// APPLIED AI — IN DELIVERY

Not everything we build walks.

Alongside our robotics roadmap, Zanier runs an applied-AI practice: computational engineering systems, natural-language software, and AI enablement for client engineering teams. This work is funded, in delivery, and in customers' hands.

STATUS LEGEND :: IN DELIVERY IN DEVELOPMENT PRE-LAUNCH

IN DELIVERY 01 / 03

Computational Engineering

Design problems, solved by machine.

Some manufacturing problems are too large to search by hand. The design space runs to millions of variants, the constraints fight each other, and the best answer is not the one an experienced engineer would reach for first.

We build computational engineering systems for those problems. Geometry is generated programmatically, scored against physics — thermal, structural, manufacturability — and iterated until it converges. The loop is deterministic by design. Engineering geometry has to be verifiably correct, not plausible.

AI sits around that loop, not inside it. It builds the engine under phased, human-verified checkpoints. It turns domain handbooks and manufacturing rules into verified constraints the engine enforces. And it fronts the system in plain language, so an engineer can describe what they need in their own words and drive it without touching CAD.

Each system is built for one problem, in one process, for one manufacturer. We are currently in delivery with an industrial client, including training for their engineering team.

PRE-LAUNCH 02 / 03

Kritify

Software in your own language.

An application builder for people who do not code and do not work in English. Describe what you need in your own language; Kritify builds it.

Small businesses across India run on WhatsApp messages and paper registers because the tools that would serve them are written for English-speaking developers. Kritify removes both requirements — no technical background, no English, no developer.

Pre-launch. Preview at kritify.one.

IN DELIVERY 03 / 03

AI Enablement

We train the team that inherits the system.

Every engineering system we deliver comes with the training to run it. We work with client engineering teams on the practical use of AI in technical workflows — where it belongs, where it does not, and how to verify what it produces.

Currently delivered as part of client engagements. Being built out as a standalone practice.

// BRING US A PROBLEM

Tell us what you make.

If you manufacture something and the hard part is the design — a space too large to search by hand, constraints that pull against each other, a process that currently depends on one experienced engineer and a lot of time — that is the kind of problem we build for.

We do not sell a product off a shelf. We start with a technical conversation about your process, and build the system around it.

Our robotics roadmap is a long-range build. This practice ships now. The same eleven people do both.

Talk to Engineering

Or write to hello@zanier.one with the problem in your own words.