Artificial Intelligence & Machine Learning
We help teams turn a business need or a working prototype into a model that runs every day on real data. The first step is checking that machine learning is the right tool at all. Then we build the data pipeline and the monitoring you need to trust its predictions once it is in production.
You need this if…
- Your model works in a notebook, but nobody knows how to get it into production.
- A model you already deployed is getting worse and you can’t tell why.
- You are weighing a custom model against an LLM API or a set of plain business rules.
- You need to catch rare cases, like fraud or defects, buried in mostly normal data.
The problem
A model that works in a notebook may fail in production. Input data changes, latency constraints become critical, and performance needs to be measured over time. Moving from a demo to an operational ML system requires additional engineering.
Who it's for
Teams with a promising model to deploy, or a business problem for which machine learning may be appropriate.
What you get
A deployed, monitored ML system with a feature pipeline, an evaluation framework, and impact measurements that support continued improvement.
What we deliver
Feasibility assessment
A review of your data and the problem, ending in a clear recommendation: a model, an LLM, rules, or nothing at all. It includes a first baseline measured on your own data.
Feature pipeline
Code that prepares data the same way for training and in production. Mismatches between the two are a common reason a model that tested well falls short after launch.
Deployed, versioned model
A model served through an API or a batch job, sized for your latency and cost constraints, with versioning and a way to roll back.
Evaluation and monitoring
A frozen test set, metrics chosen around what each kind of error actually costs, and alerts on data drift. You can spot a decline without waiting for a user to report it.
How we work
Define acceptable error
We turn the business goal into a decision the model has to make, and work out with you what a false alarm costs compared with a missed case.
Beat a simple baseline
Before any complex model, we measure a simple approach such as business rules or logistic regression. The final model has to clearly beat it to justify the added complexity.
Run in shadow mode
We integrate the model into your system, then run it alongside the current process to compare its decisions on real data before it acts on anything.
Monitor and retrain
We track drift and performance, and document a retraining procedure your team can run on its own when the data changes.
Frequently asked questions
Do we need an LLM to do AI these days?
Not necessarily. For predicting, classifying, or scoring from structured data, a classic model is often a better fit: cheaper to run and easier to explain. An LLM earns its place when the problem involves free text or language. Many systems combine both, and sometimes plain rules are enough.
How much data do we need?
It mostly depends on how hard the problem is and how rare the cases you care about are. Rare events need a longer history to collect enough examples. Reliable labels often matter more than sheer volume. The feasibility assessment exists to check exactly this on your own data, before you invest in a model.
What happens when the model degrades over time?
That is expected: customers and their behavior change, and the data changes with them. Drift monitoring flags when inputs move away from what the model saw in training. Depending on the cause, the fix is retraining on recent data or revisiting the features and the decision threshold. Plan for this upkeep from the start.
Our other services
Custom Software Development
Production software with tested code, documented architecture, monitoring, and a handover your team can use.
Technical Architecture
Architectural decisions shape data flows, service boundaries, and the platform’s ability to evolve.
Research & Development
Use focused prototypes and measurements to assess technical feasibility before committing to development.
Automation & Integration
Connect existing systems and automate the manual transfers between them.
Technical Consulting
Technical advice on architecture, hiring, and technical due diligence decisions with lasting implications.
Not sure which service applies?
Many projects involve more than one service. Describe the problem, and we’ll outline a technical approach.