Applied AI · Thoughtful engineering

EunoAI

Building reliable AI experiences with clarity and care.

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Product-minded delivery Reliable retrieval systems Practical AI strategy

Embedding Technology
RAG Pipelines
Model Selection & Fine-tuning
Vector Databases
LLM Integration
Python & PyTorch
MLOps

Embedding Solutions

Design and optimise text, image, and multimodal embeddings for search and similarity tasks.

Model Selection

Benchmark and choose the right model — OSS or proprietary — for your task and budget.

Pipeline Development

End-to-end ML pipelines from data ingestion through deployment and monitoring.

RAG Systems

Retrieval-augmented generation systems that ground LLMs in your proprietary data.

Understand the problem

Clarify the goal, constraints, and what “good” looks like before writing any code.

Prototype fast

Validate the riskiest assumptions early with a small, working slice of the system.

Build for production

Harden the pipeline — evaluation, monitoring, and error handling included.

Iterate with data

Use real usage and metrics to guide what gets improved next.

Embeddings transform raw text, images, or structured records into dense vector representations that capture semantic meaning. They power semantic search, recommendation engines, RAG pipelines, anomaly detection, and much more.

I work with state-of-the-art embedding models — from sentence-transformers and OpenAI embeddings to custom fine-tuned bi-encoders — and pair them with vector stores like Qdrant, Pinecone, and pgvector to build production-ready retrieval systems.

Key use-cases: semantic document search, knowledge-base Q&A, product recommendation, duplicate detection, cross-lingual retrieval.

View embedding projects →

Case study

Semantic Document Search

A search experience for large document collections that understands meaning rather than just keywords.

Python Embeddings Qdrant FastAPI

embedding retrieval

Outcome

Improved retrieval relevance while keeping latency acceptable for interactive use.

Focus

From vector indexing to relevance tuning and product-ready integration.

15+

AI Projects Delivered

Years Experience

10+

Open-source Contributions

20 Jul 2025 embedding

Choosing the Right Embedding Model for Production

A practical guide to balancing quality, speed, cost, and domain fit when selecting embeddings for a production product.

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EunoAI

What I Do