Agentic development
We use AI at every stage of how we work, from the first day of planning to post-launch support. Our goal is simple: better software, delivered faster.
ISO
We’re ISO 27001 certified
4.5
years of client collaboration,
on average
EU
EU-based software development company
How we work with AI
We’ve implemented AI at every stage of our development process. Here’s what that means in practice:
Faster planning and product discovery
Before we build anything, we need to understand what we’re building and why. We use AI to build industry context quickly, map the market, and surface patterns across user research. That frees us up to spend more time on trade-offs, scoping, and important product decisions.
Quicker validation and prototyping
We use AI to get to a high-fidelity prototype much faster than before. Instead of spending weeks on prototyping, we can put something real in front of users and stakeholders in days. That means earlier feedback, fewer assumptions, and less risk of heading in the wrong direction.
Faster development, full control
Our engineers use agents to handle the repetitive parts of development: boilerplate, scaffolding, unit tests, and wiring layers of the architecture together. But the process is always engineer-led. We review the agent’s plan before any code gets generated, and our engineers stay fully responsible for the logic and the final result.
Strict reviews of AI-generated output
Nothing gets merged without human review, and nothing gets shipped without a person standing behind it. Our engineers check what was generated, challenge the output, and own the result. You always know what went into your codebase and why.
More thorough testing, less manual work
We use AI to generate test cases, write automation scripts faster, and draft test strategy documents. We’ve also built our own tools on top of that, like a Chrome extension that turns a two-sentence bug description into a complete report.
Smoother releases and support
There’s always a lot of work around a release. We use AI to draft release notes, changelogs, and handle the repetitive side of issue triage and post-launch support. We spend less time on admin and more time making sure everything is working and ready to go.
What that means for you
- Faster delivery, without cutting corners
- More time spent on decisions that actually matter
- Full visibility and control at every step
- Lower risk of building the wrong thing
How we use AI across the software development lifecycle
We use AI throughout the software lifecycle to reduce repetitive work and put more time into quality, product thinking, and better decisions.
In this article, we break down how that works across planning, design, development, testing, and post-launch support, and where agentic development fits into the way we build.
OUR AI TECH STACK
Platforms
OpenAI
Anthropic (Claude)
Gemini
Hugging Face
Llama
Frameworks
PyTorch
TensorFlow
LangChain
Streamlit
Databases
PostgreSQL
MySQL
Cassandra
Redshift
Vector databases
Pinecone
Qdrant
Chroma
Private AI for regulated industries
For projects with stricter privacy, security, or compliance needs, we can run AI models locally inside controlled infrastructure.
Spark is our dedicated AI workstation — equipped with an Nvidia GPU and 128 GB of RAM — built specifically for running and training models in-house.
That means your data isn’t sent to external providers or public AI APIs during experimentation, testing, or integration.
This allows us to prototype, train, and refine systems without exposing sensitive documents, internal processes, or proprietary information to third parties.
Find where AI makes real business impact
From identifying the right use cases to building and scaling AI-powered features, we integrate AI into complex software where it actually delivers value.
Start your project with an agentic development approach
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Arrange a chat with Ivor, our client strategy specialist.