ML Pipeline Engineering
Reproducible training and retraining pipelines, from data to deployed model.
- Automated data pipelines
- Versioned models & experiments
- Scheduled retraining
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Secure & Manage
GPU Pipelines · LLM Deployment
ML & AI Infrastructure
We design and deploy the infrastructure behind machine learning and LLM-powered products — from GPU training pipelines to production inference at scale.
GPU-optimized pipelinesProduction-grade LLM deploymentBuilt for cost-efficient scalingSecure, monitored inference
Start a ProjectGPUOptimized pipelines
24/7Inference monitoring
100%Reproducible pipelines
ScalableBy design
The problem
Challenges we solve
Technology should remove friction from your business, not create more of it.
- ML models stuck in notebooks, never reaching production
- GPU costs spiraling with no visibility into usage
- No reliable pipeline for retraining or redeploying models
- LLM integrations that are slow, fragile, or expensive
- No monitoring for model drift or inference failures
- Security and data-privacy gaps in AI workflows
What we deliver
Built around your goals
LLM Deployment & Integration
Production deployment of LLMs — self-hosted or via API — integrated into your product.
GPU Infrastructure Setup
Right-sized, cost-optimized GPU infrastructure for training and inference.
Model Serving & Scaling
Low-latency inference infrastructure that scales with demand.
MLOps & Monitoring
Monitoring for model drift, latency, and inference failures in production.
AI Feature Integration
Embed AI features — search, recommendations, chat — directly into your product.
How we work
A clear path from idea to impact
- 01
Discover
We assess your data, models, and target use case.
- 02
Design
An architecture for training, deployment, and monitoring.
- 03
Build
Pipelines and infrastructure built and validated on real data.
- 04
Deploy
Production rollout with monitoring for drift and failures.
- 05
Scale & Optimize
Ongoing tuning for cost, latency, and accuracy.
Why Chrisent
What you can expect
- Experience across GPU infra and LLM deployment
- Cost-conscious infrastructure design
- Production monitoring, not just a working notebook
- Security-conscious handling of data and models
- Direct access to the engineers who built it
Our toolkit
Technology that fits
PyTorchKubernetesNVIDIA CUDAVector databasesAWS / GCP GPU instancesLLM provider APIs
Questions
Frequently asked
Can you deploy an existing model we've trained?
Yes — we can take an existing model and build the production serving and monitoring infrastructure around it.
Do you work with self-hosted or API-based LLMs?
Both — we'll help you weigh cost, latency, and data-privacy trade-offs to pick the right approach.
How do you keep GPU costs under control?
Right-sizing infrastructure and autoscaling based on real demand are core parts of every engagement.
Can you help us go from prototype to production?
Yes — this is one of the most common engagements: taking a working notebook and making it a reliable, monitored production system.
Ready when you are