AI & ML Services
We build predictive models, forecasting systems, recommendation engines, and computer vision pipelines, trained on your data, deployed in your infrastructure.
What we build
Most companies claiming AI expertise are wrapping GPT APIs with a UI. We build actual machine learning, custom models trained on your data, statistical systems that produce measurable predictions, and data pipelines that keep them accurate over time. Our ML engineers have shipped production systems in fintech, e-commerce, healthcare, and logistics.
Core capabilities
Train models on historical business data to predict future outcomes: sales volume, inventory demand, user conversion, risk scores.
Identify customers with a high probability of leaving before they do. We build, train, and deploy classification models connected to your CRM.
Time-series forecasting models for supply chain, staffing, and resource planning. We handle seasonality, trend decomposition, and anomaly correction.
Collaborative filtering, content-based, and hybrid recommendation systems for e-commerce, media, and SaaS products.
Unsupervised models that flag unusual behavior in financial transactions, system metrics, sensor data, or user activity.
Image classification, object detection, OCR, and visual quality inspection using CNNs, YOLO, and custom architectures.
Sentiment analysis, named entity recognition, document classification, and information extraction at scale, using both classical NLP and fine-tuned language models.
End-to-end: data cleaning, feature engineering, model training, evaluation, A/B testing, and deployment with monitoring for data drift.
Our stack
Real applications
Use case 01
Built a product recommendation system that increased average order value by 23% by surfacing relevant products based on browsing history, purchase patterns, and category affinity.
Use case 02
Deployed a churn model that scores every active account weekly. The sales team uses it to prioritize at-risk accounts for outreach, reducing monthly churn from 4.2% to 2.8%.
Use case 03
Time-series model for a 3PL operator predicting daily inbound volume per warehouse. Reduced overstaffing costs by 18% in the first quarter post-deployment.
Use case 04
Computer vision system for a manufacturing client that detects surface defects on products using a camera line. Replaced a manual inspection process with 99.1% accuracy.
Our process
step 01
We assess your data quality, volume, and structure. We define the ML problem formally: what are we predicting, with what input, and how do we measure success.
step 02
Clean, transform, and structure your data for model training. Build repeatable pipelines that keep the model up to date as new data comes in.
step 03
Train multiple model architectures, evaluate on hold-out sets, and tune for your specific business constraints (precision vs. recall, latency vs. accuracy).
step 04
Deploy as a REST API, batch job, or embedded service. Integrate with your existing systems, CRM, data warehouse, application layer.
step 05
Set up data drift detection and model performance monitoring. Schedule retraining when the model's accuracy degrades past your threshold.
Track record
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Avg. AOV increase from rec engines
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Model accuracy on test sets
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Cost reduction from demand forecasting
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Defect detection accuracy (CV)
FAQ
Ready to build?
Book a technical call with our ML team. We'll review your data situation and tell you what's realistically buildable in the next 90 days.
