We build and maintain ad platform API integrations across Amazon Ads, Google, Meta, TikTok, and retail media, with rate-limit handling and restatement-aware syncs.
Why connector tools stop being enough
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
- Authenticated ingestion from Amazon Ads, Amazon SP-API, Google Ads, Meta, TikTok, and retail media networks
- A normalized schema where campaign, ad group, keyword, and conversion mean the same thing across every source
- Rate-limit aware scheduling with queuing and backoff so heavy report pulls never lock out the account
- Token and credential management across multi-account, multi-tenant agency structures
- Restatement-aware syncs that re-pull attribution windows rather than freezing yesterday as final
- Per-account ingestion status so you can answer "is this client's data current?" without opening a log
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 remaining platforms against the proven pattern, then hand over runbooks
Typical stack
Frequently asked questions
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.