Building AI you can actually trust with your work.
Artfical builds tAI and tCode end to end: our own models, our own servers, and a product built around the idea that your data is yours, not a resource we monetize.
Why we build it this way.
We think the way an AI product is built matters as much as what it can do. A model that's genuinely useful for your email, your code, and your work has to earn a level of trust that a general-purpose chatbot doesn't need, and that trust has to be backed by real decisions, not just a page like this one.
That's why we made three choices early on that shape everything else about Artfical: we train our own models on data we compile ourselves, rather than fine-tuning someone else's; we run our own infrastructure, rather than renting space on a general-purpose AI platform; and we treat privacy as the first constraint on every feature, not a policy we retrofit once something ships. Each of those choices made the company harder to build. We made them anyway, because they're the only way we know to actually keep the promises we make.
Three decisions that shape everything else.
Each of these is its own real commitment, not a marketing line. Read the full detail behind any of them.
Privacy first
Every connector is opt-in, access is scoped to what a request actually needs, and deleting something means deleting it. Nothing here is aspirational; it's how the product is actually built.
Read the full policy →Our own infrastructure
tAI runs on servers we operate ourselves across multiple data centers, with our own failover and our own database replication. Nothing your data touches is rented from a third party.
See how it's run →Models built from scratch
Every version of tAI is trained end to end on data we collected and compiled ourselves. No third-party corpus was used as a base for any of them.
Read about our training →Small, and working across the whole stack.
We're a small team by design, spanning the parts of the stack that most AI products split across several vendors.
Research & training
Builds and maintains the ArtficalAI and Artfical Code Index corpora, runs training for every tAI version, and evaluates each release against our own benchmark suite before it ships.
Infrastructure
Runs the servers tAI is actually served from, our multi-region failover, database replication, and the sandboxed execution environment tAI's tools run in.
Product
Builds tAI and tCode themselves, the connectors to Gmail, Notion, GitHub, and Linear, and the documentation that explains exactly what each one does.
Security & safety
Red-teams every model before release, tunes safeguard thresholds as false-positive rates improve, and is the team you reach when something looks wrong.
How we actually make decisions.
Privacy is a constraint, not a feature
We check every new feature against what it means for your data before we check how well it performs. A feature that can't be explained plainly to a user doesn't ship until it can be.
Own the parts that matter
Models and infrastructure are the two things we refuse to outsource, because they're the two things that determine whether the privacy commitments above are actually true.
Ship, then tell you
New models and features go out to real users first. The announcements page is a running log of what actually happened, not a highlight reel written after the fact.
Ask before it matters
Connectors, integrations, and anything touching your data are opt-in, explained plainly, and as easy to turn off as they were to turn on.
Turkish first, not translated afterward
tAI is used heavily in Turkish. That's part of the training data from the start, not a localization pass bolted on after the English version is done.
Explain, don't just assert
"We take this seriously" is something every company says. We'd rather publish the specific mechanism, the connector docs, the permissions page, the privacy policy, and let you check it yourself.