Trust & Safety
At Gridspace, we engineer trustworthy agentic voice systems that serve regulated industries and their customers. Accordingly, we don’t just consider the safety of models, we take a systems approach to AI safety and alignment. We partner equally with MRM and product teams to deliver complete, reliable solutions that exceed compliance requirements and customer expectations.
Gridspace’s architecture divides its language models into specialized subsystems. This compartmentalized design allows for dynamic responses within clearly defined, organizationally approved boundaries, ensuring both safety and conversational flexibility.
Fast identification and resolution of issues, such as deviations from expected phrasing, data or tool actions, are crucial. By managing each response layer independently, Gridspace reduces latency, lowers operational costs, and minimizes inappropriate or irrelevant outputs.
Gridspace offers continuous auditing for transparency and control. Operators can review interactions to ensure performance meets standards and adjust response guidelines to improve quality and compliance. The entire platform is HITRUST i1, PCI DSS v4 compliant, SOC 2 type 2 compliant, GDPR compliant and exceeds the standard of HIPAA. Watch our Opus Research interview on Trust & Safety with Amy Stapleton.
Fast identification and resolution of issues, such as deviations from expected phrasing, data or tool actions, are crucial. By managing each response layer independently, Gridspace reduces latency, lowers operational costs, and minimizes inappropriate or irrelevant outputs.
Gridspace offers continuous auditing for transparency and control. Operators can review interactions to ensure performance meets standards and adjust response guidelines to improve quality and compliance. The entire platform is HITRUST i1, PCI DSS v4 compliant, SOC 2 type 2 compliant, GDPR compliant and exceeds the standard of HIPAA. Watch our Opus Research interview on Trust & Safety with Amy Stapleton.
AI Safety & Trust Guiding Principles
TRADITIONAL MODEL VALIDATION Validate machine learning models with separate training, test, and validation sets. Compare the final validation against a product-defined performance requirement.
MODEL ISOLATION Don't expose ML models directly to end customers and business analysts. Instead, embed them within larger systems featuring heuristics, guardrails, and state machines to limit individual model impact. Direct prompting of language models is limited to developers.
OUTPUT VALIDATION Independent systems should validate outputs, regardless of model type, to manage output errors and hallucinations.
MODEL INTERPRETABILITY Models must reference indexed documents, coaching sessions, or intermediate states as "breadcrumbs" to aid validation, monitoring, and iteration.
DATA PREPARATION Training datasets should closely mimic real-world interactions—think realistic, unbiased, and professional dialog. This means leveraging in-domain data whenever possible.
INTERATION & CONTROL Systems should offer iterative mechanisms, such as model improvements, coaching, or authoring tools, to enable agent designers to correct unexpected behaviors.
RELIABILITY ENGINEERING Rigorous reliability engineering—including empirical validation, testing, redundancy, and failsafes—maximizes autonomous system safety.
TRADITIONAL MODEL VALIDATION Validate machine learning models with separate training, test, and validation sets. Compare the final validation against a product-defined performance requirement.
MODEL ISOLATION Don't expose ML models directly to end customers and business analysts. Instead, embed them within larger systems featuring heuristics, guardrails, and state machines to limit individual model impact. Direct prompting of language models is limited to developers.
OUTPUT VALIDATION Independent systems should validate outputs, regardless of model type, to manage output errors and hallucinations.
MODEL INTERPRETABILITY Models must reference indexed documents, coaching sessions, or intermediate states as "breadcrumbs" to aid validation, monitoring, and iteration.
DATA PREPARATION Training datasets should closely mimic real-world interactions—think realistic, unbiased, and professional dialog. This means leveraging in-domain data whenever possible.
INTERATION & CONTROL Systems should offer iterative mechanisms, such as model improvements, coaching, or authoring tools, to enable agent designers to correct unexpected behaviors.
RELIABILITY ENGINEERING Rigorous reliability engineering—including empirical validation, testing, redundancy, and failsafes—maximizes autonomous system safety.