Normalized across platforms
Campaign, ad group, keyword and conversion mean the same thing everywhere.
Ad platform API integrations
Every ad platform exposes its data differently, breaks differently and restates history differently. We build the integration layer that normalizes all of it into one warehouse schema your team can query without knowing which API a number came from.
What you get
Campaign, ad group, keyword and conversion mean the same thing everywhere.
Queued, prioritized pulls that never lock out an account.
Credentials handled across hundreds of client accounts.
Per-account ingestion status, without opening a log.
The problem
Off-the-shelf connectors cover the happy path. They fall over on the things that define ad data at scale: per-account rate limits that throttle exactly the reports you need at month end, conversion metrics defined differently on every platform, sandbox-only fields that silently return null in production, and OAuth tokens that expire across hundreds of client accounts at once. Once you are past a few dozen accounts, the connector becomes the bottleneck and nobody owns it.
What the integration layer covers
How we work
Inventory every platform, account and metric you actually report on today
Build the highest-spend platform end to end first, so one channel is trustworthy within weeks
Normalize metric definitions in the warehouse layer, not in each connector
Add the remaining platforms against the proven pattern, then hand over runbooks
What we use
FAQ
For a handful of accounts they are the right call, and we will tell you so. The break point is multi-tenant scale and custom metrics: per-client cost allocation, row-level access, retail media networks with no managed connector, and restatement handling. At that point you are paying per row for something that still needs an engineer, and owning the pipeline becomes cheaper and more flexible.
Requests go through a central scheduler that knows each platform's limits and each account's priority, with queuing, exponential backoff and retry budgets. Heavy historical backfills run in windows separate from the daily syncs, so a backfill can never starve current reporting.
Either your team or ours, decided up front. Deprecations are usually announced months ahead, so we track platform changelogs and schedule migration work before the cutoff. Everything ships with tests that fail loudly on an unexpected schema rather than writing nulls into your warehouse.
Usually, yes. Most retail media networks and smaller DSPs offer a reporting API or scheduled file drops, and we have built ingestion against both. Where only a UI export exists, we will say so plainly rather than promise a fragile scraper.
Go deeper
Guide
SP-API and the Amazon Ads API cover completely different data and have different rate limits, auth flows, and report models. Here's how to think about integrating both.
Read the guideGuide
Retail media spend is spread across Amazon, Walmart, Instacart, and more, each with its own dashboard. Here's how to build one data model across all of them.
Read the guideRelated
Amazon, Walmart, Shopify, Google, Meta, TikTok and more, pulled into one database you own.
Learn moreMulti-tenant ad platform integrations, built in your codebase.
Learn moreIncremental, orchestrated, tested pipelines that re-run without a babysitter.
Learn moreAmazon Ads API, SP-API and AMC pipelines, dashboards and AI agents.
Learn moreTell us which platforms and how many accounts you pull from. We'll tell you where connectors will break first.