AI Discovery Intelligence
Most AI visibility tools tell you whether you show up, and stop there. AI Discovery Intelligence goes further: the decision system that connects your share of AI recommendations - across ChatGPT, Claude, Gemini, and Perplexity - to your real traffic, leads, and costs: where you're losing ground, to whom, and the moves to win it back.
This is the intelligence, not a slide. Your AI Recommendation Score and the five dimensions behind it, who is winning the queries you are losing, and what to do about it - live, in the tools your team already uses. Representative view, anonymized demo data.
Benchmarked across the engines buyers actually ask
QUERY UNIVERSE
A category-sized query set across 4 engines, every response captured. Mapped to your intent classes - Direct Brand, Comparison, Discovery, Local, Purchase Intent, Competitive Displacement.
RETRIEVAL PROBES
Whether the engines can reach, render, and cite your content - the posture that decides if you are even eligible to be recommended. Most teams are blocked before the answer is written.
CITATION GRAPH
Which of your pages get cited, which get skipped, and which competitors sit ahead of you on the queries that matter. The deliverable your team builds against.
One method, run the same way every quarter - a stable baseline, and a clear read on what moved.
What the audit captures
Are you recommended?
Whether you come back as the answer, or anywhere at all.
Across which intents?
Discovery, comparison, local, purchase - where coverage holds or drops.
Who is cited above you?
The competitors the engines position ahead of you, per query.
Can engines reach you?
Whether your retrieval posture helps or blocks the engines.
What becomes part of your infra
Each layer builds on the one below and runs against the systems you already operate - AI-channel signal, CDP, paid feeds, analytics. Once live, it flags material moves in real time and refreshes monthly. Every claim is confidence-tagged and auditable back to its method.
Four engines in. Four layers. Three team-ready outputs.
Every engine response captured across your category - the measurement base every other layer feeds from. Reproducible from raw run data, so any number traces back to the response that produced it.
A per-URL citation graph across all four engines: which pages get cited, which get skipped, and where competitors sit ahead of you. The concrete targets engineering builds against.
The real AI-channel demand signal your JavaScript analytics can't see - genuine human-driven visits, separated out and joined into your existing stack. The feed is yours.
A quarterly, dollar-terms view of where AI demand can offset paid spend, by intent class and category - the line finance underwrites against. Treated as incremental, never one-for-one.
Engagement model
A one-time read to see where you stand, the full diagnostic on a cadence, or the System integrated into your stack. Scope is shaped to your category and the engines your buyers actually ask.
See where you stand. A fast, fixed-scope read of how AI recommends your category - and where you sit.
Best for
The full diagnostic, on a cadence. Depth and movement for a brand serious about winning its category.
Best for
Integrated into your stack. The full System, per property, with the layer your CFO reads.
Best for
Architecture
Between your first-party data feeds and your analytics layer. An outbound enrichment feed - no SDK, no dashboard, no vendor in your rendering path. The integration surface is the same as ingesting any data feed.
AEO is the execution layer - the content and page fixes that change what an engine retrieves. We're the measurement and decision layer above it: which moves actually grow your share, and what that share is worth in traffic and cost. Your team runs the AEO; we run the measurement that decides what's worth doing.
Owned operationally by Product and Data Science. Briefed to CMO. Surfaces to CFO at budget cycles.
Talk to us about your stackYour existing infra
Your first-party AI-channel signal · your CDP · Paid spend feeds (Google, Meta, programmatic) · Internal data science workflows
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28 Labs layer
AI Discovery Intelligence engine
Recommendation share · Citation graph · Demand read · Spend reallocation read · Reliability-tiered findings
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Your existing surfaces
Web analytics · Executive dashboards · CFO budget instruments · Engineering build targets
CISO-friendly: outbound enrichment feed, no SDK in rendering path, no new attack surface.
Why the value compounds for you
Not a one-off report. Run the same way each quarter, it becomes a trend line your teams plan against - what moved, what your moves changed, where to spend next.
A prioritised list of which pages to fix and what to change, mapped to the queries you can actually win. We re-measure the delta against your baseline each quarter.
Live human-prompted fetches, separated from training crawls and joined to your analytics - the AI-channel read JavaScript tools can't produce. The feed is yours.
Every claim is tagged HIGH / MED / LOW and traces back to its method. Nothing asserted without a confidence label.
A quarterly, dollar-terms view of where AI demand can offset paid spend, by category and intent. Treated as incremental, never one-for-one - the line finance underwrites against.
You keep what we build with you. The budget read, the demand feed, the build targets all live in your stack. We earn the next quarter by being useful in this one.
What it looks like operationally
Integration sits with Product and Data Science, briefs surface to CMO, the reallocation read lands at CFO. Each team gets what it needs in the tool it already uses.
Page specs, schema models, internal-linking and freshness patterns - at the URL level, mapped to where citation share is most winnable. You build; we re-measure the delta.
The real AI-channel demand, isolated and joined to your analytics - the feed is yours. Plus raw data they can re-run or feed their own models. An outbound feed they own.
Per-engine citation-share trajectory, the competitors cited above you, the moves shipping next, and the delta from last quarter's. No new dashboards.
A quarterly, dollar-terms view of where AI demand can offset paid spend, by category and intent - a defensible underwriting line. Incremental, never one-for-one.
Outbound enrichment feed. No SDK, no JS overlay, no vendor in the production codepath - the same surface as ingesting any data feed. Most security reviews already approve the pattern.
Training-data exposure. Verbatim user prompts. Non-clicked citations. Revenue attribution from logs alone (needs a CDP join). We name our limits before your data science team asks.
A 60-minute working session. We walk the architecture, what a sample audit looks like, and how the spend reallocation read would shape up for your category. No deck-and-pitch - we come in with a category read. Want to see the output first? See a sample audit.
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