What we build
What we don't build
Stacks we often work in: Python/FastAPI, Node/TypeScript, Postgres/pgvector, LangGraph — Azure OpenAI or bring-your-own-key.
Advisory, execution and upskilling — for senior dev teams that need AI in production.
One concrete challenge. We map the options and build a proof-of-concept in your stack — so you know whether it makes sense before you commit.
Typical delivery: architecture sketch, POC in your repo, eval on your documents, written go/no-go.
Most popular
2A senior consultant works in your team and builds production features together with your devs — matched by AIPOPS to your stack. RAG, agents and LLM integration in the code you already have.
Strategic AI leadership combined with hands-on execution. Two parallel tracks, architecture review and sparring with your leadership — without the full-time hire.
Everything is billed by the hour — no hidden package prices. We agree on expected hours and scope before we start.
Hourly rate
1,200–1,500 DKK/hour
excl. VAT · depending on task and consultant
Hours are logged continuously and settled as agreed — typically monthly. You get visibility along the way, not a surprise at the end.
Short scope, expected hours and who delivers. AI Sprint gives you a go/no-go before you invest more time.
Ongoing engagements can be ended by agreement. You only pay for hours spent.
From first chat to code in your repo.
A short chat about your RAG or agent case, stack and compliance requirements.
2–3 weeks. POC in your repo, eval on your data, written go/no-go.
Production features together with your team — you own the code along the way.
Everything lives in your repos. Operations and further development stay with you.
A track beside the sprint path. Strategic lead and two tasks at a time, also billed hourly.
Apps and products we have built ourselves.
iPhone and iPad only.
A weekly overview and reminder list for parents who use Aula. Not developed by or affiliated with Aula or KOMBIT.
A private diary on the iPhone. Photos, places and calendar come together in one day, and stay on the device.
A habit tracker. One square a day. Habits stay on the device and in your own iCloud.
Local-first project and task management — everything is saved as files in your own folder, no database on our side. The same privacy and architecture thinking we use in enterprise RAG and agent projects.
AI for people who do not work in software. Shows that an LLM feature can run in production.
Describe your RAG case, stack and compliance requirements. You'll hear from us within 24 hours.