AI & Intelligent Systems
End-to-end ML pipeline development, LLM integration, and intelligent automation for businesses ready to move beyond demos.
Overview
Production AI — Not Just Prototypes
EulerHive builds AI systems that run in production — from data ingestion and model training to serving, monitoring, and continuous improvement. We work across the full ML stack: PyTorch, TensorFlow, FastAPI, Triton, RAG pipelines, and LLM fine-tuning.
Capabilities
What We Deliver
Core capabilities within AI & Intelligent Systems.
ML Pipeline Development
End-to-end pipelines covering data ingestion, feature engineering, model training (PyTorch / TensorFlow), evaluation, and versioning with MLflow or DVC.
LLM Integration & Fine-Tuning
Integrate OpenAI, Anthropic, Mistral, or open-source models into your product. Fine-tune on your domain data for higher accuracy and lower latency.
RAG Systems
Retrieval-Augmented Generation pipelines with vector databases (Pinecone, Weaviate, pgvector). Accurate, grounded responses from your own knowledge base.
Model Serving & Inference
High-throughput model serving with FastAPI, Triton Inference Server, or vLLM. Optimised for latency, cost, and horizontal scalability.
AI Monitoring & Observability
Production monitoring with Evidently, Prometheus, and Grafana. Drift detection, data quality checks, and automated retraining triggers.
Generative AI Automation
Custom AI agents, document processing pipelines, and workflow automation using LangChain, LlamaIndex, or bespoke orchestration layers.
Stack
Technologies We Use
FAQ
Common Questions
Answers to what clients typically ask before engaging.
We have a prototype — can you help take it to production?
Yes. We frequently take over proof-of-concept AI systems and harden them for production: adding proper data pipelines, monitoring, error handling, and scalable serving infrastructure.
Do you work with open-source models or only commercial APIs?
Both. We help clients choose the right model for their cost, latency, and data-privacy requirements — whether that is GPT-4, Claude, Llama 3, or a fine-tuned open-source model running on your own infrastructure.
How do you handle data privacy for AI projects?
We design AI systems with data minimisation and privacy-by-default principles. For sensitive use cases, we recommend on-premise or VPC-hosted models to ensure data never leaves your environment.
More Services
Other Practice Areas
One integrated engineering team across four disciplines.
Product Engineering
Full-stack web and mobile development for startups building their first product and enterprises modernising legacy systems.
Explore Product EngineeringPlatform & DevOps
Infrastructure-as-code, Kubernetes, GitOps, and full-stack observability for engineering teams that need to move fast without breaking things.
Explore Platform & DevOpsData Engineering
Streaming and batch pipeline design, data warehouse modelling, and real-time analytics for teams that need reliable, observable data infrastructure.
Explore Data Engineering