Edge case and stress-testing

Simulate high-surprisal events using synthetic data designed to reveal blind spots and rare but impactful behaviors.

Use information-theoretic models to identify and construct scenarios where model predictions are least certain or most disruptive.

Navigate edge cases with tools that highlight unexpected behaviors and counterfactual possibilities, especially where human oversight is critical.

Generate rich, privacy-safe synthetic data to explore cases that are rare, risky, or underrepresented in real-world datasets.

How it works

We apply principles from information theory to identify high-surprisal conditions: contexts where your model is likely to behave unpredictably or with low confidence. Our synthetic data engine then builds representative scenarios that let you probe those blind spots in a safe, controlled environment.

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Setting up this feature

Choose a domain and set parameters like surprisal thresholds or scenario types (e.g., regulatory noncompliance, anomalous user activity). The system synthesizes data and agent interactions that expose those edge conditions, ready for testing or guided exploration.

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Scaling with this feature

Scale from single-point anomalies to webs of interrelated edge conditions across agents and systems. As your models evolve, our surprisal-driven simulation engine helps you continuously uncover what you didn't know you didn’t know.

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Edge case and stress-testing

High-Surprisal Scenario Generation

Guided Exploration of Rare Events

Synthetic Data for Low-Frequency Risk

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High-fidelity simulations

Create risk-free, realistic environments that mirror complex real-world systems without exposing sensitive data

Answers

to your top questions

What do you do with my data?

At Simthetic, we're built on the principle that your data and IP are always yours.

Ownership & Control

No lock-in, no exploitation: Download your data, interactions, and ideas anytime.
Private by design: Built on open frameworks (like Llama) and deployable in your private cloud. No hidden data harvesting.
Ethical foundations: We don't sell, reuse, or monetize your work. Your insights remain your competitive edge.

Technical and operational safeguards

Zero-trust access: Interactions are isolated per session. Data is never stored centrally.
Encrypted by default: Format-preserving encryption ensures sensitive inputs are never processed in raw form.
Minimal and transparent: We only collect metadata essential for functionality (never PII). We also offer audit trails and user-defined retention policies.

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Why synthetic data helps

Less real data, more control: We use synthetic data to reduce reliance on real-user inputs, aligning with GDPR, HIPAA, and other regulatory standards.
Differential privacy: Aggregated insights include privacy-preserving noise to prevent re-identification.

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Open by design

Core components are open-source and auditable.
Our models run on transparent, Llama-based architectures. No black-box data traps.

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Privacy isn't a feature. It's foundational.

Is this secure for enterprise use?

Yes. Simthetic is designed from the ground up for enterprise-grade security, privacy, and compliance.

Security-first architecture

Zero-trust access: Interactions are isolated per session. Data is never stored centrally.
Encrypted by default: Format-preserving encryption ensures sensitive inputs are never processed in raw form.
On-premise or private cloud deployment: Run Simthetic in your own secure infrastructure. No vendor lock-in.

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Compliance-ready

Aligned with GDPR, HIPAA, and industry-specific standards.
Support for data lineage tracking, audit trails, and custom retention policies.
All models are built on open-source, auditable frameworks. No hidden data processing or opaque black-box behavior.

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Built for trust

No hidden analytics, tracking, or data resale.
Your inputs, simulations, and agent behaviors are yours. Always.
Transparent roadmap toward SOC 2, ISO 27001, and other enterprise compliance frameworks.

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Security isn't a checkbox. It's a foundational principle.

Can I integrate Simthetic into my current workflow?

Yes. Simthetic is designed to be integration-friendly and developer-accessible, whether you're running experiments in notebooks or deploying in enterprise pipelines.

Flexible access

Python SDK (coming soon): Use Simthetic directly from Jupyter or Google Colab to load simulations, interact with agents, and export results.
API-ready architecture: Integrate with your internal tools or automation scripts using simple HTTP endpoints.
CLI and batch-tooling (in development): Run large-scale simulations or regression tests on your own infrastructure.

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Works with what you use

Export datasets, agent responses, or scenario summaries to:

CSV, JSON, or Pandas
ML platforms like Vertex AI, SageMaker, or HuggingFace
Observability tools like Arize or WhyLabs

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Built to extend, not replace

Simthetic is designed to plug into your workflow, not overhaul it. Whether you're exploring edge cases, testing AI behavior, or validating regulatory scenarios, we integrate with your stack on your terms.

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Let us know which tools you use. We're building integrations based on real needs.

How are Simthetic's agents different?

Simthetic's agents aren't just text generators: they're contextual, goal-driven, and grounded in structured environments.

Behavior with purpose

Each agent is designed with clear motivations, constraints, and relational context to their simulated world. They behave like stakeholders, not chatbots.
Agents operate within dynamic environments (grounded in documents, personas, and even regulatory frameworks) that shape their decisions.

Structured, not scripted

Agents reason across scenarios, documents, and timelines, not just prompts.
Their behavior adapts as they explore simulations, learn from outcomes, and respond to rare events.
You can test how they act under pressure, uncertainty, or conflict, just like real-world personas.

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Transparent and tunable

Our agents are built on open frameworks like Llama so behavior is auditable and modifiable.
You can define traits, roles, or strategies, and even explore how agents disagree or collaborate.

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Simthetic agents don't just respond. They reveal.