Models learn from people. Ours are experts.
Every capable AI system is built on human judgment - labeled data, careful evaluation, and expert feedback. Pansolve supplies trained, domain-fluent human teams for the work that makes models genuinely better.
Labels a model can actually learn from.
What it is
High-quality annotation across text, documents, images, and structured data - classification, entity tagging, transcription review, and complex domain labeling.
Why it matters
Noisy labels put a hard ceiling on model performance. The difficult 10% of examples - the ambiguous, the edge cases - are exactly where cheap labeling fails and where model quality is decided.
What our team does
- Builds and refines labeling guidelines with your team before scaling up
- Routes ambiguous and edge-case items to senior reviewers instead of guessing
- Runs multi-pass review with measured inter-annotator agreement
- Reports quality metrics openly - you see our error rate, not just our throughput
A client's previous vendor labeled clinical notes at speed:
"looks done" 9% of labels wrong
Our audit of a 500-item sample found systematic errors on negated conditions ("no evidence of..."). We rebuilt the guidelines and re-labeled the affected slices.
Model accuracy improved on the next fine-tune.
Someone has to check the model's homework.
What it is
Structured human evaluation of model outputs: accuracy, helpfulness, safety, and fitness for your use case - with rubrics, calibrated raters, and reproducible scoring.
Why it matters
Benchmarks don't measure your use case. Before a model touches customers, someone with judgment needs to read what it produces and say, credibly, whether it's good enough.
What our team does
- Designs evaluation rubrics tied to your real quality bar
- Calibrates raters against gold examples before scoring begins
- Scores outputs side-by-side across models, prompts, or versions
- Flags failure patterns - hallucination, tone, missed instructions - with examples
A model's answer read as fluent, confident, and well-cited.
The cited study didn't exist.
A calibrated evaluator checked the reference - automated metrics had scored the answer highly.
Hallucination pattern documented before launch, not after.
Teaching models what better means.
What it is
Human preference data for fine-tuning and alignment: ranking model responses, writing demonstrations, and giving structured feedback that steers model behavior.
Why it matters
Preference data is only as good as the judgment behind it. Careless rankings teach a model to sound right; thoughtful ones teach it to be right.
What our team does
- Ranks and compares model responses against clear, documented criteria
- Writes high-quality demonstration responses for supervised fine-tuning
- Provides granular feedback - what failed, why, and what better looks like
- Maintains consistency across raters with ongoing calibration sessions
Two model responses to the same customer question:
A: fast, polished, slightly wrong. B: plain, correct.
Untrained raters preferred A. Our calibrated raters chose B and documented why - correctness outranks polish in the client's rubric.
The model learned the right lesson.
When the subject is serious, so are our reviewers.
What it is
Specialist review of AI outputs in high-stakes domains - healthcare documentation, medical coding, finance, and accounting - by people credentialed in those fields.
Why it matters
General-purpose raters can't judge whether a clinical summary is safe or a financial answer is compliant. Domain accuracy requires domain experts, and we already employ them across our other practices.
What our team does
- Reviews AI-generated medical coding and clinical documentation for accuracy
- Verifies finance and accounting outputs against professional standards
- Creates domain-specific gold datasets for training and evaluation
- Feeds recurring error patterns back to your ML team with worked examples
An AI tool drafted a patient encounter summary. Our certified coder reviewed it:
"hypertension, controlled" not in the chart
The condition appeared in an old note but not this encounter - a subtle, high-risk error only a trained coder would catch.
Corrected before it entered the record.
The human layer, built for AI teams.
Experts, not crowds
Trained, managed teams with domain credentials - not an anonymous crowd platform optimizing for speed.
Transparent quality
Inter-annotator agreement, audited error rates, and honest reporting. You always know what you're getting.
Secure by design
NDA-bound staff, access controls, and data-handling practices sized for sensitive datasets.
Pilot a batch with us.
Send a small labeling or evaluation task. We'll return it with quality metrics and our honest read on your guidelines - then you decide whether to scale.
Book a consultation or write to support@pansolve.com