For Marketplaces & Enterprise
Across ChatGPT, Claude, Gemini, and Perplexity. Per-query, per-engine, per-landing-URL, per-intent-class breakdown of AI engine retrieval behavior - joined to your first-party server logs, your paid CPL data, and your CDP.
We extend your analytics stack. We don't displace it. No new platform required. Your team gets the dashboards and the quarterly refresh.
Standard scope. Configurable for vertical depth.
Built for
28 Labs Enterprise is scoped for organizations with multi-hundred-million-dollar P&Ls, internal engineering and data science teams, mature analytics stacks, and partnership-exposed revenue worth defending. The audit substrate, the integration model, and the deliverable shape are tuned for that buyer.
Auto, real estate, jobs, travel, financial services - any multi-sided marketplace where AI engines now compose category answers (lender lists, vehicle valuations, neighborhood comparisons, partner shortlists) that route demand around your partnership corridors.
Companies where AI is becoming a measurable referral source, Adobe RT-CDP or equivalent is already in production, and paid CPL is being scrutinised by finance teams that need a reallocation case backed by query-level evidence.
Where exclusive financing, distribution, or fulfilment partnerships represent a high-value commercial asset, and the offline corridor doesn't reproduce on AI surfaces - because AI engines compose competitor lists side-by-side rather than honour the exclusivity.
Where every recommendation needs to land at CPO / CTO / CDO level, be forwardable to the board, and survive scrutiny from internal data science teams. We brief at that bar.
The method
Each layer extends the one below it. The audit substrate is delivered in every engagement. The three intelligence layers above it are scoped to client need - and operate against existing client infrastructure.
The measurement substrate. Standard scope 65 buyer queries x 4 engines = 260 AI responses captured. Architecture probes and bot-behavior modeling layered on top.
robots.txt, rendering posture, CDN geo-policyPer-URL citation share across every retrieved AI engine. Where competitors are cited above you. Where the answer-engine format pattern is already working inside your own portfolio - and where it isn't.
We integrate your server-log feed, classify AI bot traffic, and surface live AI-channel demand signal. This is substrate Adobe Analytics, Target, and Audience Manager structurally cannot produce - the bots don't execute the JavaScript those tools depend on.
ChatGPT-User, Claude-User, Perplexity-User fetches = real human demandWe ingest your paid spend feeds and CDP conversion data, classify the query universe at the intent-class level against the AI demand we measure in the audit substrate, and produce a per-category reallocation matrix in dollar terms.
Methodology discipline
No mystery numbers, no hand-waving. Internal data science teams should be able to audit any cited figure back to its method. The reliability framework is shown on slide 1 of every executive review.
HIGH
Captured from the live AI engine response or measured against a verifiable artifact: a curl probe, a published bot IP range, a robots.txt directive, a citation in the actual AI output. Reproducible.
MED
Derived via a documented statistical method (intent attribution from log patterns, format-pattern clustering, cross-engine consensus modeling). Methodology disclosed; honest about the inference layer.
LOW
Estimated against published industry signal, not yet joined to your specific data. Marked LOW explicitly so the joined substitution-matrix output (with your CPL + CDP feed) is the trusted version, not the placeholder.
Integration posture
No new analytics platform. No vendor lock-in. No "you need our SaaS" pressure. The intelligence layers operate as a managed service against existing client infrastructure.
Existing access logs (CloudFront / nginx / Akamai / Fastly). We classify AI bot traffic against published IP ranges and turn raw logs into three actionable intelligence streams. No new instrumentation.
The Substitution Matrix joins to your existing CDP. We don't replace it. We classify the query universe at the intent-class level and produce reallocation candidates against your real conversion data.
Whatever paid platform your team or agency runs (Google, Meta, programmatic). We ingest the spend feed and the CPL data, the agency continues executing. We operate the measurement layer.
AI referral attribution lands in your Adobe Analytics so per-engine landing distribution + downstream funnel behavior become observable inside the tool your team already uses every day.
28 Labs specifies the page architecture and re-measures citation share against the baseline. Your engineering team builds. We don't deploy to your codebase.
Why this matters
AI bots don't execute the JavaScript that Adobe Analytics, Target, and Audience Manager depend on. The behavior never reaches those tools. The substrate sits in the server logs, not the analytics layer.
28 Labs delivers the AI-channel layer as a managed service feeding intelligence back into the platforms your teams already operate. You get the answer-engine signal, in the tool you already trust, on a quarterly refresh cadence.
Your CISO will appreciate that we deliver intelligence as an outbound feed and dashboards, not as an embedded SDK. No new attack surface, no new vendor inside the production rendering path.
An anonymized executive summary from a recent enterprise marketplace engagement. 65 queries x 4 engines = 260 AI responses, joined to retrieval-side architecture probes. The same shape every audit produces - methodology, scorecard, the structural-leak finding, the substitution-matrix shape, the pilot scoping.
Engagement model
Indicative scope below. Actual engagement structure is shaped in the working session against your category, your engines, and the analytics integration that already exists.
Executive read before committing to deeper work. Single-market, smaller query set.
The full audit substrate. Decision-grade read for CPO / CTO / CMO review.
The audit substrate plus the full intelligence layers, operated as a managed service. Quarterly refresh.
Next step
We walk a sample audit, the substitution-matrix shape for your category, and the strategic options for which class of move is most actionable first. No deck-and-pitch. Working session. We come in with a category read.
28 Labs is operated out of Dubai (Lab 28 Technologies - FZCO). Engagements are signed on DocuSign. Methodology and what we explicitly do not claim are named in writing.