Google Ads reports media spend. Meta reports its own numbers. Neither touches broker fees, post-conversion refunds, chargeback attribution, or compliance costs. The result is a cost-per-lead figure that looks clean and is materially wrong.
CDAI ingests cost and outcome data across every channel, reconciles true contribution margin after every hidden cost, detects six structural distortion signals, and issues a single enforceable capital directive per campaign — automated delivery on your data cadence.
The CDAI dashboard is live and operational. All thirteen panels update daily from verified cost and outcome data. What you see is the actual output your team works from — not a mockup, not a demo environment.
Every adjacent tool solves a fragment. Attribution tools handle channel spend. Lead distribution platforms track delivery. BI tools visualize what you give them. None assembles the complete cost stack and issues a directive.
| Capability | Attribution Tools (Rockerbox, Triple Whale) | Lead Distribution (boberdoo, LeadsPedia) | BI / Analytics (Looker, Tableau) | CDAI Engine |
|---|---|---|---|---|
| True post-refund contribution margin | ✗ | ✗ | ✗ | ✓ |
| Broker payout reconciliation | ✗ | Partial | ✗ | ✓ |
| Compliance cost attribution | ✗ | ✗ | ✗ | ✓ |
| Automated capital directives | ✗ | ✗ | ✗ | ✓ |
| Confidence scoring per directive | ✗ | ✗ | ✗ | ✓ |
| Quality decay detection | Partial | ✗ | ✗ | ✓ |
| Partner risk scoring | ✗ | ✗ | ✗ | ✓ |
| Budget reallocation modeling | ✗ | ✗ | ✗ | ✓ |
| Enrollment deadline intelligence | ✗ | ✗ | ✗ | ✓ |
| Zero consumer PII architecture | ✗ | ✗ | ✗ | ✓ |
Most marketing analytics tools display confident numbers on incomplete data. CDAI does the opposite: when the data isn't trustworthy, the engine flags it and refuses to issue directives. Here's what happened when we ran it on two real businesses with real data quality problems.
Google Ads export. 94 days, $2,112 spend, 37 conversions. Data was complete but 4 months stale. The health monitor set directive_safe = FALSE and refused to issue directives.
Result: The engine refused to issue directives on stale data — exactly as designed. No fabricated numbers. No confident-looking outputs built on bad inputs.
Meta Ads export. 3.5 months, $2,414 spend, 7 campaigns. Lead attribution was missing — all 276 CRM records marked "Contact Import". The engine surfaced the gap and refused to invent attribution.
Result: The engine flagged the attribution gap and refused to invent data. When CFOs ask "how do I know your tool isn't hallucinating?" — this is the answer.
Multi-tenant isolation verified in production. Both tests ran against the same database with Row-Level Security policies active. No cross-organization data leakage. Complete technical validation available in the full case study.
READ FULL VALIDATION CASE STUDYPricing based on verified scope and spend — not estimates. Audits prove the value. Retainers sustain it. Every engagement includes 100% audit credit toward retainer if started within 30 days.
Choose your audit based on campaign count. Final price confirmed within 5 business days of platform access. If actual scope differs significantly from the tier selected, pricing is adjusted in writing before work begins.
Ongoing monitoring with automated nightly updates. Tier determined by verified monthly ad spend observed through platform APIs during audit or first 30 days — not self-reported figures.
Audit Credit: 100% of audit fee credited toward first 90 days of any retainer started within 30 days of audit delivery.
| Feature | Attribution Tools (Triple Whale, Rockerbox) | Agencies (Manual consulting) | CDAI |
|---|---|---|---|
| Enforceable Directives | ✗ | Manual | ✓ Automated |
| Outcome Accuracy Tracking | ✗ | ✗ | ✓ 80% hit rate |
| True Margin (7-Cost Stack) | ✗ | Manual | ✓ Automated |
| Partner Accountability | ✗ | ✗ | ✓ |
| Price | $99–$999/mo | $5K–$25K/mo | $1.5K–$25K/mo |
Nick Baum is the founder of Allocera Intelligence. His background is in performance marketing, paid acquisition, and full-funnel measurement — owning GA4 and GTM architectures, Google and Meta campaign management, and conversion tracking across live, revenue-generating operations in regulated environments.
Working directly inside lead generation operations, Nick encountered the same structural problem consistently: platforms report only the costs they control. Broker fees, post-conversion refunds, chargebacks, compliance costs, and variable overhead never appear in campaign reporting — which means the cost-per-lead number used to make scaling decisions is systematically wrong. The gap between what dashboards show and what campaigns actually cost is not a rounding error. At meaningful spend levels, it is a capital allocation problem that compounds every single month.
CDAI was engineered to solve that problem specifically. The system ingests data from ad platforms, CRMs, and lead distribution infrastructure — including boberdoo and LeadsPedia — reconciles every real cost layer, and issues one enforceable directive per campaign. It is not a visualization tool. It is a decision engine, built by someone who understood where existing tools stop and where the real problem begins.
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