Embeddings at Shivacha
Numerical representations of meaning used for search, clustering and retrieval.
Overview
Embeddings map text, images or other data into vectors where similar meanings are close together. We select embedding models by domain, language and cost, evaluate retrieval quality and manage re-embedding when models change.
Why we use it
- Semantic search
- Clustering and classification
- Multilingual support
- Cross-modal retrieval
How we use it
Embeddings in our engineering work
Search
Semantic document search.
Classification
Lightweight classifiers on embeddings.
Analytics
Topic discovery and clustering.
Services
Services that use Embeddings
RAG Development
Retrieval-augmented generation systems that ground LLM answers in your documents with permissions, citations and measurable accuracy.
Learn moreNLP Development
Natural language processing for classification, entity extraction, sentiment, search and multilingual text understanding.
Learn moreGenerative AI Development
Generative AI applications for text, documents, code and media — grounded in your data and engineered for production reliability.
Learn moreLLM Development
LLM application and platform engineering: model selection, fine-tuning, serving, evaluation and cost-optimised inference.
Learn moreAI Chatbot Development
LLM-powered chatbots for customer service, sales and internal support — grounded, on-brand and integrated with your systems.
Learn moreAI Copilot Development
Embedded AI copilots that help users inside your product or internal tools — drafting, analysing, searching and taking actions.
Learn morePairs well with
What we combine with Embeddings
Models, retrieval, agents and ML operations.
Insights
Related insights
Designing AI products users trust
Trust in AI products is designed: show sources, make uncertainty visible, make correction easy and keep humans in control of consequential actions.
Learn moreWhy most AI pilots never reach production — and the engineering that fixes it
The model is rarely the problem. Retrieval quality, evaluation, integration and governance decide whether an AI pilot becomes a production system.
Learn moreAI development cost: what you pay for when you build an AI product or agent
Model fees are rarely the main cost. Data preparation, evaluation, integrations and guardrails decide both the budget and whether the system works.
Learn more
Build with Embeddings.
Tell us about your project, or the engineers you need, and we will propose an approach.