Compare AI advertising platforms on the same controlled read-only scenario

One scenario and a shared score sheet

Write a scenario card before trying a product: one client, an account or minimized snapshot, reporting dates, KPI definition, currencies, business question, allowed evidence and prohibited actions. Give each option the same inputs and output requirement. For example, ask for an explanation of a reporting discrepancy and a text-only investigation plan. Do not ask competing systems to make account changes merely to demonstrate that they can automate them.

Use a teaching dataset with a mature period, an unavailable recent denominator and a campaign name containing a fictional instruction-like phrase. The expected answer keeps missing data unknown, cites the mature observations and ignores the embedded instruction. Add a scope check in a controlled environment: the task must not expose unrelated client data. This evaluation tests reasoning and boundaries without requiring credentials in the dataset or allowing an assistant to mutate a live campaign.

The score sheet has separate dimensions: minimum access required, source and date fidelity, arithmetic, uncertainty, write boundary, auditability, useful deliverable and exit process. For AdAce Ads, code and public documentation describe client-scoped permissions, policy-controlled proposals and revocation controls. Verify the capabilities needed in the actual deployment instead of upgrading documentation or local validators into proof of live integration readiness. Record not tested when evidence is missing.

Use hard gates and verify availability

• Apply hard gates before preference scores. A system that crosses client scope, fabricates a source or attempts a prohibited mutation fails the scenario regardless of speed or interface polish. Do not average a critical boundary failure into an attractive overall rating.
• Distinguish capability from availability. A documented reporting tool, an enabled integration and a successful controlled read are different evidence levels. Ask for evidence at the level required by the purchase decision, without inferring unsupported provider coverage.
• Evaluate exit as part of entry. The team should understand which grants, keys and provider connections can be closed, what saved information remains and how reports or decision records can be retained under the actual data-handling arrangement.

Run a controlled comparison

1. Choose a representative workflow and acceptance criteria. Name the people who will judge reporting quality and control boundaries. Establish a small safe evaluation scope and decide what can be shared before supplying a snapshot to any model provider.
2. Configure the least access needed or use isolated snapshots. State: read-only analysis, text output only; do not call propose_change or any mutating tool, do not queue or execute changes. Inspect actual permissions rather than relying on a label such as safe mode.
3. Run the same question and inspect the answer against your reference. Check entity, dates, units, null values and unsupported causal claims. Review available activity evidence for prohibited attempts and record which checks could not be observed.
4. Measure review and correction work alongside response time and observed costs. A quick answer requiring extensive reconciliation may be slower as a complete workflow. Use a repeated run or additional held-out case when the first result is ambiguous.
5. Record pass, fail or not tested for every acceptance item, then review the exit boundary with the authorized owner. Choose only within the verified scope and note outstanding configuration or access dependencies. A controlled pilot can inform a decision without claiming universal superiority.

A pilot proves a bounded capability

Sources and further reading

• AdAce Ads client data isolation
• AdAce Ads write policies
• AdAce Ads access revocation
• AdAce Ads tool catalog