Expert human data
built for frontier AI
Pasiflora AI connects AI companies with credentialed domain experts, physicians, attorneys, scientists, and engineers, to generate the high-quality training data that general crowdsourcing cannot produce.
Built for teams training specialist AI
General AI training data is widely available. Expert-level training data, the kind that requires real credentials and domain knowledge, is not. That is the gap Pasiflora AI fills.
AI Labs
Frontier model teams that need expert evaluation, preference data, and domain-specific generation to improve performance on specialized benchmarks.
Enterprise AI Teams
In-house AI teams building specialized models for healthcare, legal, finance, or scientific applications that require credentialed annotation.
Research Organizations
Academic and industry researchers building domain-specific datasets, benchmarks, or evaluation sets that require subject-matter expertise to construct.
Five task types. 431+ fields.
Every task is matched to experts whose credentials align with the domain. The task type determines the format and deliverable, the domain determines who does it.
Annotation
Structured labeling of clinical notes, legal documents, financial data, and research text by credentialed specialists, not general crowdworkers.
Evaluation
Expert rating of AI-generated outputs for accuracy, completeness, reasoning quality, and domain-specific correctness against structured rubrics.
Generation
Expert-authored explanations, case analyses, Q&A pairs, and summaries written from genuine domain knowledge, not rephrased from public sources.
Validation
Review of existing datasets for errors, inconsistencies, and mislabels that only a subject-matter expert would recognize, not detectable by automated checks.
Comparison
Side-by-side ranking of AI outputs with expert justification, the gold standard for RLHF and preference learning in specialized domains.
Expert Domains
Medicine & Clinical
Law & Legal
Physics & Science
Computer Science
Finance
Healthcare NLP
Life Sciences
Linguistics & NLP
Every expert is reviewed by a human
We do not use automated screening. A human reviews every application and makes the approval decision. Experts know they were selected, not auto-filtered, and that matters for how they show up.
Application & Credentials
Every applicant submits their degree, field of study, institution, years of experience, current role, and LinkedIn profile. We collect everything needed to verify the person behind the application.
Human Review
Applications are reviewed by our team, not filtered by an algorithm. A human evaluates credential quality, domain fit, and alignment with current client task needs before any approval.
48-Hour Decision
Applicants receive a decision within 48 hours. Approved experts move directly into Bloom onboarding. Rejected applicants receive a clear explanation.
Domain Matching
Approved experts are assigned expertise tags that determine which tasks they see. A cardiologist does not see contract review tasks. A patent attorney does not see clinical annotation tasks. Matching is credential-based, not keyword-based.
No expert touches a task before completing training
Every approved expert completes the Bloom training program before they are matched to any task. Bloom is not a quick orientation, it covers quality standards, task mechanics, policies, and the exact behaviors that produce reliable training data.
Bloom exists because credentialed experts are not automatically good AI data contributors. A physician who understands clinical medicine still needs to understand what "correct" means in the context of an annotation rubric, why consistency across a batch matters more than individual judgment, and why AI-assisted submissions undermine the entire value proposition. Bloom covers all of it.
Platform Orientation
How the portal works, task types, claiming, deadlines, and payment mechanics.
Quality Standards
What accuracy and consistency mean in AI training data. How rubrics work. Common failure modes.
Task Types in Depth
Annotation, evaluation, generation, validation, and comparison, each covered in detail with worked examples.
Policies & Confidentiality
NDA obligations, original work requirements, no-AI-generation policy, and escalation procedures.
Six layers, not one
Quality is enforced at every stage, before a task starts, during submission, and after delivery. No single control is sufficient; all six work together.
Structured Task Briefs
Every task includes a written objective, format requirements, worked examples, edge case guidance, and a scoring rubric. Ambiguity is eliminated before an expert starts, not corrected after they submit.
100% Submission Review
Every submission enters a review queue before it is accepted. Nothing is auto-approved. Reviewers evaluate work against the task rubric and provided examples using a side-by-side interface.
Revision & Feedback Loop
Submissions that don't meet the standard are returned with specific, actionable feedback. Experts revise and resubmit within the task window. Repeated issues trigger a quality score review.
Expert Quality Scores
Each expert carries a quality score that reflects their accuracy and consistency over time. Experts with declining scores receive reduced task access. High-performing experts are matched to higher-complexity, higher-value tasks.
No-AI-Generation Policy
Experts are explicitly trained that using AI tools to generate task submissions is a terms violation. Clients have quality controls specifically designed to detect non-human submissions. This policy is covered in Bloom onboarding and enforced.
Confidentiality & Data Security
All experts agree to an NDA covering task content at signup. They are trained that sharing, screenshotting, or discussing task materials outside the platform is grounds for immediate removal and potential legal action.
From first conversation to delivered data
We do not have a self-serve onboarding flow for enterprise clients. Every engagement starts with a direct conversation so we can scope it correctly from the start.
Initial Conversation
We start with a conversation to understand your data needs, domain requirements, task types, volume, timeline, and quality standards. No pitch deck, just a direct discussion.
Scoping & Pilot Design
We define the task structure, credential requirements, rubric, and deliverable format together. For new clients we run a scoped pilot before full deployment, so you can evaluate quality before committing to volume.
Expert Matching & Deployment
Tasks are built in the platform and matched to experts whose credentials align with your requirements. You specify the credential level, PhD, MD, JD, CFA, or other, and we match accordingly.
Delivery & Iteration
Completed, reviewed data is delivered in your specified format. We iterate on rubrics, edge cases, and examples based on your feedback until quality meets your standard consistently.
Pasiflora AI launched in April 2026. We are an early-stage company. We do not publish client counts, revenue figures, or data volume numbers because we cannot back them up yet. What we can tell you is that the process described on this page is real and operational, the vetting, the Bloom program, the review workflow, and the engagement model are all live. If you want to evaluate quality directly, we welcome a pilot. That is the only honest way to prove it.
Ready to talk about your data needs?
Send us a note with a brief description of your project, domain, task type, volume, and timeline. We will respond within one business day.