Marketing intelligence you can check.
Most marketing teams work from half-connected dashboards and confident opinions. AI Forensic Lab exists to give them one honest view of what is happening, why it matters, and what to do next, with the evidence attached.
Make the reasoning as visible as the number.
Marketing analytics is often either locked in a black box or scattered across tools that disagree with each other. Neither helps someone decide what to do on Monday.
We put first-party tracking, competitor monitoring, SEO and campaign checks, attribution and prioritised actions in one place, and we show how every score and finding was produced. If a result cannot be explained, it should not be trusted.
Fragmented data produces fragmented decisions.
Many tools, many definitions
Sessions, conversions and channels mean different things in different reports, so meetings turn into arguments about data.
Findings without follow-through
A chart or audit ends as a screenshot. Nobody owns the fix, and nobody checks whether it worked.
Confidence without evidence
Scores and recommendations often arrive with no visible reasoning, which makes them hard to correct.
Observe. Understand. Detect. Act. Measure.
Every module is built around the same five questions, so intelligence always ends in an action and a result.
- 01
Observe
What is happening?
First-party visitors, sessions, events, campaigns.
- 02
Understand
Why?
Funnels, journeys and attribution.
- 03
Detect
What changed or broke?
Baselines, competitor moves, checks.
- 04
Act
What next?
Ranked opportunities, actions, experiments.
- 05
Measure
What happened after?
Baselines and minimum samples.
What we hold ourselves to.
No fabricated data
If a metric is not available, the product shows an honest empty state rather than an invented number.
Explainable scores
Priorities are computed from named inputs, and the reasons are shown alongside the score.
Conservative claims
Impact measurement needs a baseline and a minimum sample, and avoids stating unsupported causality.
Estimates are labelled
Observed, modeled and estimated values are kept distinct.
Your data stays yours.
Tracking data belongs to the workspace that collected it. Per-site privacy settings control consent requirements, IP handling and retention, and consent is enforced where anonymous activity would be linked to a lead.
We do not claim formal certifications we do not hold. The methodology and trust page lists what we do, how fresh each module is, and where the limits are.
See what is moving your numbers.
Install one tracking snippet and start reading your own data. Or ask for a walkthrough first.