Custom AI Models Built for Your Business
We design, train, and deploy machine learning models tailored to your specific problem — from classical ML to deep learning, from tabular data to unstructured text and images.
Trusted by innovative teams worldwide
Industry-Recognized ML Expertise
Our ML engineers hold certifications from the platforms that power production AI.
Full-Cycle Model Development
From problem framing to production inference — every stage of the ML lifecycle handled with rigor.
Problem Framing & Feasibility
Before writing any code, we validate whether ML is the right solution and define success criteria, baseline metrics, and data requirements with your team.
Data Preparation & Feature Engineering
Cleaning, labeling, augmentation, and feature extraction — we transform raw data into high-signal training sets that maximize model performance.
Architecture Selection & Design
We evaluate classical ML, gradient boosting, neural networks, and transformer architectures — selecting the simplest model that meets your accuracy and latency requirements.
Training & Hyperparameter Optimization
Distributed training with automated hyperparameter search using Optuna, Ray Tune, or SageMaker — squeezing maximum performance from your data.
Evaluation & Validation
Rigorous evaluation with cross-validation, holdout testing, fairness audits, and adversarial robustness checks — no model ships without thorough validation.
Deployment & MLOps
Containerized model serving with A/B testing, canary rollouts, drift monitoring, and automated retraining pipelines — production-grade from day one.
Off-the-Shelf Models Not Cutting It?
Let's build a model trained on your data, for your problem. Free feasibility call included.
Custom models that solve real problems, not demo problems.
We combine research-grade ML expertise with production engineering discipline. Every model is built to perform in the real world — noisy data, edge cases, and scale included.
ML Engineering Principles We Live By
Building a model is easy. Building one that works reliably at scale, handles edge cases gracefully, and improves over time — that's what we do.
Why Companies Choose Us for Model Development
We've trained models across fraud detection, credit scoring, demand forecasting, medical imaging, and natural language — with measurable business impact.
Describe Your ML Challenge
Tell us about your data and the problem you're trying to solve. We'll respond with a feasibility assessment within 48 hours.
Our Engagement Process
Problem Framing
Define success criteria, identify data sources, set accuracy baselines, and confirm ML is the right approach for your problem.
Data Preparation
Cleaning, labeling, feature engineering, and train/test split design — the foundation that determines model quality.
Model Development
Architecture selection, training, hyperparameter tuning, and iterative improvement with weekly progress reviews.
Validation & Testing
Cross-validation, fairness audits, robustness testing, and comparison against baselines — no model ships without proof it works.
Deploy & Monitor
Containerized serving, A/B testing, drift detection, and automated retraining pipelines — production ML that improves over time.
What Our Clients Say
“OpenMalo built a fraud detection model that catches 96% of fraudulent transactions while keeping false positives under 0.3%. Our previous vendor's model was at 78% detection. The difference in real money saved is staggering.
“We needed a demand forecasting model that could handle 10,000 SKUs across seasonal patterns. OpenMalo delivered a model that reduced our overstock by 34% and stockouts by 28%. ROI was visible within the first quarter.
“What impressed me most was their discipline. Weekly iterations, clear metrics, and honest communication about what was and wasn't working. No ML handwaving — just solid engineering and results.
96% Fraud Detection with 0.3% False Positive Rate
Real-Time Fraud Detection for TrustBridge Capital
How we built a custom gradient-boosted fraud detection model that processes 500K transactions daily, catching 96% of fraud while keeping false positives at 0.3% — saving $3.8M in the first year.
Legacy rules engine missing sophisticated fraud patterns
TrustBridge Capital's rule-based fraud system was catching only 78% of fraudulent transactions while flagging 2.1% of legitimate ones — costing millions in both fraud losses and customer friction.
Our Approach: Feature engineering from 200+ transaction attributes, LightGBM ensemble with temporal validation, real-time scoring API with <40ms latency, and automated weekly model retraining — deployed in 6 weeks.
Read Full Case StudyFrequently Asked Questions
We build across the full spectrum: classical ML (regression, tree-based models, SVMs), deep learning (CNNs, RNNs, transformers), and specialized architectures for time series, NLP, and computer vision. We choose the simplest approach that meets your performance requirements.
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