Hire RAG
Developers
Retrieval-augmented generation systems that actually retrieve the right thing — built with chunking strategy, hybrid search, reranking, and RAGAS evals from day one. US, UK & EU companies, served from India.
What our RAG developers build for you
Every layer of the RAG pipeline matters. A weak chunking strategy, the wrong embedding model, or no reranking step will produce wrong answers regardless of which LLM you connect to the end.
Chunking Strategy & Document Processing
How you split documents determines what the retrieval system can find. Fixed-size, recursive, semantic, and document-structure-aware chunking — chosen based on your document types, not copied from a tutorial.
Vector Database Implementation
Pinecone, Weaviate, pgvector, Chroma, and Qdrant — selected and configured for your scale, query patterns, and infrastructure. Including index design, metadata filtering, and namespace strategy.
Hybrid Search (Semantic + Keyword)
Pure vector search misses exact matches. Pure keyword search misses semantic similarity. Hybrid search, with tuned weighting between the two, consistently outperforms either alone on real-world queries.
Reranking & Context Compression
Cross-encoder reranking to improve retrieval precision, and context compression to remove irrelevant passages before they reach the LLM. Both reduce hallucination and improve answer quality more than a better embedding model.
RAG Evaluation (RAGAS & Custom Evals)
RAGAS metrics — faithfulness, answer relevance, context recall, context precision — plus task-specific evals for your domain. Built from the start, not added after users complain about wrong answers.
RAG Pipeline Production Deployment
Ingestion pipelines that handle document updates without full re-indexing, query latency optimisation, caching for repeated queries, and monitoring for retrieval quality drift over time.
Hire RAG developers the way you need them
Staff Augmentation
RAG expertise added to your team
A RAG developer embedded in your engineering team — building or improving the retrieval layer your LLM application depends on. You keep control of the rest of the system.
Best for
Teams with a working LLM app who need the retrieval layer significantly improved.
Dedicated RAG Pod
Full RAG system built end-to-end
RAG developer plus backend engineer — ingestion pipeline, vector database, retrieval layer, eval framework, and production deployment delivered as a complete system.
Best for
Companies building a document Q&A, enterprise search, or knowledge base product from scratch.
RAG Audit & Rebuild
Fix what's not retrieving well
A structured audit of an existing RAG system — measuring RAGAS metrics against a ground truth set, identifying the bottleneck (chunking, embedding, retrieval, or generation), and rebuilding the weak layer.
Best for
Teams with a deployed RAG system that's producing wrong or incomplete answers.
Technologies our RAG developers work with
GPT
Claude Sonnet
Gemini
LLaMA
Mistral
Pinecone
pgvector
Chroma
Qdrant
LangChain
LlamaIndex
Hugging Face
LangSmith
RAGAS
Python
TypeScript
FastAPI
SQL
DockerWhy global companies hire RAG developers through Artinoid
Anyone can connect a vector store to a chat interface in an afternoon. Building a RAG system that retrieves accurately, scales to a large document corpus, and stays accurate as documents update — that takes real experience.
Most RAG Demos Work. Most RAG Products Don't.
A RAG system that retrieves the right chunk 70% of the time looks impressive in a demo. At 70%, users trust it, get burned, and stop using it. Our developers build toward the 90%+ accuracy that makes a knowledge product viable — and they measure it with evals, not impressions.
Faster Than In-House Hiring
RAG developers with production experience — chunking strategies, hybrid search, RAGAS evals, reranking — are a specific and rare skill set. We've already found them. You get matched in days.
40–60% Lower Than US/UK Rates
Senior RAG engineering expertise out of India at a fraction of US or UK market rates — and a retrieval system that works well pays for itself in user trust.
No Lock-In
RAG systems need iteration — what works for one document corpus may not work after you add a new data source. Two weeks' notice, weekly billing — designed for systems that need to improve over time.
Simple process, faster than in-house hiring
Discovery Call
We learn about your goals, team structure, timeline, and what a successful engagement looks like for you.
Candidate Matching
We shortlist pre-vetted candidates whose skills and experience closely match your requirements.
Technical Interview
You interview shortlisted candidates directly — same process as in-house hiring. You decide who joins.
Contracts & NDA
Agreements signed swiftly. IP assignment, confidentiality, and data handling are all covered before work begins.
Onboarding
Your new team member is set up, briefed, and contributing from day one. No extended ramp-up.
Common questions
What makes a RAG system actually accurate in production?
Four layers that all need to be right: chunking (are the right passages being indexed?), retrieval (are they being found when asked?), ranking (are the most relevant ones at the top?), and generation (is the LLM answering from what was retrieved rather than from its weights?). Most systems that don't work well have a problem in one of the first three layers — not the LLM. We measure each layer separately so we know which one to fix.
Which vector database do you recommend?
It depends on your infrastructure and scale. pgvector if you're already on PostgreSQL and want to avoid managing another database — it handles millions of vectors with reasonable latency. Pinecone for managed, high-scale deployments where you don't want to run infrastructure. Weaviate or Qdrant for self-hosted with more control over filtering and indexing. Chroma for local development and prototyping. We'll recommend based on your specific query patterns, update frequency, and scale requirements.
What is hybrid search and does it actually help?
Hybrid search combines dense vector search (semantic similarity) with sparse keyword search (BM25 or similar). Pure vector search misses exact matches — if a user asks about 'ISO 27001' and the document uses the exact phrase, keyword search finds it reliably. Pure keyword search misses paraphrase — if a user asks 'what are the information security requirements' it may not match 'ISO 27001 compliance obligations'. Hybrid search, with tuned reciprocal rank fusion weighting, typically improves retrieval recall by 10–20% on real-world document corpora.
How do you handle documents that update frequently?
We build incremental ingestion pipelines — detecting changed or new documents, re-chunking and re-embedding only the affected content, and updating the vector index without a full rebuild. We also maintain document-level metadata so you can filter by source, date, or version at query time. Full re-indexing is available as a fallback but shouldn't be the normal path.
Can you build RAG over our internal company data (Notion, Confluence, Slack)?
Yes. We build connectors for common internal data sources — Notion, Confluence, Google Drive, SharePoint, Slack, and databases — with OAuth auth, incremental sync, and access control that respects the source system's permissions. Users should only be able to retrieve documents they could have accessed directly.
How do you measure whether a RAG system is working?
RAGAS provides four core metrics: faithfulness (does the answer come from the retrieved context?), answer relevance (does it answer the question?), context recall (did retrieval find the relevant passages?), and context precision (were irrelevant passages excluded?). We set up these metrics as a regression suite — run against a ground truth Q&A set after every change — so you can see whether improvements in one metric cause regressions in another.
Do your RAG developers sign NDAs before project details are shared?
Yes. NDA and IP assignment agreements are signed before any proprietary documents, data, or system details are discussed. Standard for every engagement.
Ready to hire RAG developers?
Tell us about your document corpus — types, volume, update frequency, and what users need to ask. We'll scope the right retrieval architecture and match you within 48 hours.
contact@artinoid.com
Response Time
Within 24 hours
Next Step
Discovery call to scope your RAG system requirements