ML Engineering
Build production-ready ML features for your product. From data pipelines to inference endpoints, we handle the engineering so you focus on impact. a .,samx.as.,sa
6+
years production ML
12+
industries served
40+
models deployed
Image classification, object detection, text processing, and language models. Domain-specific fine-tuning included.
ETL systems and data infrastructure that handle volume and latency. We design for reliability, not just speed.
End-to-end training workflows, feature engineering, and hyperparameter tuning. We ship models that generalize.
APIs, batch systems, and edge deployments. Low latency, high throughput, monitored in real time.
Model drift detection, performance tracking, and alerting. Know when your model degrades before your users do.
Collaborative filtering, content-based models, and learning-to-rank pipelines. Personalization that converts.
Image classification, object detection, text processing, and language models. Domain-specific fine-tuning included.
Our story
We're ML engineers who build products, not researchers who publish papers. We've shipped recommendation systems for e-commerce, fraud detection for fintech, demand forecasting for logistics, and computer vision for manufacturing. Every project starts with your business problem, not a fancy algorithm. We design data architecture, train robust models, and deploy systems that stay reliable in production. Our focus is on the gap between a notebook and a live service—the part that actually matters.
Expert in computer vision and model optimization. Handles training workflows and inference deployment.
Founder & ML Engineer
Built recommendation systems at scale. Leads project architecture and model design.
Data Engineer
Specializes in ETL systems and data infrastructure. Ensures pipelines run reliably at volume.
ML Engineer
Expert in computer vision and model optimization. Handles training workflows and inference deployment.
Backend Engineer
Builds APIs and production systems. Owns monitoring, logging, and operational reliability.
“We needed computer vision for quality control. They handled the entire stack—data labeling, model training, deployment. Turned a six-month project into two.”
“They understood our recommendation problem immediately and shipped a system that improved conversion in three weeks. No fluff, just results.”
Vikram
E-commerce platform
“Our fraud detection model was brittle in production. Pranjali rebuilt the pipeline with proper monitoring. We caught issues before customers did.”
Deepa
Fintech startup
“We needed computer vision for quality control. They handled the entire stack—data labeling, model training, deployment. Turned a six-month project into two.”
Sanjay
Manufacturing company
“The scoping call alone clarified what we actually needed. They said no to two ideas that wouldn't have worked and yes to one that did.”
Priya
Logistics network
From the engineering bench
Data drift, class imbalance, and latency constraints. How to spot and fix them before launch.
Collaborative filtering, cold start problems, and real-time ranking. A practical walkthrough.
Drift detection, performance degradation, and alerting. Keep your models honest.
Lag features, rolling statistics, and seasonal decomposition. Techniques that work in practice.
Pricing is confirmed during the scoping call based on scope and timeline.
Pricing is confirmed during the scoping call based on scope and timeline.
₹15,000
Problem definition and feasibility review
Data assessment and architecture sketch
Timeline and resource estimate
Recommendation on approach
₹80,000–₹250,000
End-to-end ML feature build
Data pipeline and model training
API deployment and integration
Monitoring and documentation
₹40,000/month
Continuous model improvement
Performance monitoring and drift detection
Infrastructure optimization
Quarterly strategy reviews
01
We've shipped ML systems for e-commerce, fintech, logistics, manufacturing, healthcare, and media. If your problem involves data and prediction, we can help.
02
Both. We often improve or redeploy existing models into production. We also build new systems when needed. The scoping call clarifies what makes sense for your situation.
03
Scoping calls are 1–2 weeks. Feature development ranges from 4–12 weeks depending on data readiness, model complexity, and integration scope. We discuss timelines upfront.
04
Most projects start with messy data. We assess data quality during scoping, design collection or cleaning pipelines, and build models accordingly. It's part of the work.
05
Yes. We deploy models as APIs or batch systems, set up monitoring for drift and performance, and provide documentation. The system is yours to run or we can support ongoing operations.
06
Book a scoping call. We'll discuss your problem, assess feasibility, and outline next steps. No commitment required—just a conversation.
Ready to build? Let's talk about your ML challenge.
Book a scoping call and let's discuss your project.