AI Inference & Serving at Shivacha
Serving models efficiently — latency, throughput, scaling and cost.
Overview
Inference engineering determines how fast and affordably models serve requests. We optimise with batching, quantisation, caching, streaming and right-sized hardware, and deploy autoscaled GPU or CPU serving with observability.
Why we use it
- Lower latency
- Higher throughput
- Controlled cost
- Private deployment options
How we use it
AI Inference & Serving in our engineering work
Private LLM serving
Open-weight models in your cloud.
Real-time ML
Low-latency scoring services.
Edge inference
On-device models.
Services
Services that use AI Inference & Serving
LLM Development
LLM application and platform engineering: model selection, fine-tuning, serving, evaluation and cost-optimised inference.
Learn moreMachine Learning Development
Custom machine learning models for prediction, scoring, forecasting and recommendation, deployed and monitored with MLOps.
Learn moreNLP Development
Natural language processing for classification, entity extraction, sentiment, search and multilingual text understanding.
Learn moreComputer Vision Development
Computer vision systems for inspection, detection, recognition and visual analytics — from cloud APIs to edge devices.
Learn moreAI Data Solutions
Data engineering for AI: pipelines, warehouses, feature stores, vector indexes and governance that make AI systems accurate.
Learn morePairs well with
What we combine with AI Inference & Serving
Models, retrieval, agents and ML operations.
Build with AI Inference & Serving.
Tell us about your project, or the engineers you need, and we will propose an approach.