Paid traffic integrity

Evidence-led ad fraud detection for paid traffic

Separate useful customer activity from suspicious automation, connect media events to first-party outcomes and act with a documented level of confidence.

Request a traffic audit Scope a pilot

First-party evidence
No assumed fraud rate
Explicit coverage and limits

Analysis active

ILLUSTRATIVE VIEW

unusual patternsession review

Likely humanconsistent behaviour

Reviewrisk signals

Automationresponse policy

Protect decisions across

Paid search
Paid social
Display
Affiliate
CRM
Analytics

The business problem

A click can look valid.
The outcome may say otherwise.

Traffic quality is not only a security question. It changes media cost, optimisation signals, lead operations and the evidence available when a source is challenged.

Budget without demand

Paid events consume budget without producing a comparable customer or business outcome.

Protect paid traffic

Distorted optimisation

Low-value clicks and conversions can enter attribution, audiences, scoring and automated bidding.

Review ad fraud bot detection signals

Evidence gaps

Media, analytics and CRM records do not align well enough to reproduce a suspicious pattern.

Build a diagnosis

From signal to business impact

Protect the decision chain, not only the landing page

A useful assessment follows traffic from campaign context through the first-party session to a qualified outcome. The response is selected after the evidence and false-positive cost are understood.

Media — source and placement quality in context
Analytics — suspect events separated from decision data
Sales — lead quality connected to acquisition signals
Evidence — reviewable records with stated limitations

How it works

Measure, connect, classify, respond

Start with one decision and a bounded dataset. Expand coverage only when the initial evidence is useful.

Define the question

Choose the campaign, source or outcome that needs a decision.

Collect relevant signals

Align media context with first-party request and session evidence.

Connect the outcome

Compare suspicious activity with qualified leads, orders or another value event.

Choose a response

Observe, exclude, rate-limit, challenge or block according to confidence and risk.

No pre-set fraud percentage. The scope, input data, exclusions and interpretation boundaries are agreed before a recommendation is made.

Explore the service

Commercial intent.
Practical education.

The service page explains the audit and pilot. The guides help your team distinguish invalid traffic, automation and evidence of fraud before deciding what to block.

Commercial service

Ad fraud protection

Review paid traffic, connect it to outcomes and design a proportionate control plan.

Ad fraud fundamentals

What Is Ad Fraud? Types, Warning Signs and Detection

Learn how ad fraud differs from invalid or low-quality traffic, which warning signs matter, and how to build an evidence-led detection process.

Bot traffic diagnostics

Ad Fraud Bot Detection: Signals, Limits and Response

Learn how ad fraud bot detection combines campaign, network, browser, behaviour and outcome signals, including the limits of IP and user-agent rules.

Honest proof

Evidence before claims

We do not publish invented detection rates, guaranteed savings or client results. A useful result is tied to the measured scope and the decisions it can support.

Defined coverage

The report states which traffic, events and outcomes were observed, and which were unavailable.

Reviewable classifications

Signals, rules and confidence are recorded so representative cases can be checked.

Proportionate action

Controls reflect the cost of abuse and the risk of blocking a legitimate customer.

FAQ

Before you assess paid traffic

Clear boundaries between bot detection, invalid traffic and a conclusion about fraud.

Is every bot visit ad fraud?

No. Search crawlers, monitoring tools and authorised automation can be legitimate. Ad fraud assessment requires campaign context, evidence of manipulation and a connection to an advertising or business outcome.

Do you need to block traffic to audit it?

No. A pilot can begin in observation mode. Tagging and classification preserve a comparison group before stronger controls such as rate limits, challenges or blocks are considered.

What data is useful for an ad fraud audit?

The minimum useful set depends on the question. It often includes campaign or placement context, first-party request and session events, and a downstream outcome such as a qualified lead or accepted order.

Does an audit prove who operated suspicious traffic?

Not necessarily. Technical evidence can justify an operational response without identifying the actor or proving legal intent. Reports should distinguish risk classification from a legal finding.

Does this replace platform invalid-traffic filtering?

No. Platform filtering and first-party analysis answer different questions. An advertiser may still need to understand lead quality, attribution and the effect of traffic on its own systems.

Start with evidence

Find out what your paid traffic is really producing

Start with a bounded audit on real campaign and first-party data. The scope and interpretation limits are agreed before measurement begins.