Founding member pricing, locked in for life. Set up in minutes.
Pre-built attribution dashboards and a queryable data layer that connects to Claude, ChatGPT, or any AI assistant. No tiers. No tokens. No scaling with your revenue.
From Brat Vukovich and the team behind Inceptly ($950M+ in D2C revenue driven, brands spending $50K–$1M+/mo) and VidTao (100K+ marketers). We've spent years in the trenches managing attribution for D2C brands — we know exactly where these tools fail you.
You already know what you're paying for isn't working. You check the tool, check the ad platform, check Shopify, compare, go with gut. The question is why.
Triple Whale has 200+ employees. Northbeam raised $25M. Hyros runs paid ads to sell you a paid ads tool.
The infrastructure to run attribution analytics costs pennies per client per month. They charge 100–1000x markup — not for better technology, but to fund sales teams, SDRs, conference booths, demo calls, and investor returns.
Every dollar they spend acquiring you gets passed to you. That's why a tool that shows you the same data as your Shopify admin costs $1,500/month at scale.
And because their business model depends on keeping you inside their app, they have no incentive to make your data portable, queryable, or connected to the AI tools that are becoming how everyone works.
We have no sales team. No SDRs. No demo calls. No conference booths. No investors demanding returns. Just engineers solving problems. So we don't need to charge you for a machine that doesn't exist.
"Your attribution vendor's biggest expense isn't engineering. It's convincing you to buy."
Attribution tracks which channels touched a customer before they bought. That's it. It's a correlation tracker with known, fundamental, mathematical limitations. It gives you direction, not certainty. It guarantees tracking, not outcomes.
The vendor industry — Triple Whale, Northbeam, Hyros — makes this simple thing feel impossibly complicated. Not because it is complicated, but because confusion is profitable. If you understood the limitations, you wouldn't need their premium tier to "solve" a problem that can't be solved.
I've written about this before: the CMO builds a court of domesticated dogs whose job isn't to find truth but to protect the narrative. Attribution is the court's oracle — it produces numbers that look rigorous but can't be verified by anyone without a statistics degree. The dashboard becomes a propaganda machine. Everyone keeps their job. The whole multi-touch discourse exists to keep people employed in the machine.
These aren't edge cases. They're the permanent, structural limitations of every attribution system ever built:
Attribution tracks who touched what before buying. Not whether the ad caused the purchase. Only incrementality testing answers the causal question. No attribution vendor offers it.
You can never observe what would have happened without the ad. Would the customer have bought anyway? Attribution can't answer this. No model can. It's not a solvable problem.
Pick first-touch, Facebook looks great. Pick last-touch, Google wins. Same data, different model, completely different budget decision. There is no "correct" model — only assumptions you chose.
Attribution shows ROAS at current spend. Double the budget and ROAS drops — but the model has no mechanism to warn you. It tells you what happened, not what will happen.
Cut Facebook and Google tanks — because Facebook drove the awareness Google captured. Attribution assigns credit per channel independently. It can't see these dependencies.
Word-of-mouth, podcast mentions, friend texts, DMs. The highest-trust acquisition channels are completely invisible to click-stream attribution. It's not a gap — it's a canyon.
Retargeting and branded search get systematically overcredited because they reach people who were already going to buy. The ad didn't create the demand — it intercepted it.
Multi-touch splits one order across channels. Order counts stop adding up. Average the per-channel ROAS and you'll overstate by 44%. Only Amplitude has publicly acknowledged this.
Our stance is simple: there is no right attribution model. There is only least wrong. Anyone telling you they've solved attribution is selling you the confusion.
First-touch and last-touch are least wrong because they're additive and transparent. One order, one channel, one credit. Revenue sums correctly. You can look at a dashboard, understand it, and act on it. Not because they're "right." Because they don't lie about being right.
We give you all five models. We don't hide multi-touch behind a paywall. But we tell you what they won't: use single-touch for decisions, multi-touch for exploration. Use attribution as one input among many — not the oracle.
