Solutions

Customer support agents that cannot invent policy.

Let AI handle messy conversations while JacqOS blocks unauthorized promises, refunds, and escalations before they reach the customer.

Inspect Air Canada Refund Policy Read Trust

The failure mode

The model sounds helpful, invents a policy exception, and turns a bad answer into a real customer commitment.

Support leaders, AI product teams, and commerce operators. This is where buyer trust is won or lost: not in whether the model sounds smart, but in whether the system can stop the wrong action from becoming real.

Hallucinated policy

The assistant invents a refund path or compensation policy that does not exist.

Unauthorized concessions

The model offers a discount, refund, or escalation outside the authority you intended.

No defensible audit trail

When legal or support leadership asks why a promise was made, the team cannot reconstruct the path cleanly.

Containment

JacqOS keeps model output in proposal space until a domain decision relation ratifies it against real policy facts and named invariants.

The job here is structural containment, not best-effort prompting. JacqOS keeps AI output inside the right semantic relay until the ontology ratifies it.

Proposal, then decision

The LLM may draft a customer action, but only a domain decision relation may ratify it into a real intent.

Policy as observations and facts

Approved policies arrive as observations and derive facts the ontology can test directly.

Named invariants for last-mile defense

Even if a rule changes, the final transition is still checked against explicit policy invariants before execution.

What operators review

Review the boundary, not the generated code.

Blocked promises, refunds, and escalation attempts with the invariant or policy fact that refused them.
Golden fixtures for contradiction paths such as invented refund policies or absurd offers.
Replay receipts that show the exact observation, fact, and proposal chain behind any customer-facing action.

Rollout path

How teams usually adopt this pattern.

Start with one narrow policy surface

Contain a single refund, credit, or escalation workflow before expanding to broader support automation.

Use contradiction fixtures early

Add obviously bad cases first so blocked-action receipts become part of the buying story.

Graduate to higher-volume lanes

Once the boundary is trusted, move from assisted review into larger slices of ticket volume.

Proof surfaces

Show the buyer something concrete.

These are the proof surfaces that make this solution page credible: example walkthroughs, trust content, and the docs entry points behind both.

Proof surface Air Canada Refund Policy

A hallucinated refund policy never becomes a paid refund.

Explore → Proof surface Chevy Offer Containment

Absurd offers are blocked before anything customer-visible is sent.

Explore → Proof surface Trust

See the guarantees and limits behind blocked support actions.

LLM decision containment — hallucinated refund policies never become paid refunds

LLM decision containment — absurd offers never reach a customer

Explore →

Next step

Take customer support from pitch to proof.

Inspect the primary example, read the trust surface behind it, then decide whether the operating model fits the workflow you want to automate.

Inspect Air Canada Refund Policy How To Evaluate