AI & Intelligent Systems
End-to-end ML pipeline development, LLM integration, and intelligent automation for businesses ready to move beyond demos.
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.
What We Deliver
Core engineering capabilities within AI & Intelligent Systems, built to enterprise reliability standards.
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.
Production RAG Systems
Retrieval-Augmented Generation pipelines with vector databases (Pinecone, Qdrant, pgvector). Grounded, context-verified responses from proprietary knowledge bases.
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 Prometheus and Grafana. Continuous drift detection, hallucination tracking, and token cost telemetry.
Autonomous AI Agents
Custom agentic workflows with Model Context Protocol (MCP) tool-calling, document processing, and multi-step research automation.
Architectural Deep Dives & Tradeoffs
Core engineering decisions, technical tradeoffs, and production standards we apply across AI & Intelligent Systems.
RAG vs. Fine-Tuning: Architectural Tradeoffs
Retrieval-Augmented Generation (RAG) grounds LLM responses in real-time external data without retraining models. Fine-tuning adjusts the model weights to learn specific behavior, tone, or style. In production systems, we frequently combine both: a fine-tuned lightweight model querying an authoritative RAG knowledge base.
Production RAG Architecture & Hallucination Mitigation
Naive vector search frequently retrieves irrelevant context that misleads LLMs. Our production RAG pipeline extracts verified evidence facts before prompts are constructed, enforces token limits, and implements automated evaluation metrics to ensure grounded, verifiable responses.
When to Deploy Autonomous AI Agents
Using standards like the Model Context Protocol (MCP), we build agents that safely execute external actions — querying CRM APIs, running database analytics, or triggering notifications — while maintaining deterministic fallback logic and human-in-the-loop oversight.
Technologies We Use
Battle-tested frameworks, databases, and infrastructure tools powering AI & Intelligent Systems.
Related Case Studies
See how our AI & Intelligent Systems practice delivered production software for verified clients.
JunixHQ: AI-Powered B2B Sales Automation SaaS
Autonomous prospect research and enrichment pipeline with FastAPI, SSE streaming, and OpenAI integration in 2–5 seconds per lead.
Engineering Playbooks & Guides
First-hand engineering articles relevant to AI & Intelligent Systems.
Common Questions
Answers to what engineering leads and founders typically ask before engaging.
Can you take a proof-of-concept AI prototype to production?
Yes. We frequently harden prototypes into production platforms by implementing resilient data ingestion, error boundaries, evaluation suites, and scalable inference infrastructure.
Do you work with open-source models or commercial APIs?
Both. We match model selection to your latency, cost, and data privacy needs — deploying proprietary APIs (OpenAI, Anthropic) or self-hosted open-source models (Llama 3, Mistral) in your private VPC.
How do you prevent data leaks and maintain privacy with AI?
We design systems with zero-data-retention agreements and VPC isolation so sensitive enterprise data never trains external commercial models.
Other Practice Areas
One integrated engineering team across four software disciplines.
Product Engineering
Full-stack web and mobile development for startups building their first product and enterprises modernising legacy systems.
Platform & DevOps
Infrastructure-as-code, Kubernetes, GitOps, and full-stack observability for engineering teams that need to move fast without breaking things.
Data Engineering
Streaming and batch pipeline design, data warehouse modelling, and real-time analytics for teams that need reliable, observable data infrastructure.
