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About this service
Build scalable, maintainable software with our experienced engineering teams. We deliver high-quality code and best practices that help your product succeed.
Go to serviceAI engineering
Build with AI. Build for AI.
We help engineering teams get the most out of AI: in how they build software, and in the products they build.
AI in your team's workflow
AI in your product
Engineering, not magic
AI is an engineering problem, not a magic trick
We do two things with AI, and we treat both as engineering. Build with AI: our staff engineers and fractional CTOs help your team make AI part of how it works every day, so you ship faster without losing control of your codebase. Build for AI: we lay the data foundations and build the retrieval, LLM and machine-learning systems that make AI a real product feature, not a fragile marketing project.
AI in your team's workflow
Build with AI
AI won't magically fix or replace a team. Used well, it removes friction and gives good engineers the leverage they deserve. Used badly, it turns your codebase into AI-generated duct tape. We've seen both — our job is to get you the first one. Our engineers and fractional CTOs build with AI daily on real products, and we bring that practice to your team by embedding, pairing and shaping the workflow around your standards.
Coding agents with intention
Coding agents and AI editors as real productivity tools, not hype-driven autocomplete. Agents that take refactors, scaffolding and migrations off your team's plate.
AI code review done right
Review bots that catch the boring stuff consistently, so human reviews stay focused on architecture, correctness and intent.
Documentation that sticks
Documentation that gives every AI tool the context it needs, so knowledge stops evaporating the moment someone goes heads-down on a feature.
Clear boundaries
What belongs to AI and what stays human, where the guardrails live, and how to review AI-generated code without lowering your standards.
A workflow, not a toolbox
Healthy AI adoption is a workflow problem, not a tooling problem. We don't drop tools on your team and hope — we embed, pair and adapt to how you work.
Long-term code health
We prevent the “six months later everything feels cursed” syndrome. The outcome is a team that ships faster without losing control of its codebase.
AI in your product
Build for AI
AI products don't fail because the model is stupid. They fail because the data was wishful thinking, the plumbing was an afterthought, or the workflow was patched together with duct tape. So we start where it actually matters: your data. From there, we take you from “we'd like to do something with AI” to “customers rely on this every day”.
A data foundation you can trust
Reliable AI only works when the underlying data behaves predictably: data you can explain, trust and audit, pipelines that run the same way everywhere, and ownership that survives handovers. We get models out of notebooks and into production, and we are not religious about the stack — the system has to be understandable, observable and owned by the people who will live with it.
From idea to shipped AI feature
Retrieval, agents, classification or generation — we make the right calls early (what problem, what signals, what “good enough” looks like) and ship with the evals, fallbacks and prompt-injection hardening that most teams skip. Measurable, monitored, and never hand-waved because the demo worked once.
Concrete outcomes, not just code
Building for AI comes down to four areas we deliver measurable improvements in, for your product and the team that owns it.
Data pipelines that work
Ingestion that does not lie. Pipelines that run everywhere, not just on your laptop. QA gates that catch issues before they contaminate your roadmap.
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Ready to talk?
Ready to stop fighting your data infrastructure?
Classic machine learning
When you need prediction, classification or recommendation, we help you choose the right approach, build the training pipeline and deploy models that actually work in production.
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Ready to talk?
Want to put proven ML to work for your product?
RAG & LLM systems
If your product needs real-time knowledge or smart retrieval over messy corpora, we build RAG systems, vector databases, hybrid search and evaluation loops that work outside the demo.
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Ready to talk?
Looking to build AI features that work beyond the demo?
Monitoring & drift detection
We treat retrieval quality with the same seriousness as data quality: measurable, monitored, never hand-waved just because the prototype looked clever.
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Ready to talk?
Want confidence that your models keep performing in production?
Our approach
“We bring proper engineering practices so everyone can work with it. By getting stuck in alongside your engineers, we turn messy data into something you can rely on.”
Why companies call us in
Models stuck in notebooks? Pipelines that keep breaking? Wondering how to actually get AI into your product? You're not alone.
