by Trivas.aiTrivas AI is an ecommerce analytics and automation platform built on Amazon Redshift, made for brands running Amazon, Shopify, Meta, Google, and GA4 at the same time. If you sell across more than one channel, you already know the problem: five dashboards, five different definitions of "revenue," and an afternoon lost trying to make them agree with each other.
That's the exact pain the Trivas AI ecommerce platform is built to remove. Instead of pulling exports from Amazon Seller Central, Shopify, your ad platforms, and GA4 and reconciling them by hand, everything lands in one Redshift-backed warehouse with one set of numbers. Teams using it have cut reporting time from 3 hours to 20 minutes, not by working faster, but by not doing the reconciliation work at all.
If you're a DTC founder or growth lead currently comparing Triple Whale, Northbeam, or Polar Analytics, this is where Trivas sits: a full data layer for the business, not just an ad attribution tool wearing a dashboard.
Trivas isn't one dashboard. It's three layers stacked on the same data foundation.
Layer 1 is performance dashboards: Amazon, Shopify, Meta and Google ad spend, and GA4 funnel data, unified on a single Redshift warehouse. No separate logins, no exporting CSVs into a spreadsheet someone updates manually every Monday.
Layer 2 is Wingman AI, the insights layer. Instead of building a pivot table to figure out why conversion rate dropped last Tuesday, you ask it in plain language and it surfaces the anomaly, with the context behind it. Learn more about how this layer works on the insights product page.
Layer 3 is forecasting and simulation. This is the part most competitors skip entirely. Rather than telling you what already happened, it models demand and ad spend scenarios ahead of time, so you can plan a Q4 budget instead of just explaining it after the fact. Details are on the forecasting and simulation page.
The three layers aren't separate products bolted together. Data feeds the insights layer, insights feed the forecasts, and all three pull from the same warehouse. That's the actual point: the number you see in a dashboard is the same number Wingman references and the same number the forecast is built on. No version drift between "reporting Trivas" and "forecasting Trivas."
A lot of tools in this space started as an Amazon reconciliation tool or a Shopify attribution app and expanded outward. Trivas was built to sit under the whole stack from the start.
On the commerce side: Amazon, Shopify, WooCommerce, Walmart, eBay, Etsy, Target, plus marketplaces like Zalando and Allegro for brands selling into Europe. If you're specifically Shopify-first, there's a dedicated breakdown on the Shopify solutions page.
On ads and marketing: Google Ads, Meta, TikTok, Reddit Ads, GA4, Klaviyo, and Mailchimp. That covers both the paid acquisition side and the retention/email side, which most "ecommerce analytics" tools treat as an afterthought or don't touch at all.
And for teams that need finance and fulfillment in the same view instead of a separate tool: Stripe, ShipStation, and Akeneo.
The point of listing all of this isn't to win a feature-count contest. It's that Trivas isn't a point solution for one channel with a few bolted-on integrations. It's a data layer that assumes you sell in more than one place, because most brands doing real revenue do.
Different roles hit different walls with fragmented reporting, and Trivas is built around four of them specifically.
Founders and CEOs who need one number for the business, not five numbers from five team leads who each calculated it differently. Check /who-we-help/founders-ceos if that's you.
Marketing and performance leaders who need blended ROAS and cross-channel attribution without a weekly spreadsheet ritual to produce it.
Data analysts and operations managers who'd rather not spend their week stitching APIs together and would prefer a clean warehouse layer already sitting there waiting.
Agencies and consultants managing multiple client accounts, where reporting has to scale across brands without rebuilding the same dashboard from scratch every time a new client signs.
If you're in one of these seats, the platform is built around your specific reporting headache, not a generic "analytics for everyone" pitch.
Let's be honest about where you probably are: you've got one of these tools open in another tab right now, or you've used one before and it didn't stick. So it's worth naming directly instead of pretending they don't exist.
The short version: Trivas is built on a Redshift-based data warehouse with forecasting and simulation baked in as a core layer, not attribution reporting with a forecast bolted on top. That's a real, structural difference in what the platform is trying to do, not just how it's priced or how it's designed.
For the line-by-line breakdown of features, pricing tiers, and integration depth, that's better covered on the comparison page than re-litigated here. Go read it if you want specifics on where each tool wins for your exact setup.
Setup follows a straightforward path: connect your data sources, and the Redshift warehouse builds itself in the background. No manual schema mapping, no waiting on a developer to configure a data model before you see anything.
If you're starting from Shopify specifically, install is handled through Trivas AI on the Shopify App Store, which is the fastest way in if Shopify is your primary channel.
For teams that want more hands-on setup instead of pure self-serve, guided onboarding and training support is available. Some teams just want to connect and go. Others want someone walking them through the dashboard build and the first few forecasts. Both paths exist here, and neither one requires waiting weeks to see your data unified.
If you've read this far, you already know whether the five-dashboard problem is yours. The fix is the same platform this whole page has been describing: Amazon, Shopify, Meta, Google, and GA4, unified on one Redshift warehouse, with insights and forecasting built on top instead of reporting alone.
The next step is to see it against your own data. Start a trial or talk to a founder directly if you'd rather walk through it live. If you want the cost picture before you book anything, the pricing page is the lower-commitment place to start. And if you're just browsing for now, our newsletter and blog cover the same ground in smaller bites, worth a subscribe if you're not ready to commit yet.
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