
Business owners and operations
Companies running on Excel, email and off-the-shelf software.
Owner of a company that grew up on Excel
Everyone has their own Excel and nobody knows which version is right.
How we solve it: We build one system in place of the spreadsheets, owned by you, with no per-seat fee.
Example: A freight forwarder worked in a shared spreadsheet; today it works in a system it owns. Case study
Whoever handles the invoices
We retype invoices from email into accounting by hand, every month.
How we solve it: AI pulls the data from each invoice into accounting; a person checks only the exceptions.
Example: Our own invoices: from 10-15 minutes to under a minute, 70 % with no person involved. Case study
A company with accounting software and other systems
Accounting cannot see the warehouse or the shop, so we copy data by hand.
How we solve it: We connect your accounting software to your other systems via API, so nothing is retyped.
Example: Our invoices go from Gmail into accounting via API, 70 % with no person involved. Case study
A wholesaler or manufacturer taking orders by email
Orders arrive by email and someone spends the day retyping them.
How we solve it: An agent reads each order, enters it in your system and sends unclear cases to a person.
Example: The same approach runs on our own invoices from email: under a minute instead of 10-15. Case study
A freight forwarder
Off-the-shelf forwarding software does not fit how we work, so we patch it in Excel.
How we solve it: We build a system around how you work and run it; the code and data are yours.
Example: We built and run the freight forwarder KIABA its own system. They find a carrier in under 10 minutes, down from 30 to 45 minutes. Case study
A company locked into a SaaS or a vendor
The vendor raised the price, will not release our data, and we cannot leave.
How we solve it: We get the data out, move it into a system you own and switch the old one off only once checked.
Off-the-shelf software cannot do what makes your company different, and you pay for features you never use.
A company collecting prices or data from the web by hand
We copy competitor prices and supplier catalogues from websites by hand.
How we solve it: We build a crawler with monitoring that alerts you when it stops bringing in data.
Example: A client with an online marketplace lost its crawler: from 250,000 to 66,000 pageviews a month. Case study
An online seller with a full inbox
We answer the same customer questions by hand, hours every day.
How we solve it: An AI agent answers routine emails from your data and hands harder cases to a person.
Example: A client with online sales: a reply within 5 minutes, customer support now handles only the complex cases. Case study
CTOs and development teams
Companies with their own product and their own developers.
A CTO with a growing backlog
We are not shipping fast enough and the backlog grows faster than we close it.
How we solve it: A senior joins your repository and pull request flow from week one.
Example: Filip delivered AI features for a SaaS that 60,000+ people used daily. Case study
A CTO or tech lead
Every new feature takes longer because we are afraid to touch the old code.
How we solve it: We pay the debt down in slices alongside the roadmap, with tests, not one big rewrite.
15+ production projects since 2023.
A CTO facing a big call
We do not know whether to fix the old system or rewrite it from scratch.
How we solve it: We review the code, recommend a refactor or a rewrite in parts, and take the first step.
Example: For a fintech company we rewrite reports in parts: a new screen first shows the same number as the old one. Case study
A CTO whose team already uses AI
The team has Copilot and Claude, but pull requests bring more noise than results.
How we solve it: We set up the rules, review and automated checks so AI code reaches production safely.
Example: A workshop for a software team: real backlog work with AI agents, from the brief to a working prototype. Case study
Founders of a digital product
A first product, a tight budget and an investor asking for a date.
A founder before the first version
I need to know when the MVP will be ready and what it will cost.
How we solve it: Together we write a list: what ships first, when, and how you can check it.
One project: the whole system live within a month.
A founder with a prototype built in Lovable or Cursor
The Lovable prototype works for a demo, but I would not put customers on it.
How we solve it: We take over the prototype, add security, data and tests, and get it into production.
Example: An audit of an app a founder built with AI. The first sentence of Filip's verdict: "None of this is Armageddon." Case study
A founder whose agency did not deliver
The agency promised an MVP in weeks; months later we only have a Figma file.
How we solve it: We find out what of the work so far is usable and ship in weeks, not months.
One project: the first version in under 10 working days.
A founder whose vendor is leaving
Our vendor is quitting and nobody else understands the code of our app.
How we solve it: We take over the code, data and infrastructure, document them and keep building.
You can leave us too: when you do, we fix the bugs from the last month.
How we pick what to solve first
Recognised your problem?
Tell us what is holding you back. We get back to you within two business days, and together we pick what to fix first.