Our commitment to responsible AI development, ethical principles, and transparent practices that prioritize human benefit, fairness, and accountability.
We build AI systems that are useful, safe, and fair by design. Our principles guide every decision in the development, deployment, and maintenance of our AI technologies.
Products must enhance human decision-making and public good; purely exploitative use is rejected.
Humans remain responsible for outcomes. Product teams retain decision logs and escalation paths.
Respect for personal and operational data, with minimal collection and maximized protection.
We test for disparate impact and mitigate harmful bias before release.
Encryption, access control, and least-privilege design are non-negotiable.
Clear documentation of model scope, limitations, and safe-use guidance.
We track and reduce environmental impact across training, inference, and infrastructure.
We use licensed, customer-provided, or publicly permissive datasets. Training sources undergo legal and ethical review.
Data is collected only for specific, disclosed purposes. Secondary use requires fresh assessment/consent.
Time-boxed retention tied to contractual or regulatory needs. Customer deletion requests honored consistent with law.
We transform data before model training wherever feasible to reduce identifiability.
Role-based access (RBAC), MFA, audit logs. Access is revoked upon role change.
Datasets and derived artifacts carry lineage metadata (who/when/why/transformations).
Customer data is logically isolated. No cross-tenant training without explicit, revocable consent.
We implement comprehensive bias detection and mitigation strategies throughout the AI development lifecycle, from data collection to model deployment and ongoing monitoring.
We evaluate datasets for coverage gaps and potential proxies for protected attributes.
Calibrate across groups (false positive/negative rates, calibration curves, subgroup AUROC/MAE, uplift parity).
Re-sampling, adversarial debiasing, counterfactual data augmentation, threshold adjustments, and human review gates.
Post-deployment drift and disparity checks trigger alerts and retraining tickets.
Each model card lists known failure modes, sensitive contexts, and 'do-not-use' scenarios.
For each production model we document purpose, inputs, outputs, training data types, metrics, and caveats.
Where meaningful, we expose saliency/feature importance, decision traces, or exemplar retrievals.
User-facing summaries clarify what the model considered and its confidence where appropriate.
Versioned datasets, code, and weights; deterministic seeds for eval runs; signed artifacts.
We implement multiple layers of safety controls to prevent misuse and ensure responsible deployment of our AI systems in real-world applications.
Safety Policies: Disallow surveillance misuse, discrimination, and unlawful activities; enforced by policy filters.
Red-Team Exercises: Internal and external adversarial testing (prompt injection, data exfiltration, geospatial misuse).
Guardrails: Prompt/response filtering, rate limits, anomaly detection, geo-fencing (where applicable), and tiered access.
Human-in-the-Loop: Mandatory human review in high-risk operations; override and rollback mechanisms are standard.
TLS 1.3 in transit; AES-256 at rest.
CIS benchmarks, container isolation, signed images, and secrets management with rotation.
SIEM, IDS/IPS, WAF, DDoS protection, and continuous vulnerability scans.
Device posture checks, short-lived credentials, and just-in-time access.
Annual pentests; SOC 2/ISO-aligned controls where applicable.
We actively measure and minimize the environmental impact of our AI systems throughout their lifecycle, from training to deployment and inference.
Track training/inference energy and estimated CO₂e.
Mixed precision, quantization, efficient architectures, batch scheduling, and renewable-powered regions where possible.
Archive/retire models that do not justify their footprint; publish efficiency improvements in release notes.
We employ comprehensive evaluation frameworks to ensure our AI systems meet high standards for accuracy, fairness, robustness, and usability before deployment.
Models must meet or exceed baseline metrics, bias thresholds, safety tests, and latency SLOs before being approved for production deployment.
We provide customers with comprehensive tools and controls to manage their data, understand AI decisions, and maintain compliance with their organizational policies.
Per-tenant policy configuration (allowed actions, export controls, redaction defaults).
Customers can enable richer explanations and decision traces (where supported).
APIs/UI for data export, retention windows, and deletion requests.
Per-tenant logs for inputs, outputs (hashed/redacted where required), and admin actions.
Clear, contract-level controls to exclude customer data from any cross-tenant training.
We maintain comprehensive incident response capabilities with defined escalation procedures and transparent communication protocols.
Defined severities, roles, and escalation timelines; 24/7 on-call rotation.
We notify affected customers and regulators within legally required timeframes.
Blameless RCAs, corrective actions, and customer summaries for material events.
Responsible disclosure program; safe harbor for good-faith researchers.
PIPEDA (Canada), GDPR (EU), and applicable US state laws.
ISO 27001/SOC 2-aligned controls; PCI DSS for payment processors.
Monitoring evolving frameworks (EU AI Act, NIST AI RMF), updating controls accordingly.
Regional hosting options and SCCs for cross-border transfers where required.
Public artifacts to help users evaluate our systems:
Seismic AI Technologies Inc., Toronto, Ontario, Canada
For Turbo AI deployments, contact support@turbo-ai.ca
Brand-specific artifacts (model cards, disclosures) are available per customer contract.