Enterprise Questions, Answered

LSAS runs as an application-layer Layered Safety and Accuracy System between your applications and model providers, internal APIs, or system-of-record boundaries. It identifies runtime risk, evaluates claims, enforces evidence thresholds, applies deterministic validators where possible, and returns a decision with proof-oriented metadata.

No. LSAS includes auditability, but its purpose is broader: identify risk, enforce evidence thresholds, apply deterministic validators where possible, and control whether AI output is allowed, constrained, abstained, or escalated before trust is granted.

Proof of findings means the runtime can show why a decision was made - what claims were detected, what evidence was matched, what failed threshold, and why the system allowed, constrained, abstained, or escalated.

If configured evidence thresholds are not met, LSAS can prevent the output from being treated as trusted work. Depending on policy tier, it can constrain the response, flag uncertainty, redact unsupported content, abstain, or escalate to human review.

No. LSAS is a boundary-governance runtime. The Epic-backed sandbox is a proof point showing how LSAS governs ingress and egress around a healthcare system-of-record API boundary using FHIR/OAuth patterns. LSAS does not replace Epic and is not embedded inside Epic.

By default, LSAS stores derived telemetry only: decisions, risk scores, rule activity, and timing metadata. Raw content remains in your boundary unless your team explicitly enables additional logging for a controlled purpose.

Yes. LSAS is designed for regulated workloads by providing deterministic controls, explicit policy decisions, and auditable evidence trails. It supports your existing compliance program, rather than replacing legal or certification obligations.

Out of the box, LSAS includes validators and policy controls for PHI and PII, PCI-like patterns, security and secrets exposure, prompt-injection patterns, and accessibility-related copy risks.

No. LSAS is provider-agnostic at the runtime layer. It can sit in front of multiple model providers and model families so governance policy stays consistent even when your model mix changes.

Current runtime integrations cover OpenAI-compatible endpoints and Anthropic in this reference stack. Teams can extend provider adapters to support additional targets and enterprise routing patterns.

Yes, teams can integrate Bedrock through a provider adapter pattern so LSAS policy and telemetry stay consistent across providers. This is a common path for organizations standardizing on AWS infrastructure.

LSAS supports four deployment models: managed evaluation sandbox, private single-tenant deployment, customer-hosted deployment in your own cloud account/VPC, and true self-hosted/on-prem deployment. Customer-hosted cloud/VPC and on-prem are distinct options. Across all models, enforcement stays close to your runtime boundary while policy packs, evidence, and auditability remain first-class.

LSAS can govern AI behavior in legal and professional contexts by applying evidence thresholds, citation-aware validation, and review controls to higher-risk outputs. It is designed to support governed workflows, not replace legal or professional judgment.

LSAS models tenants, apps, memberships, and role-based access so teams can separate duties across security, compliance, and engineering while keeping a shared view of policy posture.

Typical rollout phases are: discovery and policy baseline selection, sandbox validation with guided scenarios, environment-specific tuning, and controlled production onboarding with evidence-pack and audit workflows.

Most teams see immediate value during sandbox evaluation by surfacing previously invisible risk patterns and standardizing decision telemetry. Time-to-production depends on boundary scope, workflow criticality, and governance requirements.

Policy packs are versioned, assignable per tenant, app, and environment, and tied to decision telemetry. This supports change review and incident reconstruction with clear evidence of what policy version was active at decision time.

Teams tune policy-pack weights and thresholds by environment, then review incident and decision telemetry to calibrate controls. The goal is high signal-to-noise while preserving strict controls for critical workflows.

Yes. The public sandbox includes an explicit, policy-constrained override workflow with structured request fields, approval simulation, explicit execution confirmation, and audit telemetry capture. Current scope is sandbox evaluation and tenant-console visibility of override execution events.

Pricing is aligned to deployment scope, tenant and environment footprint, and governance depth. The pricing page provides current package guidance and your team can map expected traffic and control requirements during discovery.

Enterprise engagements typically include tenant onboarding workflows, policy-pack governance controls, runtime telemetry and audit surfaces, and implementation support for regulated workloads.

Pilot-to-production usually follows gated milestones: sandbox signal quality, policy calibration, operational runbooks, and environment hardening. Teams promote from sandbox to production once controls and incident handling are validated.