“We want to add AI to our product but don’t know where to start.”
“Rolling out new features takes too long.”
“We now have stable and performant products that users love, so the impact of the collaboration is well proven.”
Sven Houtmeyers, Technical Lead (CTO) at Publiq
“Our product is full of bugs.”
“We need to scale now.”
“The madewithlove team works alongside us, treated our product with the love and attention it deserved and actively challenged us throughout the entire product development process.”
Michelle Dassen, Head of Product at Flexmail
“We have data but nobody is using it to make decisions.”
“Our best engineers are drowning.”
Happy clients
We work with companies that want to get their data and AI right. Here are examples of how our approach translated into real results.
Fixform
Keeping buildings and spaces in good, safe and clean condition
After their technical co-founder departed unexpectedly, we stepped in with two staff engineers to build a quality-first MVP using Inertia.js, Vue, and Laravel. We established robust CI/CD pipelines, implemented comprehensive testing, and mentored the team through knowledge transfer.
Read case study
We help our clients succeed
You will not see companies like Amazon among our past clients. You will, however, see the names that will soon rock the SaaS world because we helped them predict risks and avoid failure.
“We enjoyed working with madewithlove for the qualitative feedback on our development flow, velocity, and attention to details they brought in our engineering team.”
Joel Harkes
Technical Team Lead, Vaigo
“Madewithlove truly has your back as a trustable business and tech partner. They are a great combination of integrity, people and technical skills that allow you to move your software application project forward at any level.”
David Macfarlan
CEO, GearJot
“We now have stable and performant products that users love, so the impact of the collaboration is well proven.”
Sven Houtmeyers
Technical Lead (CTO), Publiq
FROM 190+ SAAS AUDITS
89% ship without automated testing
Manual QA dominates at every stage, seed to M&A.
Why QA becomes the bottleneckFROM 190+ SAAS AUDITS
51% mishandle credentials in the codebase
The finding investors react to hardest.
The security hole nobody talks aboutLatest insights
Our latest thinking on data engineering, AI integration, and building reliable ML systems.
Blog
A back-end engineer can do what now?
3 min read
Read article
Blog
Ask Claude what you're not tracking
4 min read
Read article
Blog
The integration loop
3 min read
Read article
We can help you build with AI
Whether you need to build reliable AI features, fix your data pipeline, or help your team use AI effectively. We're ready to dig in.
Frequently asked questions
Everything you need to know about working with our data and AI engineers.
No. You need good enough data that is owned, understood and monitored. Perfection is a myth. Predictability is the goal.
Usually yes. We identify what is salvageable, what needs guardrails and what must be replaced. Most teams are not one rewrite away from salvation; they are one clear ownership model away.
The one your team can maintain. We choose tools based on your skills, constraints and long-term reality, not fashion. Python, Airflow, Spark, Snowflake or something simpler: the logo is never the point.
We help you define measurable quality thresholds, check for drift and establish pipelines that make data issues visible instead of silently passing incorrect signals into your model.
Yes. We look at your problem, the available signals, expected latency, accuracy needs and cost. Sometimes a simple model is enough. Sometimes you need RAG or vector search. Sometimes you need neither.
By setting clear rules. We help teams define what belongs to AI, what needs human review and how to keep codebases coherent when half the suggestions come from a model.
It ingests data reliably, rejects garbage, is reproducible end-to-end, has proper monitoring and makes failures loud. If it only works on one engineer's laptop, it is not healthy.
Yes. We work incrementally: stabilise the data, design a minimal pipeline, add monitoring and ship features in thin slices. You do not need a full AI overhaul to start delivering value.
The same way we measure data quality: with metrics, tests and evaluation loops. A demo that works once is not a quality signal. We make retrieval measurable and observable.
Yes. We plan our exit from day one. We document, pair-programme, build with your stack and transfer full ownership. No mysterious pipelines. No ghost systems. Your team keeps shipping long after we are gone.