Amazon changes report schemas, throttles endpoints, and deprecates fields without much notice. We build resilient ETL pipelines with schema drift detection, automatic retries, and versioned staging tables so a breaking change becomes a monitored alert not a silent gap in your dashboards.
We ingest each platform via its API (not CSV exports), normalize currencies and date formats in dbt, and land everything in a single warehouse layer. One ROAS definition, one spend model, one place your team and your AI agents query.
Yes with guardrails. Our agents read from a modeled warehouse, propose actions within rules you define (max bid change, budget caps, approval queues), and log every change. Nothing runs autonomously until you sign off on the policy layer.
Dashboards show what happened. A warehouse lets you join ad spend with inventory, CRM, and finance data and gives AI agents a single source of truth to act on. Without it, every "smart" automation is guessing from incomplete numbers.
A focused single-source pipeline (e.g. Amazon Ads → Snowflake) usually ships in 4–6 weeks. Multi-platform unification with automation layers typically runs 8–14 weeks depending on source count, historical backfill depth, and BI requirements.
Both. We partner with performance agencies managing hundreds of client accounts (like Tinuiti) and with marketplace brands running their own ad ops. The architecture scales differently, but the core problem fragmented data and manual workflows is the same.
Python and Airflow for orchestration, dbt for transforms, Snowflake or BigQuery for warehousing, and Kafka or Spark when real-time volume demands it. We match the stack to your team's skills and your data volume not the other way around.
Tokens are stored encrypted, rotated on schedule, and scoped per client account. We build retry logic around rate limits and monitor token health so a revoked credential surfaces as an alert before it silently stops a pipeline.