Straight answers about ad data.

Amazon Ads (including SP-API and Amazon Marketing Cloud), Walmart Connect, Shopify, Google Ads, Meta and TikTok, plus attribution platforms. We also work with Instacart, Criteo and other retail media networks.

No. Start with the step that hurts most, often one pipeline and one dashboard. A focused single-source pipeline usually ships in 4–6 weeks. Multi-platform work typically runs 8–14 weeks, depending on how many sources, how much history and what reporting you need.

Amazon changes report schemas, throttles endpoints and retires fields with little notice. We build pipelines with schema-change detection, automatic retries and versioned staging, so a breaking change becomes an alert we fix, not a silent gap in your dashboards.

We pull each platform through its API, not CSV exports, and clean it into one model. Revenue comes from the right place for each channel: retailer sales data for Amazon and Walmart, Shopify orders for your own store. Then one ROAS rule you agree on is applied everywhere, so your team, your dashboards and your AI all use the same number.

Every platform credits sales differently: Amazon uses 7- or 14-day windows depending on the ad type, Meta defaults to 7-day click and 1-day view, and Amazon restates recent conversions after the fact. We keep each platform's own numbers and window, re-pull recent days when they restate, and show your attribution platform's model next to them. Your blended view uses one rule you choose, and it's labeled clearly.

Insights tell you what changed and why: a ROAS drop, wasted spend, a spike worth a look. Agents go one step further and make the change, like moving a bid, shifting budget or pausing waste. Most agent actions are rules you approve, with AI summaries on top. Many teams start with insights and add agents later.

You choose how much control to hand over. Either you approve every change, or you approve the rules once (bid caps, budget limits, which accounts) and the agent acts only inside them. Every change is logged with the before and after, can be rolled back, and the agent stops if the data feed is late.

You do. Everything runs in your cloud (AWS, GCP or Azure) and your warehouse (Snowflake, BigQuery or Redshift). Code, docs and runbooks are yours.

No. Pipelines and dashboards are the foundation. Most clients also ask for custom work on top: their own metrics, client-specific views, data joined to inventory and margins, forecasting, or internal tools and product features built on the data. We scope that with you.

We're not a media-buying agency: our core is the data layer. But our AI agents do real account work on top of it. They analyze your accounts and show what to fix first, draft new campaigns, keywords and budgets from rules you set for your team to approve, and adjust bids and budgets within your limits. Your PPC team or agency stays in charge; the agents take the repetitive work off their plate.

Every engagement is custom, so we quote after a free 30-minute call: a written scope and a fixed price before any work starts. What shapes it is how many platforms and accounts you run, how much history to backfill, the custom work you need, and whether we run it or hand it over. Your own cloud and warehouse usage is billed to you by your provider.

Your choice. We can run and monitor it for you on a monthly plan, fixing breaks when a platform changes something, or hand it to your team with docs, runbooks and a walkthrough. You don't need to hire a data engineer to work with us.

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.

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.