A San Francisco startup that reads your database's replication log and streams every change to your warehouse before you finish your coffee.
Photographed: a logo small enough to fit in a favicon, sitting on top of 700 billion rows of other people's data.
A customer at a fintech updates a payment record. A user on Substack hits publish. An inventory count ticks down by one. In the old world, those changes sat in a source database and waited - for a nightly batch job, for a Kafka cluster someone forgot to patch, for a data engineer to notice the pipeline had quietly died. In Artie's world, the change is already gone. Read off the replication log, streamed across the wire, and written to Snowflake in under a minute. Nobody paged. Nobody noticed. That is the point.
Artie is a fully managed real-time data streaming platform. Roughly eighteen people in San Francisco run the plumbing under companies like ClickUp, Substack, and Alloy. The product does one unglamorous thing extremely well: it moves production data from where it is created to where it is analyzed, continuously, without breaking. In a category full of dashboards and demos, Artie sells the absence of drama.
For two decades, moving data meant batches. Extract at midnight, transform somewhere, load before the analysts arrive. It worked when "the data" meant last week's sales. It works less well when the data is supposed to catch fraud, show a customer their live balance, or feed an AI model that is only as smart as its freshest input.
The alternatives weren't kind. Roll your own change data capture on Debezium and Kafka, and congratulations - you now operate a distributed system whose failure modes you will memorize at 3 a.m. Use a managed service like AWS DMS, and you inherit its quirks around schema changes and large tables. Either path leaves a data team babysitting infrastructure instead of using the data.
The cruel detail is that pipelines fail silently. A schema changes upstream, a column is added, and the load breaks in a way nobody sees until a number on a dashboard looks wrong a week later. Trust, once gone, is expensive to rebuild.
Artie's founders had lived this. The bet was that real-time, log-based replication shouldn't require a platform team - it should be a connector you set up in under an hour and then forget.
Artie was founded in 2023 by Jacqueline Cheong and Robin Tang - a married team who chose data plumbing over almost anything more glamorous. Cheong runs the company as CEO. Tang, the CTO, had spent years scaling infrastructure at Opendoor, Zendesk, and a string of early-stage startups, which is to say he had personally felt the pain Artie now sells against.
Leads the company and its streaming-first thesis: in the AI era, stale data is a defect, not an inconvenience.
Scaled infrastructure at Opendoor and Zendesk before building the engine that reads replication logs for a living.
Y Combinator backed them early and stayed in. So did an angel list that doubles as a data-and-creator hall of fame: Dropbox's Arash Ferdowsi, Mode's Benn Stancil, Substack's Chris Best, and Lenny Rachitsky. The thesis they all bought into is simple enough to fit on a sticker.
Artie uses log-based change data capture. Instead of querying your database and slowing it down, it reads the replication log - the running record every database already keeps - and streams those changes onward. The data passes through and lands at the destination; Artie keeps none of it. For a security team, that last sentence does a lot of work.
Postgres, MySQL, MongoDB, and DynamoDB in. Snowflake, BigQuery, Redshift, Databricks, and Oracle out. Set up in minutes, with monitoring built in.
Columns get added, tables get sharded, things drift. Artie evolves the schema in-flight and recovers from failures instead of skipping rows.
Column-level include/exclude, encryption and hashing for PII, credentials encrypted at rest. SOC 2 Type II and HIPAA compliant. Deploy in your own cloud or on-prem.
One click turns a table into an SCD Type 4 history table, so you can see how a record looked at any point in time - useful for audits and after-the-fact questions.
Figures self-reported via Artie customer stories. Bar lengths are illustrative; Tatango's bar is scaled for display, not a percentage.
The customer list reads like a cross-section of companies that cannot afford stale data: ClickUp and Substack on the product side, and fintechs like Alloy, Synctera, Keep, and YNAB where a wrong number is a compliance problem, not a typo. Partnerships with Snowflake and a listing on AWS Marketplace put Artie inside the stacks customers already use.
The Series A came with a thesis, not just a check. Standard Capital's Dalton Caldwell led the round because AI systems are only as good as the data feeding them, and most data still arrives late. Artie's plan for the money is unsexy and specific: extend real-time support beyond databases into event APIs, search systems, and vector databases; build a self-serve experience; and offer enterprises a bring-your-own-cloud deployment so the data never leaves their account.
The vision is that "real-time" stops being a premium feature you justify in a planning meeting and becomes the assumption - the way HTTPS quietly became the default and nobody argues about it anymore.
Go back to that customer at the fintech, updating a payment record. The inventory ticking down. The user hitting publish. A few years ago, each of those events would have started a clock - the gap between something becoming true and the rest of the system finding out. That gap is where fraud hides, where dashboards lie, and where an AI model gives a confident answer based on last Tuesday.
Artie's wager is that the gap is going to zero, and that whoever makes it disappear quietly - no cluster to babysit, no 3 a.m. page, no data stored where it shouldn't be - gets to be the wire everyone runs on. Eighteen people, 700 billion rows, and one stubbornly boring promise: by the time you look, the warehouse already knows.