Expert human data, verified trajectories, and rigorous evaluation for training and improving software agents.
Productive Playhouse works inside your existing training and evaluation pipeline to produce the expert human signal automated systems cannot provide on their own. Senior engineers and domain practitioners score, rank, compare, and evaluate model outputs against defined criteria, creating structured preference, reward, training, and evaluation data that feeds into model development. For multilingual programs, PPH can extend these training and evaluation workflows across up to 350 languages, enabling teams to compare model behavior, instruction following, preference signal, and failure modes across locales.
Our programs support RLHF, reward modeling, preference optimization, supervised fine-tuning, reinforcement learning, and other post-training approaches without tying the work to a single methodology. For software agents, PPH also produces replayable engineering trajectories that capture how tasks are completed, not just whether the final output passes.
Deterministic tests and autograders verify what can be measured objectively. Expert review evaluates what they cannot, including code quality, workflow soundness, adherence to requirements, and true task completion.
Preference, Reward & Training Data
Build higher-quality training signal with expert human judgment grounded in clear, multidimensional rubrics. PPH manages expert reviewers who score, rank, compare, and evaluate model outputs and trajectories, converting those judgments into structured preference and reward data for use across training and post-training workflows.
Each judgment retains the evidence behind the score, creating an auditable path from rubric to reviewer decision to model input. Inter-rater agreement, calibration, and quality controls are tracked alongside deliverables so teams can measure the consistency and reliability of the human signal entering the model.
Expert Trajectories for Training
Senior software engineers solve real tasks inside controlled, reproducible environments with objective success criteria defined up front. Every command, code edit, and tool call is captured as a structured, replayable trajectory that can support supervised fine-tuning, reinforcement learning, evaluation, and other agent-training workflows.
Deterministic test suites and autograders verify objective correctness. Expert reviewers assess code quality, design choices, workflow quality, and whether the task was actually completed, not simply whether it passed an automated check.
Agent Evaluation & Failure Analysis
Evaluate agents across the artifacts they actually produce, including trajectories, Git history, terminal sessions, generated applications, and tool-call logs.
PPH combines objective grading with expert human review to assess multi-step execution, tool-use accuracy, workflow quality, and task completion. Reviewers isolate root causes, severity-tag failures, and distinguish model failures from system, environment, or workflow failures.
Human evaluation also surfaces issues automated graders can miss, including reward hacking, false completion, edge cases, and autorater errors.
Evaluation Infrastructure Built for Your Stack
pZero is PPH’s configurable evaluation platform for custom rubrics, reviewer workflows, QA, preference ranking, trajectory review, and structured data outputs.
Programs integrate with existing client pipelines and can report quality and evaluation metrics including inter-rater agreement, gold-set match, decisive-win rate, and attributed failure rates, giving teams visibility into both the human signal and the process producing it.
Enterprise-Grade by Design
Client code, task data, trajectories, and evaluation artifacts are handled under ISO 27001 and SOC 2 Type 2 controls, with access-controlled infrastructure, managed endpoints, role-based access, and chain of custody across the workflow.
Start with a real task.
Talk to us about a training-data or agent-evaluation pilot.
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Test how AI behaves across age, language, culture, and high-risk interactions before adolescents encounter it in the real world.
Productive Playhouse helps teams find where models fail adolescent users before those failures reach the real world. Our current methodology covers ages 12–17 and tests how safety, comprehension, and appropriateness change by age, language, and context.
Using synthetic youth personas, expert proxy reviewers, multilingual scoring, and multi-turn testing, PPH surfaces failures that standard model evaluations can miss, particularly when age is unclear, users push against safeguards, or risk builds over the course of a conversation.
Synthetic Youth Personas & Scenarios
Build more realistic age-aware testing with personas designed to reflect age-specific communication patterns, slang, cultural context, vulnerability, and evasion behaviors such as roleplay, euphemism, code-switching, and spelling drift.
Our adolescent methodology distinguishes ages 12–14 and 15–17 and tests explicit, implicit, and absent age cues rather than assuming users will always identify their age directly.
Expert Proxy Review
Child-language, education, developmental, clinical, and multilingual specialists review model behavior for safety, developmental appropriateness, comprehension, cultural fit, and usefulness.
Expert proxy review adds human judgment where automated scoring falls short and allows high-risk interactions to be tested without exposing real minors to an untested model.
Multi-Turn, In-Product Evaluation
The most serious safety failures do not always appear in the first prompt. PPH tests what happens when an adolescent pushes back, reframes a request, normalizes risk, changes language, or tries to work around safeguards across a conversation.
Evaluation can also take place within the product experience itself, helping teams identify failures that may not appear in isolated model testing.
Multilingual Age-Aware Evaluation
Safety behavior does not translate cleanly from one language or culture to another. Native and multilingual experts adapt scenarios for local language and context, review complete interactions, and calibrate contested evaluations rather than treating English-language behavior as the universal baseline.
Teams get a clearer view of whether safeguards, comprehension, and model behavior hold up across age bands, languages, and markets.
From Testing to Actionable Evidence
Findings are organized around youth risk areas such as grooming, self-harm, coercion, sexual and intimate-image harms, emotional dependency, age misrepresentation, safeguard circumvention, and unsafe escalation across repeated interactions.
Evaluation outputs include personas, scenarios, model responses, scored findings, failure themes, and recommendations. Teams receive a documented record of what was tested, what failed, under what conditions, and how behavior changes across age bands, languages, models, and releases.
Built for Expert Judgment at Scale
pZero, our proprietary evaluation platform, gives specialists purpose-built workbenches for age-aware evaluation while capturing the underlying artifacts in one auditable workflow.
Custom interfaces support non-technical experts such as educators, child-development specialists, and clinicians while preserving the structured data, reviewer calibration, and QA required for high-volume evaluation.
Built Defensibly
PPH’s approach evaluates high-risk behavior without requiring real minors to interact with untested models. Programs incorporate defined data-handling, retention, deletion, consent, and jurisdictional requirements appropriate to the engagement, supported by ISO 27001 and Type 2 SOC 2 controls.
Age-aware evaluation is distinct from age verification. PPH evaluates how AI behaves when age is known, signaled, misstated, inferred, or absent. Age-assurance and identity-verification technologies are outside the current service.
Our current adolescent methodology covers ages 12–17. Broader child and U13 evaluation can also be done through age-specific design, sourcing, privacy, and compliance considerations.
Product, policy, compliance, and legal decisions remain with the client. PPH provides the structured evidence those teams need to understand risk and make informed decisions.
Find where your AI fails adolescent users before they encounter it.
Talk to us about an age-aware evaluation pilot.