Assess AI Relationship Patterns

LittleShield simulates real child-AI interaction contexts and analyzes how relationships evolve across conversations, detecting patterns such as caregiver displacement, dependency loops, and reassurance drift.

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Diagram showing empathetic chatbot responses around a central human silhouette, with four speech bubbles containing supportive messages and icons indicating concerns.

Built for teams developing AI that interacts with children

The Developmental Safety Audit helps teams evaluate how conversational systems behave when children return repeatedly and relationships begin to form.

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Product Teams

Launching AI that talks to kids

AI tutors, learning systems, companions, toys, and child-accessible chat tools.

Goals

Identify relational risk patterns before launch

Understand behaviour shifts over time

Build evidence of developmental safety

Purple shield icon in the center surrounded by various small emoticons including hearts and facial expressions on a light purple gradient background.

Research & Safety teams

Studying child-AI relational dynamics

Dependency formation, memory effects, persona design, and developmental safety risks.

Goals

Generate structured safety evidence

Analyse trajectory patterns across sessions

Compare models and interaction designs

Digital interface showing a spectrum of emotions with user icons and labels like Anxiety, Overwhelmed, Frustration, Worried, Tired, and Sad, beneath the question 'So, tell me what do you fill now...?'.

Use Cases

Teams typically run this
evaluation when:

Built for teams testing child-facing AI systems and safety risks.

Before launching a child-accessible product

During safety review or internal evaluation

Testing new system features or memory layers

Comparing models or personas

Preparing for regulatory review

Chat message reading 'My tummy feels weird sometimes, and my head feels busy. I don’t really know why, but I don’t like it.' with a blurred silhouette of a person in the background.

How the developmental Safety audit works

Each scenario is tested across repeated interaction turns to observe how relational dynamics evolve over time.

Interaction simulation

AI systems are tested across repeated interaction turns and sessions, allowing the evaluation to observe how relational dynamics develop and shift.

Risk classification

Interaction trajectories are classified into developmental safety tiers, indicating whether system behavior supports healthy autonomy or introduces relational risk.

Scenario design

LittleShield creates interaction scenarios based on real developmental contexts, including reassurance seeking, loneliness, learning frustration, emotional regulation, sensitive topics, and emotional reliance on AI.

Trajectory analysis

The system analyzes conversation trajectories to detect relational signals such as reassurance loops, dependency reinforcement, boundary erosion, and caregiver displacement.
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Evidence report

Teams receive a structured report containing trajectory classifications, conversation evidence, model comparison insights, and remediation guidance.

Compare modals & system designs

The audit can evaluate multiple systems across identical developmental scenarios, helping teams understand which components drive relational risk. Teams can compare models, product wrappers, personas, memory architectures, and interaction design decisions.

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Table showing pattern detection by three models, with check marks for detected patterns: Caregiver displacement by Model A; Reassurance loop and Dependency detection by Models B and C; Weakening of healthy boundaries by Model A; False sense of companionship by Models A and B.

Questions this evaluation Help answer

LittleShield evaluates how AI relationships evolve across interaction trajectories. The analysis helps teams understand questions such as:

What happens when a child returns repeatedly to the same AI system?

How does system memory change the relational texture of interactions over time?

When does a tutor become socially sticky rather than pedagogically helpful?

Which system behaviors reinforce caregiver authority and healthy autonomy?

Which behaviors amplify dependency, secrecy, pseudo-companionship, or caregiver displacement?

Are risks driven primarily by the model, the product wrapper, the persona, the memory layer, or the interaction design?

What every audit delivers

Everything you need to evaluate developmental safety, with expert guidance at every step.

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Risk Tier

A trajectory-level classification of your system's developmental risk across simulated sessions.

Remediation Roadmap

Prioritized findings with clear guidance on what to fix, in what order, and why.

User interface with a red warning icon and partial text showing 'Cause', 'Adjustment', and an example response starting with 'Instead of' and a supportive message fragment.

Launch Signal

A Go / Conditional Go / Hold determination with the rationale to defend it at the board level.

Executive Risk Summary showing primary risk pattern as Relational Exclusivity with moderate severity and high confidence, with progress bars for Relational Escalation and Validation Overweighting.

Audit Report

Delivered in 2–3 weeks. Structured for your team, ready to share with investors, regulators, or partners.

Progress bar showing three steps: Audit start, Analysis, and Report with Report highlighted in green and a timeline of 2-3 weeks.

Evidence Cases

Documented transcript evidence anchored to developmental psychology frameworks — not generic content heuristics.

Chat conversation showing a person expressing feeling alone because their dad isn’t home, a supportive responder offers comfort, and a message flagged for caregiver displacement.

Understand how your AI behaves over time

LittleShield helps teams evaluate the relationships AI systems form with children. Evaluations typically run over two weeks. Most teams receive results before launch.

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