EST. 2026
Move fast.
Stay standing.
We design, develop and run custom software and applied AI — built at the speed you need and architected to still run when the hype moves on.
01 / SOFTWARE
Applications people actually want to open.
Internal tools, customer portals and the integrations between them. We work from the process outward, so the software fits how your people already work instead of forcing a rewrite of their day.
- Internal tools and back-office systems
- Customer portals and self-service
- API design and integration between systems
- Replacing spreadsheets that became infrastructure
02 / APPLIED AI
AI that runs in production, not in a pilot deck.
Most AI work stalls between the demo and the day it has to be reliable. We start from the failure modes: what happens on a bad input, what a human reviews, and how you measure whether it still works next quarter.
- Agents and automation over real business processes
- Retrieval over your own documents and data
- LLM features inside existing products
- Evaluation and monitoring so quality stays measurable
03 / ARCHITECTURE
Order in the IT you already have.
When systems grow one urgent decision at a time, nobody owns the whole picture. We map what exists, name the constraints, and design the shape it should take — including the parts we would not build ourselves.
- System and integration architecture
- Security and access model review
- Technology choices and build-versus-buy calls
- Documentation your team can maintain without us
04 / HOW WE WORK
Four steps, and you own the result at every one of them.
No discovery phase that produces only a document. Every step ends with something you can look at, argue with, and keep — even if you stop working with us right after it.
STEP 01
Understand
We map the process, the data and the constraints, and name what is actually in the way. Usually it is not the technology.
STEP 02
Design
System architecture, technology choices and a scope we are willing to commit to. You get the reasoning, not just the conclusion.
STEP 03
Build
Short iterations with something running at the end of each one. You see the real thing early enough to change your mind cheaply.
STEP 04
Operate
Deployment, monitoring and handover. We write the documentation your team needs to run it without calling us.
05 / TECHNICAL APPROACH
What we build with, and what we refuse to build.
Technology choices are reversible for about six weeks and expensive forever after. Here is where we start, and the rules we apply when something wants to be more complicated than it needs to be.
PRODUCT
- TypeScript across front end and back end
- React and Astro for interfaces
- PostgreSQL as the default store
- Background jobs before microservices
AI
- Frontier LLMs through provider APIs
- Retrieval over vector and full-text search together
- Structured output and tool calling over prompt scraping
- Evaluation sets written before the feature ships
PLATFORM
- Cloudflare and managed runtimes over servers we babysit
- Infrastructure as configuration in the repository
- Observability from day one, not after the first outage
- One command to run the whole thing locally
- A system nobody but us can operate is a failure, no matter how well it runs.
- Boring technology at the core, interesting technology at the edges.
- If a feature cannot be measured, we do not know whether it works.
- We would rather delete code than add a setting to switch it off.
06 / WORKED EXAMPLE
What a system from us actually looks like.
Instead of showing logos, here is one worked example end to end: a document-heavy approval process that runs on email and spreadsheets, rebuilt as a system a team can operate itself.
ILLUSTRATIVE EXAMPLE — NOT A CLIENT PROJECT
- 01
Intake
Documents arrive by email and upload. A parser extracts structured fields and writes them to Postgres. Anything it cannot read with confidence goes to a review queue instead of failing silently.
- 02
Decision layer
Rules handle the cases that are genuinely rules. An LLM handles the ones that need reading comprehension, returns structured output, and always reports its own confidence.
- 03
Human review
Low-confidence items land in a queue with the source document side by side. Every override is stored, which is what later turns into the evaluation set.
- 04
Integration
Approved results are pushed to the systems that already exist — ERP, document storage, notifications. No parallel source of truth is created.
- 05
Operations
Dashboards for throughput, queue depth and model agreement. Alerts fire on drift in the review rate, not just on errors.
The point is not the layers we happened to name. It is that every one of them has a named owner, a failure mode, and a way to tell whether it is still working — which is the part that decides whether a system survives its second year.
07 / CONTACT
Tell us what is not working.
A short description of the problem is enough to start. We will come back with what we would do first and whether we are the right people for it — including when the answer is no.
or email us directly
hello@airchitects.cz