Dana unifies Meta, Google, TikTok, Shopify, Klaviyo, and GA4 into one reconciled number. Every metric carries provenance. Every gap is flagged before it reaches a decision.
Meta says 847 conversions. Google says 1,102. GA4 says 1,876. Shopify shows 1,243. Variance runs 30 to 70 percent across sources, and the loudest dashboard wins the meeting.
An API drops at 2 AM. A pixel breaks on mobile. A UTM convention shifts mid-campaign. Most teams notice three to seven days later, after the decisions have already been made.
Ten to fifteen hours per week pulling, cleaning, matching, and rebuilding reports across four platforms. By the time the spreadsheet is finished, the numbers are already stale.
Forecasts, attribution, scenario tests are all only as good as the inputs. Dirty data flowing into Felix, Parker, and Sam compounds every miss they make.
Each capability serves a specific job: unify the sources, reconcile the math, monitor the pipes, normalize the schema, and feed clean data to the rest of the workforce.
Six-plus data sources unified into one reconciled view, with full provenance on every number.
Example. 987 ad-driven conversions traced to 412 Meta + 318 Google + 257 organic
Cross-checks platform-reported conversions against Shopify orders every cycle, with the gap explained line by line.
Example. Meta reported 847; 435 view-throughs removed; 412 verified against Shopify
Watches every pixel, server-side event, and API connection. Surfaces breaks within minutes, not days.
Example. Meta Pixel mobile failure flagged at 2:14 AM, traced to a site deploy
Reconciles event names, currencies, timezones, and naming conventions across platforms so downstream models read one shape.
Example. PST, UTC, and EST source feeds normalized before nightly retrains
Dana is the data layer. Felix, Parker, Sam, Maya, Olivia, and Dex all read from the same reconciled truth.
Example. One reconciliation cycle feeds six agents, no manual exports
One operating day, hour by hour. Specific numbers vary by account.
Meta Pixel fires zero events for 12 minutes on mobile Safari. Dana detects the volume break and pages on-call before the morning standup.
Pulls fresh data from Meta, Google, TikTok, Shopify, Klaviyo, and GA4. Every record stamped with source, pull time, and API version.
Reconciles platform-reported conversions against Shopify orders. 435 view-through duplicates removed. Verified count: 987.
Normalizes timezones (PST, UTC, EST) and currencies (USD, EUR, GBP) to business clock and base currency. Schema written to shared layer.
Feeds reconciled inputs to Felix's nightly forecast retrain and Parker's attribution checks. One shared schema, zero export friction.
UTM convention drift detected: half of last week's paid social campaigns landing in Other. Pattern flagged with 14-day diff.
Server-side event dedupe patches the Add to Cart double-fire on mobile checkout. Provenance log records the fix and the prior state.
Dana runs on purpose-built reconciliation pipelines: per-source ingestion, schema normalization, order-level matching, and provenance for every number that leaves the system.
Shopify orders, Klaviyo events, GA4 sessions, plus Meta, Google, and TikTok ad platforms via OAuth. No warehouse copy, no nightly dump.
OAuth-based ingestion, refresh cadence per source
Sheets for reconciled tables and provenance audit logs. Notion for the long-form data-quality reports. Format adapted per surface.
Reconciled tables refreshed every 6 hours
Felix trains on reconciled inputs. Parker validates conversions against Shopify. Sam grounds scenarios on verified baselines. Maya, Olivia, and Dex read the same shared schema.
One shared data layer across all six other agents
Per-source ingestion adapters, schema normalization across platform event taxonomies, order-level matching on order_id and UTM, and provenance hashing on every record.
Reconciliation pipelines with full audit trail
Ready when you are
The pilot connects your platforms in under five minutes. The first reconciliation cycle finishes within 24 hours, with full provenance from day one.
Looking for something specific? Felix forecasts on clean data →, Parker reconciles spend lift → or Maya remembers the why →.
