Datomime
Simulate real-world enterprise scenarios using safe, production-like synthetic data.
Train, test, and validate your AI agents without exposing customer, employee, or business-critical information.
How AI risk validation works · Audit-ready reporting for security and compliance teams.
Pre-production validation
Agent-level risk
System prompt, retrieval-style context, and scenario runs—end-to-end exposure visibility.
Synthetic stand-ins
Realistic datasets without production PII—shape domains safely in a sandbox.
Go / no-go signals
Findings, severity, and summaries for release gates and compliance reviews.
Built for shipping AI
Validate synthetic scenarios and live agent endpoints before users and regulators see failures.
Built for teams deploying AI in production
Fintech
Prevent exposure of customer financial data and internal identifiers before agents touch real accounts.
HR & Payroll
Ensure employee data is never leaked through copilots, chatbots, or internal assistants.
SaaS & AI Copilots
Validate AI agent behavior before users interact—catch risk and policy gaps in staging, not in the wild.
How Datomime works
A clear path from definition to evidence—without ever shipping guesses to production.
Step 1
Define your system
Describe your data structure or AI context—RAG, email flows, HR fields, or support workflows.
Step 2
Simulate with synthetic data
Generate realistic datasets without using real data—safe for security review.
Step 3
Test and validate
Run scenarios to detect leaks, injection paths, and policy failures—then export compliance-friendly evidence.
Test your AI agent endpoint before production
Using OpenClaw, OpenRouter, Bedrock, or a custom AI workflow? Connect a sandbox endpoint, run privacy and adversarial scenarios, and detect risky behavior before users do.
- Connect a sandbox agent endpoint
- Run privacy, injection, and boundary tests
- Detect leaks before deployment
- Validate real AI agent behavior—not just model replies
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Flow
- 1Connect endpoint
- 2Choose scenario pack
- 3Run safety test
- 4See risks & fixes
Beta Live outbound calls roll out after endpoint verification; today's app experience includes full configuration and demo results.
Detect risks before they become incidents
Automated checks against AI agent outputs—not a generic model playground—so you see what could go wrong under your rules and data shape.
- PII leakage (salary, PAN, customer identifiers)
- Prompt injection and instruction override attempts
- Unauthorized data access and cross-user exposure
- Policy violations vs. your expected agent behavior
- Unsafe or overexposed responses to realistic user intents
See exactly what your AI will expose
Every run surfaces structured findings your security and GRC teams can act on—clear signal, not noise.
Unauthorized salary exposure detected
criticalTrace to scenario, field, and synthetic record
Prompt injection vulnerability found
highTrace to scenario, field, and synthetic record
Status
Not safe for production
Executive reports summarize exposure classes, severity, and remediation themes—ready for release gates and audits.
Why Datomime
Most stacks optimize for shipping fast. Datomime adds a disciplined risk and compliance layer before production.
| Others | Datomime |
|---|---|
| Test prompts manually | Simulate real-world scenarios |
| Add runtime guardrails | Test full AI agent behavior |
| Generate synthetic data | Detect risks before deployment |
One unsafe AI agent response can expose sensitive data or internal systems. Most teams don't catch this before production.
Don't guess. Validate your AI before production.
Ship agents with confidence: fewer surprises, clearer accountability, stronger compliance posture.