Dashboard with AI features
A dashboard that pulls data from several sources into one place, with AI helping you summarize, sort and draw conclusions.
Web apps · AI
AI web applications — from idea to a working product
We build custom web applications that genuinely fit your process, and where it makes sense we strengthen them with AI features. We start with an MVP so you see a working version fast, before you invest in the full product.
Free, no strings attached. At the end you get a PDF report breaking down your project.
The problem
Off-the-shelf tools can't keep up with your process
Boxed apps rarely fit 100%. You usually end up with workarounds, Excel exports and a few tools held together with duct tape. When you need your own features or AI support, a custom application becomes the only sensible option — but without an MVP it's easy to burn budget on features nobody will use.
- the tool doesn't support the way you actually work
- several apps stitched together by hand and exports
- missing AI features where they would genuinely help
- a big budget upfront, before you know what works
Who it's for
The biggest gains go to teams that lack their own tool
If you recognize any of these situations, a custom web application is probably worth considering.
- Startups and founders
- Companies building a digital product
- Ops teams that need their own tool
- SaaS
- E-commerce
- Service companies
Example applications
What we most often build
App with an AI assistant
A built-in assistant that answers users' questions based on your data and knowledge base, right inside the app.
Internal tool for your team
An application tailored to your team's real process — instead of workarounds in spreadsheets and several separate tools.
Client portal
A place where clients log in, check status, download documents and communicate with you without endless emails.
Data and document processing
An app that takes in files and data, while AI reads them, classifies them and extracts what matters most.
SaaS product MVP
The smallest working version of your product that you can put in front of users and investors before building it all out.
How implementation works
From an idea to a working product
01
Discovery and scope
We talk through the goal, the users and the problem to solve, then set the scope of the first working version.
02
UX and architecture design
We design the screens and flows along with the app's architecture, so it scales and grows well from the start.
03
Iterative MVP build
We build the smallest sensible version in short iterations, regularly showing you working results.
04
AI integrations, launch and growth
We add AI and data integrations, launch the application, and then grow it based on user feedback.
Technologies
The tools we build this on
- React
- Next.js
- Angular
- TypeScript
- Node.js
- PostgreSQL
- OpenAI API
- Docker
- AWS
Risks and limitations
We're upfront about where a project can go off the rails
Scope creep
Too broad a scope at the start is the most common way to burn budget. That's why we start with an MVP and add the rest step by step.
AI for its own sake
Not every feature needs AI. We add it only where it genuinely improves the product, not just to stay on trend.
Long time-to-market
A project built for months in secret easily drifts away from the market. We work iteratively and ship quick versions.
FAQ
Common questions about AI web applications
How much does an AI web application cost?
Cost depends on the scope, number of features, integrations and how heavily you use AI. The cheapest and safest way to start is with an MVP — the smallest working version. You can describe your idea in our estimation tool and get a preliminary breakdown into modules, effort and risks in a few minutes.
Do we start with an MVP?
Usually yes. An MVP lets you ship a working version fast, gather real feedback from users and find out what actually matters before you invest in the full product. That way you don't burn budget on features nobody uses.
Do you develop an existing application?
Yes. We can take over an existing application, add AI features, improve performance or clean up the code and architecture. We start with a review of what's already there and decide what can be extended and what is better rewritten.
What technologies do you use?
Most often React, Next.js or Angular with TypeScript on the front end, Node.js and PostgreSQL on the back end, and OpenAI API and similar models for AI features. We run everything in Docker and on AWS. We pick the stack to fit the project, not the other way around.
Next step
See how long your application will take
Describe your idea in a few steps and get a preliminary breakdown into modules, effort and risks. Then we can go through the report together.