Why ANTA exists

The market doesn't wait for you to catch up.

Every quarter you spend deciding whether AI applies to you, three competitors already shipped with it. ANTA exists to close that gap before it becomes permanent.

ANTA is an AI development studio, founded and run by Anadi. It designs and builds custom AI systems for growth-stage B2B companies, typically 5–50 people. That means internal tools, lead-generation engines, content pipelines and workflow automation. Engagements start as a fixed-price pilot sprint and the code is the client's from the first commit.

automating next time-less architecturedetroit, mi · overlaps etSee the shift ↓
01 / The adoption curve

Adopt, or get dragged out by whoever did.

This isn't a hype cycle. It's a market redraw that is already three stages in.

01Denial“It's a toy.” Pilots die in committee; nobody's job changes.closed
02ExperimentDemos, not systems. Impressive in the room, absent from the P&L.closed
03AdoptionCompetitors ship AI-native workflows to production. Their cost per unit of output falls. Yours doesn't.now
04Lock-inThe gap stops being a project and starts being an acquisition price.ahead
stage 03 of 04 · one exit: build the system while it's still a decisionScope the system →
02 / How we help

We build the system you'd build, if you had the time.

Custom AI software, workflows, and automation, built into how your team already works rather than bolted on top of it.

01

Custom AI software

Purpose-built tools and applications, not a wrapper on an off-the-shelf model.

02

Workflow redesign

We map how work actually moves through your team, then rebuild the slow parts around AI.

03

Automation

The repetitive, mechanical work your team already agrees shouldn't be manual.

04

Team enablement

Your people learn the systems as we build them, so you're not dependent on us to run them.

03 / Why ANTA

One process. No handoffs. No delay.

01

Design

Scope the real bottleneck, not the requested feature.

02

Architect

A system that still stands after the tooling under it changes twice.

03

Build

The same team that scoped it writes it. No handoff, no drift.

04

Ship

First deploy in weeks. Yours from the first commit.

05

Automate

Every deploy feeds the next one. The system keeps compounding after we leave.

04 / FAQ

Questions that come up before the first call.

How much does it cost to build a custom AI tool?

A first engagement is a fixed-price pilot, typically $3,000–$6,000 for two to three weeks and one narrowly scoped system. Ongoing work after that runs as a monthly retainer rather than an hourly rate, because hourly bills you for our learning curve and invites scope creep. You get the price before we start, not a range that moves.

How long does it take to build a custom AI tool?

Scope is written in days and a first deploy typically lands in two to three weeks. That's a real system in production against your data, not a demo. The pilot is deliberately narrow so it ships, gets used, and proves the case before anyone commits to a larger build.

Should we hire an AI engineer or work with a studio?

Hire when you already know exactly what to build and will keep building it for years. Bring in a studio when you don't yet, or when it's one system rather than a roadmap. A senior AI hire is a six-month search and a permanent salary line before anything ships. A pilot puts a working system in front of your team in weeks, and if it justifies a hire, you'll be recruiting against a spec you've already validated instead of a guess.

Who owns the code and the data?

You do, from the first commit. The repository is yours, it runs in your accounts on your infrastructure, and your data never becomes part of anyone else's product. There's no per-seat licence, no platform to stay subscribed to, and nothing that stops working if we stop working together.

Do you build on existing AI models, or train something custom?

We build on frontier models like Claude and GPT, through their APIs. The engineering goes into everything around them: your data, your rules, your workflows, and the integrations into tools your team already uses. Training a model from scratch is almost never the right answer at this size. The value sits in the system around the model, and that's where the work goes.

What does a first engagement actually look like?

A short call to find the one workflow worth automating first, a written scope with a fixed price, then two to three weeks to build and deploy it with your team using it as we go. You end up with a working system, the repository, and enough evidence to decide whether there's a second one worth doing.

We're not big enough to need this yet.

Size isn't the trigger. Repetition is. If work is repeating manually somewhere in your team, it's already worth automating. Waiting for scale just means automating a bigger mess later.

How is this different from hiring an agency?

No account manager translating your problem into a statement of work. You talk directly to the people building it, scope moves in days, and the repository is yours from day one.

What if we're locked into legacy tools?

We build around your existing stack, not against it. Most engagements start by wiring AI into tools you already use. The legacy layer is the constraint, not the blocker.

Will our team actually use it?

We train your team as we build, not after. Nobody inherits a system they don't understand.

How fast can we actually start?

Scope is written in days. First deploy typically lands in weeks, not quarters.

Move before the gap closes.

Send the two-paragraph version of your problem. You'll get a real technical response, not a discovery questionnaire.

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