Knowi's answer

Verizon Telstra Everbridge ConvergeOne NJM Insurance Volante Systems intlx Solutions Alteas Health Hometime Tata

Knowi's answer

Knowi runs AI agents inside the data layer rather than on top of finished dashboards. The agents reach schemas and the query engine directly, which means they can build queries, widgets and dashboards, not just answer questions about ones that already exist.

That architecture comes from what Knowi was built to do: query data where it already lives. It speaks MongoDB and Elasticsearch in their own query language, including nested documents and arrays, and it joins across MongoDB, PostgreSQL, Snowflake, Databricks SQL, Trino, Salesforce and REST APIs in a single query without first moving anything into a warehouse.

Knowi also runs its own AI by default, with OpenAI and Claude available as optional integrations rather than requirements, and it deploys to cloud, your own VPC, on-premises via Docker or Kubernetes, or air-gapped.

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.

Knowi's answer

Three practical reasons.

Your data does not have to be relational first. Most BI tools need a warehouse and an ETL pipeline before you see a chart, which means unstructured, nested and API data either gets flattened or gets left out. Knowi queries those sources natively, so the modelling work you would normally do up front becomes optional.

The AI is part of the query path, not a chat box on the side. Many platforms added a copilot that describes existing dashboards. Knowi's agents have access to the schema and the query engine, so they can create new datasets, widgets and dashboards from a question.

You control where it runs and which model touches your data. Knowi AI is the default, third-party models are optional, and deployment can be Knowi Cloud, your own VPC, on-premises, or air-gapped. Knowi is SOC 2 Type II certified, GDPR compliant, and offers a HIPAA BAA.

mbuzz.co's answer:

Dreamdata, HockeyStack, and Northbeam all ship with a proprietary "data-driven" model you can't see inside. You pay $1,400–$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 $1–100M company spending $20K–$1M a month on ads, that's the difference between renting an attribution tool and owning an attribution stack.

Knowi's answer

Data, engineering and product teams at mid-market and enterprise companies whose data is spread across more than one kind of system: NoSQL alongside SQL, warehouses alongside SaaS APIs and documents.

Two buying patterns show up most often. Internal analytics teams who need governed self-service across sources without building a pipeline for every question. And product teams who need customer-facing, multi-tenant analytics embedded inside their own application with row-level access control.

By industry, the customer base skews to telecom, healthcare, manufacturing, SaaS and adtech, proptech and e-commerce. Knowi is sold through a sales team and priced per deployment rather than by public self-serve tier.

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 $20K–$1M a month on paid media. Specifically the ones who've stopped trusting their dashboard — who want to audit the math themselves, not hear "trust our algorithm."

Knowi's answer

Knowi was founded in 2014, at the point where a lot of production data had stopped being relational. Teams were running MongoDB and Elasticsearch, and the BI tools of the day all assumed a star schema in a warehouse. Getting a dashboard meant building a pipeline first, and anything nested or semi-structured got flattened or dropped along the way.

Knowi was built the other way round: connect to the source, query it in its own language, and join across sources at query time instead of moving the data. Cross-source joins, post-query transformation and a dataset layer that data teams could govern followed from that starting point, and embedded analytics came from customers who wanted to give the same views to their own users.

The AI work is a continuation rather than a pivot. Because Knowi already owned the query path across every connected source, AI agents could be placed inside the data layer with access to schemas and the query engine, instead of being bolted onto a finished dashboard.

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 — 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.