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AI-first · AI-native engineering
AI built into the systems you already run
We add AI to .NET and JavaScript applications, and we build with AI every day. Fast PoCs and MVPs to validate your business, new features shipped faster, legacy code rewritten, all within your security rules.
Real example · Fintech
Germany
A proof of concept in 1 month. Others quoted 6.
A fintech client from Germany wanted to test a new product idea. Every other vendor quoted around six months. Building with AI from day one, our team delivered a working PoC in one month. Two months later, the client was already making money from it.
AI-first, AI-native
We build with AI in practically every project
Our engineers work with Claude and Cursor every day. It nearly doubles their performance, cuts delivery time by up to 40% with the same savings on cost, and makes the whole process smoother. Senior people stay in charge of architecture, quality and security.
- 01
Fast PoCs and MVPs
Validate your business idea on real data in weeks, before you commit a full budget.
- 02
Features shipped faster
AI-assisted development lets our senior engineers add new features to your product faster.
- 03
Code rewritten and modernised
We use AI to rewrite and refactor legacy code, with senior engineers reviewing every change.
- 04
Security first
We agree with you which tools, models and data can be used, and keep your code and data within your security rules.
Use cases
Where AI pays off first
Assistants on your own knowledge
Chat and search that answer from your documents, wikis and tickets, with sources, instead of making things up.
Document processing
Pulling structured data out of PDFs, emails and forms, and routing it into your systems.
Workflow automation
AI steps inside existing processes: triage, classification, drafting replies and summaries for your teams.
AI in your product
New AI features for your own users, built into your .NET or JavaScript application.
Proof of concept
A small, working version on real data so you can judge the value before committing budget.
How we work
Start small, ship to production
Most AI projects fail between the demo and production. We plan for the boring parts from day one: data access, security, testing and cost.
- Workshop: pick one use case with measurable value.
- Proof of concept on your real data.
- Integration with your systems, users and access rules.
- Testing for quality, safety and cost per request.
- Monitoring and improvement once it is live.
Technology
What our AI work is built on
How we work with you
Pick the model that fits
Start small, prove the value, then scale.
Proof of concept
Best place to startOne use case, your real data, a working result you can judge.
- Billing
- Fixed fee
- Timeline
- Weeks, not months
- Best for
- Validating an idea before a budget
Team Extension
Engineers with AI experience join your team to build AI features into your product.
- Billing
- Monthly, Time & Material
- Start
- In about 2 weeks
- Best for
- AI features in an existing product
Dedicated Team
A complete team that takes an AI product or feature from idea to production.
- Billing
- Monthly, Time & Material
- Team
- Developers, analyst, QA, PM
- Best for
- A new AI product or MVP
FAQ
Questions we hear most
Can you add AI to our existing .NET or JavaScript application?
Yes. Most of our AI work is adding AI features to systems that already run the business, not building separate experiments.
Do we need our own data scientists?
No. For most use cases proven language models are the right starting point. Our job is to connect them safely to your data, systems and users.
Which AI tools do your engineers use?
Claude and Cursor in practically every project, plus Azure OpenAI and other LLM APIs where your product needs them. We agree with you up front which tools can be used on your code.
Is our data safe?
We design for your security rules from the start: access control, logging and, where possible, setups where your data stays in your own cloud environment.
How do we start?
With a short call and a workshop to pick one use case with clear value. Then we build a working proof of concept before you commit to a full build.
How do you charge for AI projects?
Proofs of concept are usually a fixed fee. Building and running AI features in production works like any other engagement: Team Extension or a dedicated team, billed monthly.
Security & trust
Ready for your procurement and security review
We work with companies like Cisco and EF Education First, so the paperwork is part of the job. Whatever your policies require, we sign and follow.
NDA, before anything else
We sign your NDA before you share anything about your project.
Data Processing Agreement
A GDPR-compliant DPA whenever we process personal data on your behalf.
Professional liability insurance
Our work is covered by professional liability (OC) insurance, with details for your procurement team on request.
Your hardware, your accounts
If your security policy requires it, our engineers work only on your laptops, VPN and accounts.
Certified engineers
Most of our engineers hold Microsoft, AWS, Google Cloud or Claude certifications.
AI on your terms
We agree up front which AI tools and models can be used on your code and data, and nothing else is.
Verified on Clutch
Fast, open and honest about what works
Rated 5.0 from 28 verified reviews.
“Their flexibility is a large advantage.”
Verified review on Clutch · 5.0 · API & custom software
“They were communicating openly with us and shared their knowledge eagerly, advising on the best solutions.”
Verified review on Clutch · 5.0 · Mobile app
“We had a good experience, thanks to their open-mindedness and outstanding ideas.”
Verified review on Clutch · 5.0 · Staff augmentation
Let’s talk
Have an AI idea, but not sure where to start?
Book 30 minutes with me. Tell me which process or product you want to improve, and I’ll tell you honestly whether AI is the right tool for it.
or email radek@appstronauts.co