01 · Pull
Every platform, pulled automatically.
Scheduled API jobs pull Amazon, Walmart, Google, Meta and TikTok, your Shopify store and your attribution tool. No CSV exports, no stale snapshots.
For agencies, brands and ad-tech platforms · since 2013
We pull your ad, store and attribution data into one database you own, clean it, build your dashboards and any custom data work you need, then add AI that spots problems and acts only within rules you approve.
Works with
+ Instacart, Criteo, Noon, Snapchat and other networks · every Amazon marketplace, from Amazon.ca to Amazon.ae and Amazon.com.au
Trusted by Tinuiti, BTR Media, Pixis, SellerFuse and Mau Brands · the data layer Tinuiti ran on as it grew to 3,500+ brands
How it works
Six steps we run for agencies and brands. Build all six, or start with the one that hurts most.
Custom work
No two clients need the same thing. Once your data is clean and in one place, we build what your team asks for on top of it.
Margin-adjusted ROAS, contribution margin by channel, TACoS by brand, new-to-brand by retailer. Whatever your finance team actually uses.
Per-client rules, KPIs and white-label reports for agencies, without forking the pipeline for every account.
Inventory, margins, promotions, CRM and offline sales next to ad spend, for inventory-aware pacing and real profit.
Cross-network deduplication, restated conversions, currency and time-zone fixes, and the matching finance signs off on.
Budget forecasts, marketing mix models and incrementality analysis built on the same clean data.
Internal tools, client portals and product features, like the ad platform and creative studio we engineered for Pixis.
Platforms
Every platform reports its own way: different columns, mixed currencies, four date formats in one table. We turn all of it into one model your team and your AI can trust.
Marketplaces & retail media
Our deepest platform
Sponsored Ads, DSP, the Selling Partner API and Amazon Marketing Cloud, lined up across every marketplace. When Amazon changes a report, you get an alert, not a gap in your dashboards.
Built for: Tinuiti, SellerFuse, Protego, Mau Brands
Amazon data in depthCampaign, item and spend data pulled on a schedule, matched to the same model as Amazon so you can compare retailers like for like.
Built for: BTR Media's retail media platform
Walmart data in depthAlso: other retail media networks like Instacart and Criteo.
Store
Orders, revenue and customers, joined to ad spend so ROAS is based on real sales, not what each platform claims.
Works alongside Triple Whale, Northbeam or your attribution tool.
Shopify data in depthSearch & social
Campaign, keyword and search-term data pulled through the API, with full history backfills.
Audience and creative breakdowns, joined to the same spend model as every other channel.
Spend, impressions and conversions with TikTok's own attribution settings kept, ready for like-for-like comparisons.
Attribution
Attribution data joined in, so every channel is credited on one model instead of each platform claiming the same sale.
Warehouse
Snowflake, BigQuery or Redshift in your own cloud, modeled from raw to reporting-ready. CRM and finance data can join too.
Who it's for
Three kinds of teams hire us. All of them are tired of stitching exports together before they can make a call.
You run many client accounts across Amazon, Walmart, Google, Meta and TikTok, and analysts lose days to exports. We build one pipeline for every client, with per-client rules and white-label client reports, so strategists get numbers they can defend.
You sell on Amazon, Walmart and Shopify, buy media on Google, Meta and TikTok, and still can't see true ROAS in one place. We join ad spend to real sales (retailer data for Amazon and Walmart, Shopify orders for your store), automate the reports, then let AI handle routine bid and budget moves within your rules.
Integrations are your product. We build and maintain Amazon Ads API, SP-API, Walmart, Meta and TikTok integrations inside your codebase and sprints, multi-tenant and rate-limit safe, so your team gets back to the roadmap.
Agencies, retail media platforms and brands run their reporting, automation and AI on pipelines we built, including the data layer Tinuiti ran on as it grew from 35 to 3,500+ brands.
Case studies
What one clean set of ad data did for these teams.
Amazon, Walmart and Instacart data in one pipeline and one dashboard.
Read the case study
One data pipeline across every client account in the agency.
Read the case study
Before you ask
Everything runs in your cloud, under access you grant and can revoke. We use each platform's own API permissions with least privilege, keep credentials in your secrets manager, and log every change an agent makes. We sign your NDA before discovery and a DPA before any data access.
Choose a monthly plan where we run it, watch the alerts and fix breaks when a platform changes something, or a handover with docs, runbooks and a walkthrough. Either way, you're never stuck.
Every engagement is custom: pipelines, dashboards and the data work your team needs on top. We quote after a free 30-minute call, as a written scope with a fixed price, then an optional monthly plan. How pricing works
Agencies get one pipeline across every client account, per-client rules and white-label client reports. In-house data teams get work done in their own repo and dbt project, through their own code review.
How we build
Engineering since 2013, with teams in Lahore, Sharjah, Bengaluru and Surrey, BC. Our focus: give agencies, brands and ad-tech platforms ad data they can trust, then put it to work.
Playbooks and explainers on Amazon Ads and AMC, retail media data, warehouses, attribution and AI agents, from the engineers who build them.
Explore the guidesAnswer a few questions and an engineer sends back a short plan: what to connect first, what to build, and a realistic timeline. No sales deck.
Start my ad data planFAQ
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 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.
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.
Talk to an engineer who works in Amazon, Walmart, Shopify, Google, Meta and TikTok data every day, not a sales rep. We'll tell you what we'd fix first.
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