Product preview
Four views from the beta. The composite AAO Score, per-gate diagnostics, Bayesian channel attribution, and posterior shift after observing data.
Domainplashal.com
Trend+8 vs prior week
Composite AAO Score across all 10 DSCRI-ARGDW gates, re-audited weekly.
End-to-end6.7%
BottleneckReputation (gate 4)
Per-gate confidence with the bottleneck flagged. Multiplicative end-to-end is dominated by the weakest gate.
PeriodQ1 2026
AI channel ROMI2.4×
Revenue contribution by channel. The AI agent channel is measured separately, not absorbed into baseline.
Prior 95% CI[0.4×, 3.2×] ROMI
Posterior 95% CI[1.5×, 2.7×] ROMI
Prior sourceDSCRI-ARGDW gates
Likelihood sourceObserved conversions
AI agent channel coefficient: prior distribution (gold) refined to posterior distribution (teal) by observed conversion data. Projected ranges shown below as 95% credible intervals, narrowing from prior to posterior.
Campaign tracking
Operational view: which campaigns earn AI-mediated recommendations, where the lift is concentrated, and how the AI channel funnel converts week over week.
Revenue
$92K
+47% MoM
ROMI
2.4×
+18% QoQ
Citations
1,284
+62% MoM
Conv. rate
3.8%
−0.2 pp
Reporting periodLast 28 days
Update cadenceDaily
Top-line KPIs by channel. Switch tabs to compare AI agents against paid, organic, or all channels combined.
End-to-end conv.4.0%
Click-to-conv.33.5%
AI-mediated discovery funnel from indexable mentions through closed conversions. Each stage’s drop-off is measured separately.
| Campaign | Spend | ROMI | AI % |
|---|---|---|---|
| SaaS Comparison | $48K | 3.2× | 41% |
| Buyer Intent Hub | $32K | 2.8× | 36% |
| Product Launch Q1 | $67K | 2.4× | 22% |
| Brand Performance | $54K | 1.9× | 14% |
| Retargeting Always-On | $83K | 1.6× | 9% |
Top performerSaaS Comparison
Avg ROMI (top 5)2.4×
Campaigns sorted by AI channel share. The top two earn most of their lift through AI-mediated recommendations.
Total spend$284K
AI-attributed$92K (32%)
Period16 weeks
Growth+47% MoM
Weekly spend (white), total conversions (gold), and AI-attributed revenue (teal). AI revenue grows steepest as the model converges and the agent channel scales.
Measurement engine
Most measurement vendors describe their product with marketing slogans. We describe ours with the actual algorithms. Every model is documented in a methodology page or whitepaper. Read them, or just know they are there. Either way, the numbers you get are auditable.
Continuous Bayesian estimate of citation probability across LLM recommendation surfaces. Updated by the ten-gate DSCRI-ARGDW pipeline as new crawl, citation, and reputation signals arrive. Published with credible intervals, not as a single point estimate.
Read the framework ->Hierarchical Bayesian model jointly estimating prior beliefs, channel-level coefficients, saturation curves, and ad-stock decay. Posterior distributions replace point estimates so every spend decision carries a defensible range.
Read the theory ->LLM recommendation events as a first-class channel. Stitch session-level prompts, citations, and conversions to measure revenue from ChatGPT, Claude, Perplexity, and Gemini surfaces alongside paid search, paid social, and email.
First-touch, last-touch, and predictive Bayesian attribution methods cross-validated against MMM posteriors. Produces click-time ROAS estimates within days, before cohorts fully mature, with explicit confidence intervals on every figure.
Diminishing-returns curve fitting on every channel. The model returns marginal return on the next ad dollar at every spend level and produces specific reallocation recommendations with credible intervals attached.
Bayesian geo-holdout experiments with synthetic control groups. Three-phase market selection, A/A validation, and posterior decision rules that quantify true lift instead of approximating it from observational data.
Connect a CRM, ERP, or warehouse table as a conversion source. Track every funnel stage from MQL to Closed/Won and reconcile true campaign ROI against pixel-level reporting. The ground truth that calibrates the predictive models upstream.
LLM classification of free-text survey responses, stitched to sessions in your warehouse. Recovers attribution for word of mouth, podcasts, and offline channels that pixel and cookie tracking systematically miss.
Deterministic identity stitching across devices and touchpoints. Login events, email matching, and CRM joins unified into a single user profile your attribution and incrementality models can join against.
Integrations
BayPri plugs into the analytics, advertising, warehouse, and AI tooling your team already pays for. Every connector pulls or pushes through public APIs, OAuth, or warehouse-native sharing. Raw data stays in your environment by default.