And our system handles the math correctly regardless. ROAS computed at query time as SUM(weight × revenue) / SUM(spend). Never pre-stored. Never accidentally averaged. Attribution weights store only additive bases. The ratio is recomputed at every aggregation level — channel, medium, source, campaign. No matter which model you choose, the math doesn't lie.
"It's not about which attribution model is right. It's about which one is least wrong."
We're a team of engineers. No sales department. No demo pipeline. Our voice is our distribution — we publish what we believe, and people who agree become customers.
This lets us do something no VC-funded attribution company can: charge a fair price.
Every other tool locks your data inside their dashboard. We give you dashboards AND an MCP endpoint that connects your data to Claude, ChatGPT, or any AI assistant. Bring your own API key. We provide the infrastructure. You choose the intelligence layer. Which means you can ask your business data anything, in plain English, wherever you already work.
Whether you're doing $500K or $5M. No tiers. No "book a demo for enterprise pricing." One price. Your success shouldn't be your analytics vendor's upsell opportunity.
Connect your store. Connect your ad accounts. Dashboards populate. No pixel debugging. No DNS changes. No three-week onboarding sequence. If we can't get you from signup to live dashboard in under an hour, we've failed.
Bratrax Lite is the self-serve product. But some brands need a dedicated analyst, custom data models, and strategic guidance. That's Bratrax Clear Vision — our service tier with a real human analyst on your data every week. You discover it by outgrowing Lite, not by reading a feature comparison chart.
Done paying $1,500/month for data you still don't trust?
100 founding member spots at $79/month, locked in for life.
Every Bratrax account includes an MCP endpoint — an open connection point that lets any AI assistant query your attribution data directly.
Connect Claude. Connect ChatGPT. Connect whatever comes next. Your data is structured, queryable via 22 specialized tools, and accessible through a standard protocol that any MCP-compatible client can use.
No other attribution tool does this. Because they have no incentive to. Their business model depends on you staying inside their app. Ours depends on your data being useful everywhere.
These aren't hypothetical. This is what happens when a real AI connects to real business data through 22 specialized query tools. The same infrastructure we use for our $5K/month service clients.
You provide the API key. We provide the data. No markup on AI usage. No token limits from us. Your relationship with your AI provider is yours.
One click. Your products, orders, and customer data flow in automatically.
Facebook, Google, or both. Bratrax pulls your spend and conversion data without pixel configuration or DNS changes.
Pre-built dashboards show you which campaigns are profitable, which aren't, and where your next dollar should go.
Grab your MCP endpoint from settings. Point Claude, ChatGPT, or any MCP client at it. Ask anything about your business.
That's it. Most brands are looking at real data within minutes of signing up.
Four steps. No onboarding calls. No six-month setup.
$79/month for founding members.
| Bratrax Lite | Triple Whale | Northbeam | Hyros | |
|---|---|---|---|---|
| Starting price | $79/mo | $129/mo | $1,500/mo | $69–$999/mo |
| Price at $5M GMV | $79/mo | $1,129/mo | Custom (higher) | $999+/mo |
| AI assistant access | MCP endpoint (any AI) | Moby (locked in) | None | None |
| Data portability | Full (query your data) | Limited | No | No |
| Money-back guarantee | 30 days | No | No | No |
| Contract | No | No | Annual | Annual |
You shouldn't need a pricing calculator to figure out what your analytics tool costs.
Most brands start with Lite. Some outgrow it. When you do, Clear Vision is there. You'll know when you need it.
I've been building custom analytics for D2C brands for years. The kind where you get dedicated infrastructure, a custom data model, and someone who actually understands your business logic.
Every engagement starts the same way. The team has an attribution tool. They have dashboards. They have data. And they still don't trust the numbers enough to make a real budget decision without pulling up a spreadsheet and cross-referencing manually.
They're paying $300, $500, sometimes $1,500 a month. And their process is still: check the tool, check Shopify, check the ad platform, compare, go with gut.
We don't have a sales team. We don't have investors telling us to charge more. We have engineers, and we have opinions about how analytics should work. That's it.
Pre-built dashboards. Queryable data layer. Connect your own AI. No sales calls. No contracts. No credit card required to join the waitlist.