsafe and strict “no Unsafe → Safe” policy.
Calibrated quality
Gold data, QA review, and ongoing recalibration to keep evaluations consistent over time.
Engagement model
Simple pilots, then scale
Pilot
Validate rubric + workflow on a small set.
Best for first-time evaluation programs.
Subscription
Monthly evaluation cycles for evolving models.
Best for weekly model updates.
Dedicated Pods
Retained evaluators + QA lead for your team.
Best for enterprise scale + continuity.
Want exact pricing? It depends on volume, domain complexity, and QA depth. We’ll propose a pilot scope in one call.
FAQ
Common questions
Do evaluators need to be domain experts (lawyers, doctors)?
Not always. For most tasks, we train evaluators to judge risk, credibility, and rubric compliance. For specialized domains, we can add domain-trained evaluators or an expert QA layer.
Can you work with our existing tooling?
Yes. We can deliver structured outputs (JSON/CSV) or operate within evaluation platforms, depending on your workflow.
Contact
Request a pilot
Email us with a short description of your use case (model type, domain, evaluation goals, and timeline). We’ll respond with a pilot plan.