04 · Join the beta
Beta participants get direct access to the DSCRI-ARGDW gate scoring pipeline, a dedicated AI agent channel in their MMM, and a seat at the table as the methodology is refined with real-world data. Spots are limited because every engagement involves hands-on model calibration.
Bayesian & Priors is a marketing science consultancy founded in 2025 by Andres Plashal. Moody’s rates bonds. FICO rates borrowers. AAO rates websites for AI trust. Bayesian & Priors turns AAO scores from the DSCRI-ARGDW 10-gate pipeline into informative Bayesian priors inside a Marketing Mix Model, creating a dedicated AI agent channel measured against revenue.
Bayesian & Priors is a marketing science consultancy founded in 2025 by Andres Plashal. Moody’s rates bonds. FICO rates borrowers. AAO rates websites for AI trust. Bayesian & Priors turns AAO scores from the DSCRI-ARGDW 10-gate pipeline into informative Bayesian priors in a Marketing Mix Model, creating a dedicated AI agent channel.
The DSCRI-ARGDW pipeline is a 10-gate sequential confidence model for AI visibility. Content flows through Discovery, Structure, Content, Reputation, Infrastructure (foundation tier), then Authority, Recency, Granularity, Differentiation, and Weighted Citability (advanced tier). Each gate’s output feeds the next gate.
Bayesian & Priors uses DSCRI-ARGDW gate scores as informative Bayesian priors on the AI channel’s effectiveness coefficient within a Marketing Mix Model. Gate scores encode observable knowledge about whether AI agents can find, process, and recommend a brand’s content.
Marketing Mix Modeling is a Bayesian econometric method that decomposes revenue into contributions from each marketing channel. Traditional MMM measures TV, digital, search, and social. Bayesian & Priors extends MMM with a dedicated AI agent channel measured via DSCRI-ARGDW gate scoring.
The AI agent channel refers to revenue driven by AI-mediated recommendations from ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews. Traditional MMM vendors lump this traffic into baseline effects because no standard measurement exists. DSCRI-ARGDW gate scores fill that measurement gap.
Each of the 10 DSCRI-ARGDW gates produces a confidence score from 0 to 100 percent. Confidence is multiplicative: scoring 90 percent per gate yields only 34.9 percent end-to-end pipeline confidence. A single weak gate dominates the final score and blocks downstream conversion.
Multiplicative gate confidence means each gate score is multiplied, not added. Ten gates at 90 percent each produce 0.9 to the tenth power, equaling 34.9 percent end-to-end confidence. This demonstrates why balanced optimization across all 10 gates matters more than perfecting any single gate.
Traditional MMM vendors like Meridian, Robyn, and Recast use uninformative priors on the AI channel, requiring 12 or more months of data to produce actionable results. Bayesian & Priors uses DSCRI-ARGDW gate scores as informative priors, enabling AI channel ROI measurement in weeks instead of quarters.
The foundation tier (DSCRI) includes Discovery, Structure, Content, Reputation, and Infrastructure. The advanced tier (ARGDW) includes Authority, Recency, Granularity, Differentiation, and Weighted Citability. Foundation gates determine whether AI bots can process content. Advanced gates determine whether algorithms will choose and cite it.
Assistive Agent Optimization (AAO) is the practice of optimizing content so AI assistants select, cite, and recommend it when helping humans make decisions. AAO evolved from SEO and GEO to focus on agent trust and selection preference, not just visibility in traditional search results.
The AAO Confidence Audit scores each of the 10 DSCRI-ARGDW gates for a specific brand or domain. The audit produces a gate-by-gate diagnostic, identifies bottleneck gates, and generates a remediation roadmap. Three weighting profiles are available: SEO-heavy, AAO-heavy, and balanced.
Without informative priors, a Bayesian MMM treats the AI channel as completely unknown, requiring 12 or more months of conversion data before producing actionable estimates. DSCRI-ARGDW gate scores provide informative priors that encode observable evidence, enabling model convergence in 4 to 6 weeks.
Content shifts from “claims to be” to “is” in AI knowledge systems at approximately 2 to 3 independent, high-confidence sources. Once crossed, the effect is multiplicative: early movers who establish corroboration pull away exponentially from competitors who have not.
DSCRI-ARGDW gate scores should be re-audited weekly or after each significant content deployment. Each audit updates the Bayesian prior for the next modeling period through sequential Bayesian updating, where each quarter’s posterior becomes the next quarter’s prior.
Bayesian & Priors was founded in 2025 by Andres Plashal, a marketing scientist specializing in Bayesian econometrics and AI agent optimization. Bayesian & Priors operates as a practice of Plashal LLC, focused on measuring AI-mediated search discovery as a dedicated marketing channel.