● Applied AI engineering studio

97 projects delivered

We bring AI into business processes

From hypothesis to production — with metrics, not a demo reel

Discuss your task → View work
CH1 · process signal Click the line to send an impulse
30–60%
cost reduction in typical scenarios
6–12mo
typical payback period of an implementation
97
projects delivered

01 — Services

Five places where AI pays off fastest

We don't sell “AI implementation” in general. We find a process where a model replaces a measurable amount of manual work, and take it all the way to production

01

Customer communication

Automated conversations, first-line support, request routing

02

Content creation

Text and visuals generated inside an editorial workflow — with review and brand guidelines

03

Document processing

Extracting and processing data from invoices, contracts, forms and scans

04

Analysis and quality control

Pattern recognition, detecting defects and deviations in the flow

05

Forecasts and decisions

Recommendations and decision support built on historical data

02 — How we work

Four phases to production

Every phase ends with an artifact you can verify: a number, a prototype, a pilot report, a running service

01WK 1–2

Hypothesis

We break down the process and count manual hours and the cost of errors. The output is an impact estimate and stop criteria

02WK 3–5

Prototype

We build a working pipeline on the client's real data. Quality is measured on a held-out sample

03WK 6–12

Pilot

We run it on part of the flow alongside the current process. We compare metrics, not impressions

04NEXT

Production

Integrations, model quality monitoring, rollback procedures and handover to the client's team

03 — Selected work

What we've already put in people's hands

Eleven projects in production — from a keyboard that recognizes emotions to AI-written lines for a coffee chain. Full write-ups on request: some of the work is under NDA

01EdTech · LLM

Folio

An adaptive AI editor for learning content. Built and launched in six weeks
02Mobile · NLP

Keemoji

An emotion-recognition model inside iOS and Android keyboards. The company's valuation grew from $20M to $200M+
03E-com · GenAI

Coverline

AI generates product cards and creatives for marketplaces — no manual layout for every item
04EdTech · LLM

Heritage School

An AI platform for teacher reports, lesson transcription and timetables. Freed up 3–4 hours a week per person
05Real estate · AI agent

RED

AI qualifies leads from messengers and passes them to the CRM. Freed up about a fifth of the team's working time
06Agriculture · Voice AI

Agrohelper

A voice AI robot calls farmers and prioritizes leads: up to 30,000 calls per organizer a day
07Quality control · CV

Monitor

AI computer vision watches work processes on ordinary cameras — deviations show up the moment they happen, not in a monthly report
08Retail · CV

SizeMyShoe

A computer-vision model measures a foot from one ordinary photo — the right size without trying shoes on
09Aerospace · LLM

Altus

A language model turned hundreds of pages of technical documentation into the product catalog of an aerospace manufacturer's website
10Brand · GenAI

Storee Coffee

An AI copy generator in the brand's voice for a coffee chain — new packaging ships without a designer or a copywriter
11Fintech · ML

Quidly

An ML model scores microloan applications and picks the lenders most likely to approve — answers in minutes

04 — Notes

What we learn in the field

Short write-ups on choosing processes, building pipelines and getting models into production. All notes →

Why AI pilots stall — and five things that get them to production

A successful demo is not a successful pilot, and a successful pilot is not a product. Five things that separate AI projects that reach production from the ones that stay on a slide.

Delivery4 min read

Voice AI that gets through: lessons from calling farmers

Some audiences don't answer email or fill in forms — they pick up the phone. What we learned building a voice AI robot that holds natural conversations with farmers and hands organizers only the leads that matter.

Voice AI4 min read

From engineering PDFs to a live catalog with an LLM

How we use language models to turn hundreds of pages of technical documentation into a structured product catalog — and why engineers stay in the loop for every description.

LLM · Data4 min read

05 — Contact

Tell us about a process that's expensive to do by hand

We'll tell you whether there's work here for a model and what it takes to put numbers on it. The first conversation comes without a sales pitch

Send a request →