AI-assisted Prebid optimization
Move beyond manual Prebid tuning.
Fairvisor uses AI to find and validate better auction policies across the traffic segments your team cannot tune by hand.
No tag. No account access. No change to your stack.
Get your public stack audit
A useful audit starts with evidence, not a generic score.
We load your live pages across browser environments and inspect the visible path from consent to auction to ad rendering.
Your report highlights the specific conditions where bidder participation, timeout behaviour, consent flow, or integration quality may be degrading auction outcomes.
Each finding includes the observed evidence, a likely mechanism, and what would need to be measured inside live auctions to verify it.
See what you will receive.
A Fairvisor Public Stack Audit is a concise technical report on the visible auction path of your live pages. It separates direct observations from opportunities that require live measurement.
It documents what we observed, where the risk appears, what it may affect, and what would need live-auction measurement to verify.
View a sample audit
Get your public stack audit
Enter your domain and work email. We will inspect your live pages and send you a report like the sample — based on your own public stack.
Fields:
From AI insight to verified change.
1. Observe — map the public auction path or collect shadow measurements.
2. Find — AI identifies segment-specific behaviour and candidate policies.
3. Validate — test one bounded change against a control.
4. Operate — apply only verified policies through managed Prebid execution.
From public evidence to managed execution.
Public Stack Audit
We inspect your live pages and deliver a PDF report. No integration required.
Shadow Measurement
If the audit identifies a credible opportunity, a passive tag measures real auctions without changing them.
Controlled Experiment
We test one reversible change against an unchanged control group.
Managed Prebid
Proven policies move into a Fairvisor-managed Prebid layer, gradually replacing your existing wrapper where it delivers value.
Start without changing your stack
1. Get a Public Stack Audit — identify visible issues and opportunities worth measuring.
2. Review the evidence — separate observed behaviour from hypotheses that require live data.
3. Measure before changing — install a passive shadow tag only where there is a credible opportunity.
4. Prove the change — use bounded experiments against a control before policy expansion.
AI turns evidence into inspectable decisions.
Static rules optimise the average. Fairvisor uses AI to analyse the long tail of auction behaviour across traffic conditions, bidder responses, timeouts, consent states, ad units, and configuration changes.
It turns raw browser and auction evidence into:
- ranked findings;
- plausible explanations;
- affected segments;
- proposed measurements and experiments;
- readable technical reports.
AI does not silently make broad production changes. Every active policy has a defined scope, a control group, measurable guardrails, and a rollback path.
Built to optimize the publisher’s side of the auction.
Fairvisor does not sell demand, resell inventory, operate an SSP, or ask you to move your buyer relationships into our platform.
We start by inspecting the stack you already have. If the evidence supports it, Fairvisor gradually takes over the Prebid execution layer and continuously tests how it should behave for your traffic.
GAM remains your final ad-serving and decision layer. Your SSP contracts, direct campaigns, and commercial controls remain yours.
Optimization should not be tied to who wins the auction.
A bidder, SSP, or managed monetization provider has its own commercial incentives. Fairvisor has no preferred demand source.
Our job is to identify which auction policy produces the best measured outcome for the publisher — by segment, under real traffic conditions, and with an unchanged control group. We have no commercial advantage when a particular buyer wins.
You do not need to hand over the stack on day one.
Start with a public audit. Move to passive measurement only when there is something worth verifying. Move to managed Prebid only when controlled experiments justify it.
Every step has a clear purpose, a bounded scope, and a way back.
The report is a core product deliverable and should be visually previewed on the site. A sample report should be freely viewable, rather than gated. The personal audit itself is requested in exchange for domain and work email. It includes observed issues, likely impact and evidence, external limits of the audit, measurement questions, and the recommended first shadow-measurement or experiment.
The report should contain:
- Executive summary — three to five areas worth investigating; never a generic health score.
- What we observed — pages, browser environments, time of tests, and trace evidence.
- Findings — each with observation, evidence, affected conditions, likely mechanism, confidence, external limits, and recommended next measurement.
- What this may mean — no invented euro-loss estimate; explain why the observation may warrant live measurement.
- Next step — a specific explanation of what Shadow Measurement would confirm or disprove.
Example report finding:
Bidder requests from mobile traffic in [segment] frequently exceed the apparent auction window. The public evidence is in section 2. To verify whether this affects live bid competition, Fairvisor would need to measure the relevant auctions.
The sample report must use fictional or fully anonymised data and state:
Sample report — illustrative data only.