Approach
The shortest route to working AI.
Fixed scope, evaluation on your real data before go-live, and a team on your side trained to keep the system alive and growing. Built so the value shows up in the daily routine, where it counts.
Step 1
Map
We sit with the people who run the workflow today and map it honestly: inputs, decisions, exceptions, volumes, and the hours it eats.
Step 2
Architect
We design the smallest system that removes the manual load: which steps agents handle, where humans stay in the loop, where your data lives.
Step 3
Prove
Before go-live, the system is evaluated on your real data against criteria we agreed in writing. If it doesn't pass, it doesn't ship.
Step 4
Ship
It goes into your stack, under your accounts. The engagement ends with training: your team runs it, extends it, and owns it.
Fixed prices, stated up front.
The whole ladder is public: Blueprint EUR 4,900, builds from EUR 14,900, rollouts from EUR 25,000, managed operations from EUR 1,900/month. Fixed numbers, invoiced at project start, never a running meter. And every Blueprint states what the system will cost to run after go-live, so there are no surprises on either side of launch.
Training is part of every package.
AI systems rot when nobody in the building understands them. Every engagement ends with a handover module: a working session with the people who operate the system, a runbook they keep, and 30 days of follow-up questions included.
Dogfood
This website is our own case study.
mapular.ai was built the way we build for clients: agent-assisted development, structured data on every page, machine-readable content for AI search, and evaluation before launch. Our own growth engine runs on the same agent workflows we sell in the marketing automation package.
We publish how, because a company selling working AI should be its own first reference.