Dioptra
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ML Image Classifier
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mbuzz.co
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Triple Whale
Attributer
Digital Analytics
mbuzz is multi-touch attribution for technical marketers who've stopped trusting their dashboard. Here's the thing nobody selling you attribution wants to say out loud: every tool runs a model under the hood, and the number it reports isn't "the data." It's that model's opinion of the data. Same touchpoints, different model, completely different "best channel."
mbuzz runs eight of them at once. First-touch, last-touch, linear, time-decay, position-based, Markov, Shapley, data-driven. You can compare them, argue with them, and write your own in a SQL-like DSL if none of the stock eight fit how your business actually works.
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mbuzz.co's answer:
Every attribution tool runs a model under the hood and reports its number like it came from physics. mbuzz is the only one that shows the model. Eight of them side by side, plus a SQL-like DSL to edit or write your own. You stop arguing about which channel works and start arguing about which model you should trust.
mbuzz.co's answer:
Dreamdata, HockeyStack, and Northbeam all ship with a proprietary "data-driven" model you can't see inside. You pay $1,400โ$5,000 a month to trust their math. mbuzz runs eight models you can inspect, lets you edit the logic in a SQL-like DSL, keeps your data exportable on every plan, and starts at $0. For a $1โ100M company spending $20Kโ$1M a month on ads, that's the difference between renting an attribution tool and owning an attribution stack.
mbuzz.co's answer:
Technical marketers, marketing ops, growth engineers, and data-savvy CMOs at startups and mid-market SaaS, DTC, fintech, and healthtech companies spending $20Kโ$1M a month on paid media. Specifically the ones who've stopped trusting their dashboard โ who want to audit the math themselves, not hear "trust our algorithm."
mbuzz.co's answer:
Years of wrestling with the limitations of various existing solutions, platform-inflated ROAS, and enterprise attribution tools that cost more than the budgets they were measuring. Every tool I tried picked one model and hid the math. I wanted to compare models, argue with them, and write my own rules โ so I built one. mbuzz is the attribution platform I wished existed when I was trying to explain channel performance to a CFO who didn't believe the Meta pixel.
mbuzz.co's answer:
Ruby on Rails (backend + dashboard), PostgreSQL, Sidekiq for background jobs, Stimulus/Turbo for the frontend. Open-source SDKs in Ruby, Node, Python, and PHP. Deployed via Kamal on DigitalOcean.
Based on our record, Dioptra seems to be more popular. It has been mentiond 1 time since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Hi HN! Iโm Farah, co-founder of [Dioptra.ai](https://dioptra.ai/) this week and wanted to get your take. Katiml is a vector+data lake to debug, curate and version AI data. With katiML, teams avoid the โgarbage in, garbage outโ effect by taking control over the quality of their data. They quickly and effectively curate high quality data for training, fine-tuning, and fixing hallucinations and edge cases. Features... - Source: Hacker News / about 3 years ago
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