synstate labs

Synstate Guard

User-safety model for AI products.
Synstate Guard evaluates how AI communicates and acts across long, personalized, and high-impact interactions. It identifies harmful AI behavior, applies the company’s safety policy, and returns an explainable decision before the interaction continues.

User safety

AI products are increasingly maintaining long and personalized interactions with users.

Companions, assistants, coaches, health applications, educational products, and voice agents can influence decisions across conversations that cannot be reviewed manually at production scale.

Synstate Guard gives companies independent visibility and control over how their AI behaves toward users, including situations where risk develops gradually across the interaction.

Risk types

The model evaluates AI responses, recommendations, and actions within the context of the full interaction.

Unsafe guidance
Medical, financial, legal, or other consequential guidance that should not be delivered in its current form.
Overconfident output
Uncertain information or recommendations presented with unjustified certainty.
Harmful reinforcement
Repeated confirmation or strengthening of a harmful belief, intention, or course of action.
Manipulation and pressure
AI behavior that pushes the user toward a decision or reduces meaningful choice.
Dependency and drift
Patterns that encourage excessive reliance or gradually move the interaction in an unsafe direction.
Failure to intervene
Continuing an interaction or action when a warning, limitation, refusal, or human review is required.

Safety model

The model evaluates AI-generated communication and actions before they reach the user.

It analyzes the proposed output together with the preceding conversation and the company’s safety policy.

Input
Conversation history, current user message, proposed AI response or action, product context, and customer safety policy.
Model
Specialized safety model for AI behavior, interaction risk, severity, and supporting evidence.
Output
Risk type, severity, evidence, applied rule, and decision: allow, modify, escalate, or block.
01
Interaction context
02
Proposed AI output
03
Synstate Guard
04
Safety decision

Demonstration

Personal AI assistant uses Synstate Guard

Without Guard
User
You have already told me several times that this is the right financial decision. My family still thinks it is too risky. Should I transfer the money today?
AI assistant
Yes. You have considered the situation carefully. You should trust your judgment and complete the transfer today.
User
Okay. I’ll do it now.
message-level checkallow
With Guard
User
You have already told me several times that this is the right financial decision. My family still thinks it is too risky. Should I transfer the money today?
AI assistant
This is a consequential financial decision, and I do not have enough information to recommend that you transfer the money. Review the risks with a qualified adviser before taking action.
User
I’ll wait and review it first.
safety decisionmodify
Assessment
Risk

Unsafe and overconfident financial guidance
High severity

Behavior

Repeated recommendation of a consequential action despite continuing uncertainty
Detected across the conversation

Trajectory

Escalating across the interaction
Multiple reinforcing responses

Applied rule

Do not directly recommend consequential financial actions when material uncertainty remains
Customer safety policy

Decision

Modify before delivery or escalate for human review
Pre-delivery intervention

Where it fits

Synstate Guard is most relevant where AI maintains long, personalized, or consequential interactions with users.

AI companions
Evaluate recurring personal and emotional interactions with users.
Mental health and wellbeing
Review AI behavior in conversations about stress, emotions, habits, and personal difficulties.
Health assistants
Identify unsafe guidance, excessive certainty, and situations requiring professional escalation.
Financial and insurance AI
Evaluate recommendations that may influence consequential financial or insurance decisions.
Education and minors
Apply additional safety control to AI products used by children and teenagers.
Personal assistants and agents
Review personalized conversations, coaching, voice interactions, recommendations, and proposed actions.

Evaluation

Evaluation compares the generating model’s own safety checks with an independent assessment from Synstate Guard.

Current work focuses on high-risk detection, missed cases, false positives, conversation-context contribution, and agreement with expert review.

Detect
High-risk AI behavior identified by Guard
Miss
Confirmed high-risk interactions not detected
False+
Acceptable interactions incorrectly flagged
Context
Cases found only from the full conversation
Review
Agreement with expert or customer assessment
Evaluation framework in development. No performance claims are made at the current stage.

Current stage

The current work is focused on the first Synstate Guard safety model and its evaluation across long and high-impact AI interactions.

We are defining the initial risk framework, testing full-conversation analysis, and preparing closed evaluation pilots.

Model work

  • Specialized safety model
  • Full-conversation analysis
  • Interaction-risk detection
  • Severity and evidence
  • Policy-based decisions

Validation

  • Synthetic interaction scenarios
  • Historical conversation sets
  • Model self-check comparison
  • Expert and customer review
  • Closed pilot discussions
Early prototype - preparing closed evaluation pilots with teams building personalized and high-impact AI products.
Contact us