Our work covers both sides of the equation: helping you build reliable AI products and helping your engineering team use AI safely and effectively. We treat AI as an engineering problem, not a magic trick.
Making AI features real, reliable and shippable
Most AI products collapse long before a model ever sees production. A clever model on top of messy, drifting, unowned data becomes a maintenance nightmare. So we start where it actually matters: your data.
Reliable AI only works when the underlying data behaves predictably. That means data you can explain, trust and audit, supported by ingestion that does not quietly swallow garbage on a bad day. It also means pipelines that run the same way everywhere, not just on the machine of the one engineer who remembers how it works, and ownership that survives handovers, holidays and the inevitable team change.
We help you establish what "good enough" actually means for your product and create the monitoring and processes to recognise the moment when your data stops being good enough. The aim is not perfection. The aim is stability you can build on without crossing your fingers.
We are not religious about tool choices. Your constraints, team and maintenance reality determine the stack. Depending on your situation, that might mean:
The logo does not matter. What matters is that the system is understandable, observable and owned by the people who will live with it.
We take you from 'we'd like to do something with AI' to 'customers rely on this every day'. That means making the right decisions early and building the boring-but-essential machinery around it.
Ingestion that does not lie. Pipelines that run everywhere, not just on your laptop. QA gates that catch issues before they contaminate your roadmap.
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.
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.
We treat retrieval quality with the same seriousness as data quality: measurable, monitored, never hand-waved just because the prototype looked clever.
AI as leverage, not chaos
AI will not magically fix (or replace) a team. But 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 have seen both. Our job is to get you the first one.
We help integrate productivity tools like Cursor or Windsurf with intention rather than hype. CLI agents that handle refactors, scaffolding and migrations.
Bots that catch the boring stuff consistently, so human reviews stay focused on architecture, correctness and intent.
Documentation helpers so knowledge stops evaporating the second someone goes heads-down on a feature.
We define what belongs to AI and what must stay human, where guardrails live, and how to review AI-generated code without lowering standards.
We embed with your team, pair, and actively shape the workflow around your standards. Healthy AI adoption is a workflow problem, not a tooling problem.
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 tools that help your team, not slow them down.
AI becomes valuable when it quietly removes friction instead of becoming a novelty toy everyone pokes at for a week. We help integrate real productivity tools like Cursor or Windsurf with intention rather than hype.
We set up CLI agents that take refactors, scaffolding, migrations and boilerplate work off your plate. We add PR review bots that catch the boring stuff consistently, so human reviews stay focused on architecture, correctness and intent. We even integrate documentation helpers so knowledge stops evaporating the second someone goes heads-down on a feature.
This is not about replacing engineers. It is about protecting their flow, their craft and their ability to build well.
Healthy AI adoption is a workflow problem, not a tooling problem.
We embed with your team, pair, and actively shape the workflow around your standards. We help you define:
The outcome is a team that ships faster, learns faster and does not lose control of its own codebase.
Whether you are building AI features into an existing SaaS or launching an AI-first product from scratch, we bring senior product and engineering judgement to the full chain.
We work across the modern data and AI ecosystem, from lightweight Python pipelines to enterprise-scale data warehouses. Here's what we use to build reliable data products.
Hover or click a technology to see details
We work with companies that want to get their data and AI right. Here are examples of how our approach translated into real results.
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.
Contract management simplified through refactoring, not rewriting
We were the only party advising them to refactor instead of starting over. Over 18 months, we modernized their PHP/Laravel stack, established CI/CD pipelines, migrated infrastructure to DigitalOcean, and dramatically reduced bug rates while mentoring their growing team.
One app to run your real estate agency
We helped develop their comprehensive platform through API-first development in PHP/Laravel, enabling real estate agents to manage listings, engage leads, and automate matching. We established the technical team from the ground up with pair programming, code reviews, and comprehensive documentation.
Whether you need to build reliable AI features, fix your data pipeline, or help your team use AI effectively. We're here to help.
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.
Read our latest thoughts on data engineering, AI integration, and building reliable ML systems.
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