Kapaxia
Enterprise MLOps

Deploy AI at scale. Train models that deliver results.

Enterprise-grade infrastructure for AI deployment and model training. From fine-tuning LLMs to production deployment in the cloud or on-premise, we build the foundation for your AI success.

Three pillars of enterprise AI

Complete MLOps solutions

End-to-end infrastructure and expertise for training, deploying, and scaling AI models in production.

Custom model training and fine-tuning for your specific use cases and data

Scalable cloud deployment with auto-scaling, monitoring, and cost optimization

Secure on-premise deployment with full control over data and infrastructure

Real-time performance monitoring and automated model retraining pipelines

Enterprise security with compliance, data encryption, and access controls

Continuous optimization for performance, cost, and resource utilization

LLM Training Excellence

Model training & fine-tuning

Custom AI models trained on your data, fine-tuned for your business objectives.

Custom Model Training

Train models from scratch or fine-tune existing LLMs with your proprietary data for optimal performance.

Data Pipeline Engineering

Build robust data pipelines for training, validation, and continuous model improvement.

Model Evaluation & Testing

Comprehensive testing frameworks to ensure models meet your accuracy and performance requirements.

Flexible deployment options

Deploy anywhere your business needs—cloud, on-premise, or hybrid infrastructure.

Cloud Deployment

Scalable, cost-effective cloud infrastructure with automatic scaling, load balancing, and global distribution.

  • Auto-scaling infrastructure
  • Pay-per-use pricing
  • Global CDN distribution
  • 99.9% uptime SLA

On-Premise Deployment

Complete control over your AI infrastructure with secure, compliant on-premise deployment solutions.

  • Full data sovereignty
  • Custom security policies
  • Zero external dependencies
  • Dedicated support

Production-grade infrastructure

Built for scale, security, and reliability from day one.

Real-time Monitoring

Track model performance, latency, and resource usage with comprehensive dashboards and alerts.

CI/CD Pipelines

Automated testing, versioning, and deployment pipelines for rapid iteration and rollback capability.

Enterprise Security

End-to-end encryption, role-based access control, and compliance with industry standards.

Partners

We work with industry-leading technology partners like Vercel, Google Cloud, Railway, and many others. These partnerships enable us to deliver robust, scalable solutions that meet the highest standards of performance and reliability.

Cloudflare logo
Vercel logo
Railway logo
AWS logo
Microsoft Azure logo
Google Cloud logo
OpenAI logo
Anthropic logo
ElevenLabs logo
XAI logo
Groq logo
Fireworks AI logo
Cohere logo
Together.ai logo
Mistral AI logo
Cerebras logo
Deepgram logo
Hugging Face logo

Our implementation process

From assessment to production in weeks, not months.

1

Assess your AI needs, data infrastructure, and deployment requirements

2

Design custom architecture for training, deployment, and monitoring

3

Build and deploy infrastructure with your team, ensuring knowledge transfer

4

Monitor, optimize, and scale based on real-world performance metrics

Frequently asked questions

Initial deployment typically takes 2-4 weeks for cloud infrastructure, 4-8 weeks for on-premise solutions. Training custom models can take 1-6 weeks depending on data volume and complexity.
Costs vary based on scale, deployment type, and compute requirements. We provide transparent pricing tailored to your specific needs and help optimize infrastructure costs for the best ROI.
Yes, we offer comprehensive maintenance packages including monitoring, model retraining, infrastructure updates, and 24/7 support for production environments.

Ready to deploy AI at enterprise scale?

Let's build the infrastructure for your AI-powered future.