High-fidelity simulations

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

Simulate individuals, teams, or systems that behave in believable, context-sensitive ways.

Generate rich, dynamic settings (markets, organizations, and networks) that respond to agent actions and evolve over time.

Achieve realism without using real data. Synthetic simulations replicate complexity and nuance while remaining compliant and risk-free.

How it works

We generate synthetic data environments that replicate real-world systems with high fidelity but without using real user data. These simulations preserve statistical patterns and behavioral complexity to reflect the scenarios your AI must navigate.

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Setting up this feature

You define the system parameters (such as user personas, workflows, or edge conditions) and our platform handles the rest. You get a ready-to-run simulation environment tailored to your domain.

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Scaling with this feature

As your system evolves, simulations can grow in scope and complexity: from simple test cases to full-scale environments simulating thousands of agents. Whether you're validating a prototype or deploying enterprise-scale AI, our synthetic worlds scale with you.

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Get started with

High-fidelity simulations

Behaviorally Accurate Agent Modeling

Environment & Context Simulation

Privacy-Preserving Fidelity

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Interactive Docent Agent

A context-aware AI guide that helps you navigate, interpret, and learn from complex synthetic environments.

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.