~50%
of ML models never make it from prototype to production.
Gartner, 2023
AI & Data
When the off-the-shelf model isn't enough.
Forecasting, classification, recommendation and computer-vision models built around your data and your accuracy requirements. With the engineering discipline to keep them running reliably in production.
Models that survive contact with production data.
Anyone can train a model on a clean training set. The hard work is what comes after: keeping accuracy stable as data drifts, integrating the model with operational systems, measuring real business impact, retraining at the right cadence, and doing all this without paging a data scientist every week. We build the model and the surrounding system. Feature pipelines, training infrastructure, evaluation harnesses, monitoring, retraining schedules and integration with the systems where the prediction is actually used. The result is an ML capability your team can operate, not a notebook nobody can re-run.
By the numbers
ML in research is easy. ML in production is where most efforts stall.
The gap between training-set accuracy and a production system delivering business value is wider than most teams realise.
$15.7T
projected global AI contribution to GDP by 2030.
PwC, 2023
91%
of leading companies invest in AI, but only 18% report widespread adoption.
Capgemini, 2024
~70%
of model accuracy degradation comes from data drift after deployment, not from initial training.
Industry estimate
What you get
A complete custom-ML capability: feature engineering, training, MLOps, monitoring and integration. Built for production.
Forecasting and time-series models
Demand, capacity, financial and operational forecasting tailored to your data's seasonality, exogenous variables and accuracy bar.
Classification and scoring models
Fraud, churn, propensity, risk. Tuned to your data and your precision-recall trade-offs, with explainability where regulation requires it.
Recommendation and ranking systems
Collaborative, content-based and hybrid recommenders. Real-time inference, A/B-tested against business metrics, retrained as user behaviour shifts.
Computer vision for production
Defect detection, quality control, document understanding, OCR. Trained on your data, deployed at the edge or in cloud, with the operational practices to keep it accurate.
MLOps and operational discipline
Feature stores, model registries, CI/CD for models, drift monitoring, automated retraining and rollback. The bits that turn a working model into a system.
Integration where prediction meets product
Real-time inference, batch scoring, embedded model deployment. Wired into the systems where the prediction actually drives a decision.
Why Codino
- 10+ years shipping ML systems to production. Not research prototypes.
- One team owns the whole stack. Feature pipelines, training, evaluation, MLOps and monitoring.
- EU-based with EU data residency and GDPR-compliant delivery by default.
- Evaluation-first. Accuracy targets and drift detection baked in from day one.
- Framework-agnostic. PyTorch, scikit-learn, XGBoost, TensorFlow, JAX. Chosen for fit.
- We design our exit. Your team operates the system the day we leave.
How we deliver
From use-case audit to a model running in production. Phased so each phase delivers measurable value.
- 01
Discover
Use-case workshop, data inventory, baseline analysis. We identify the highest-ROI prediction problem and the data needed to solve it.
- 02
Prototype
Feature engineering, model training, evaluation on real data. You see the accuracy and the cost-of-errors honestly before committing.
- 03
Productionise
MLOps stack, real-time or batch deployment, monitoring, drift detection, integration with downstream systems. The model runs unattended on real traffic.
- 04
Scale
Add use cases, expand feature pipelines, refine retraining cadence as data accumulates. The capability compounds across the business.
What changes when models actually run
Schedule an ML auditPredictions move real business KPIs
Fraud rate down, conversion up, inventory waste down. Measured against a baseline you set before launch, not against a research benchmark.
Accuracy doesn't silently degrade
Drift monitoring and automated retraining catch quality changes before users feel them.
Your data team stops re-running notebooks
Feature pipelines, model registries and CI/CD turn ML delivery into engineering, not artisan work.
Compliance and explainability are handled
Model cards, lineage, audit trails and explainability where regulation requires it. Regulatory reviews pass the first time.
Where custom ML lands first
Sectors where domain-specific accuracy, regulatory constraints or data shape make off-the-shelf models insufficient.
Financial services
- Fraud and anti-money-laundering models
- Credit and risk scoring
- Customer churn and lifetime-value prediction
- Trading and pricing models
Selected work
Recent ML systems we built and operated through production scaling.
Recast
Recast is a platform that resets what it means to stream. Making access fairer, rewarding creators better, and connecting through content. Where Fans pay creators per view, rather than the platform per subscription.
View Case Study
ZipZero
ZipZero lets users collect funds from their purchases to pay monthly household bills. Backed by AI that scans receipts and recognises products, it lets users earn from their own shopping data.
View Case StudyWhat Clients say about us
Pete Willcox
VP Product
"All members of the Codino team fit seamlessly into our delivery teams, building excellent relationships and always willing to go the extra mile to deliver on our Roadmap in a timely and efficient way. We have built extremely good relationships with them and they feel just like part of the team"
Recast
Marcin Walaszczyk
CTO
"The expertise of the leaders, coupled with the diverse skill sets of their teams, truly sets them apart. Their vast experience across a myriad of projects ensures that they can adeptly handle virtually any project you envision. Furthermore, their deep involvement in the process is palpable; it's as if they seamlessly integrate and become an intrinsic part of your in-house development team."
ZipZero
Custom machine learning, explained
LLMs are great for language and reasoning tasks, but for high-volume scoring, structured prediction and real-time inference, classical ML is usually faster, cheaper and more accurate. At scale, the cost difference is often 10-100x. We pick the right tool. Sometimes LLM, sometimes XGBoost, often both.
Built in from day one. Feature stores, model registries, CI/CD for models, drift monitoring, automated retraining and rollback. We don't hand over a model and disappear.
Yes, most engagements run that way. We bring senior ML engineers alongside your data scientists, share the production discipline, and step out cleanly when your team is autonomous.
Model cards, lineage tracking, audit trails and explainability methods (SHAP, LIME, attention attribution) where regulation requires it. We meet the bar for financial services, healthcare and other regulated sectors.
PyTorch, scikit-learn, XGBoost, LightGBM, TensorFlow, JAX. Deployment: TorchServe, ONNX, Triton, SageMaker, Vertex AI, custom serving. We pick what fits your scale, latency and cost requirements.
Let's Talk About Your Project
Get In Touch
Maciej Roman|CEO & Co-founder