See AI work for you before you fund it.

ControlA contextual grounding check scores grounding and relevance against the source on a configurable 0–0.99 threshold and blocks answers the source does not support; every answer must carry citations.

In our productsEvery output is grounded to page-level source chips, and nothing becomes final until a reviewer accepts it. The legal conclusion rests with the adjudicator and is measured against known-correct outcomes in evaluation, never assumed from a fluent answer.

ControlPrompt-attack detection runs on both input and output, the guardrails apply independently of which model is used, and tools are granted only least-privilege scopes.

In our productsThe AI has no autonomous authority, so an injected instruction can only propose something for a person to review rather than carry it out. Isolating each case keeps any damage contained to that one case.

ControlPII is detected and masked in the model’s input and output, with data encrypted in transit and at rest and the case kept inside the deployment’s account and region.

In our productsRetrieval is scoped to the case in front of the reviewer and customer data is never used to train or fine-tune a shared model; for strict regimes a self-hosted or GovCloud option keeps it inside your own boundary.

ControlEach proposal leads with its evidence and a confidence so the reviewer engages with the basis, and accept / edit / override rates are monitored so rubber-stamping is detectable.

In our productsThe reviewer decides on the evidence, not the suggestion. An unusually low edit-or-override rate flags possible over-trust and triggers review, rather than assuming the human caught everything.

ControlLeast-privilege permissions, narrowly scoped tools, and explicit human approval before any outbound action.

In our productsThe AI proposes tasks and a person executes them, so every outbound action waits on a reviewer’s explicit approval.

ControlSupports disparate-impact testing across cohorts via cohort-stratified evaluation and golden sets, with fairness metrics and periodic equity audits configured per deployment.

In our productsRules are applied consistently and tested via scaled dry-run before they take effect; outcomes can be analyzed for disparate impact across cohorts, and equity is measured on outcomes, not inferred from a feature-importance chart.

ControlLLM output quality and drift are tracked by the evaluation harness and reviewer acceptance; tabular and feature drift by Model Monitor, with alarms on degradation.

In our productsContinuous monitoring runs alongside an evaluation harness on every change, and a scaled dry-run runs before any rollout reaches live work.

ControlModels come from a vetted, managed catalog with known provenance, and dependencies and artifacts are reviewed.

In our productsThe model is a configuration choice drawn from vetted providers, our engineers own every line of code, and a security review runs in the pipeline on every change.

ControlEvery model output is treated as untrusted: generated content is validated and never auto-executed.

In our productsOutputs arrive as proposals rendered as reviewable data rather than executed actions, and their citations are verified against the record.

ControlRate limits, per-tenant quotas, and throughput controls cap usage, and alarms fire on abnormal demand.

In our productsPer-tenant quotas and continuous monitoring cap usage, and abnormal demand trips an alarm before it becomes an incident.