Dedicated full-time
Owns ingestion, retrieval, prompts and evaluation for a RAG feature that customers rely on.
Dedicated RAG engineers
Hire RAG and LangChain developers who make answers cite the source.
Need staff or customers to ask questions of your manuals, contracts or tickets and get answers they can check? DayDecoder hires RAG developers from Ahmedabad, India who build retrieval with LangChain, LlamaIndex or plain SDKs, keep each customer’s data separate, and show where every answer came from. Overlap hours for the US, UK, Europe and Australia.
What our RAG developers build.
Retrieval-augmented generation means finding the right passages first and only then asking the model to answer from them. Most of the work is in the finding.
The AI customer support assistant in our portfolio combines knowledge search with ticket summaries and suggested replies. The document intelligence platform uses OCR, extraction and classification to turn PDFs and scans into searchable records with review queues. Our article RAG explained for business owners walks through the same approach in plain language: chunk documents sensibly, embed them, retrieve with filters, answer with citations, and test with real questions before launch.
A RAG hire at DayDecoder owns the whole pipeline, not only the prompt. That includes ingesting files when they change, deciding chunk sizes for your kind of document, storing embeddings in pgvector or a hosted vector store, filtering by tenant and permission before retrieval, and keeping a set of golden questions to re-run after every change. If keyword search alone would solve the problem, we will tell you.
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Ingestion that stays current
Queued jobs that parse, chunk and embed new or edited files, and remove stale chunks when a document is deleted.
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Retrieval with permissions
Filters by customer, team and document access applied before the model sees any text.
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Answers with citations
Responses that link to the source passage, and a clear “not found” when the documents do not contain the answer.
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Evaluation and cost
A golden question set, retrieval quality checks and token budgets reviewed every week.
Skills and tool stack
RAG and LangChain tools we use.
The AI stack listed on our site, with a preference for the simplest piece that does the job.
LangChain and LlamaIndex are in our AI service stack and used when their loaders, splitters or retrievers save real effort. Embeddings and answers usually come from OpenAI or Claude. Vectors live in pgvector inside PostgreSQL when the product already uses it, or in a hosted store when scale or features demand it. Python handles OCR and heavier parsing; Laravel or Node.js owns users, billing and the interface.
Frameworks
- LangChain
- LlamaIndex
- Plain provider SDKs
Models
- OpenAI embeddings and chat
- Claude
- Re-ranking when it helps
Storage
- pgvector on PostgreSQL
- Hosted vector stores
- Object storage for source files
Pipeline
- Python parsing and OCR
- Queued ingestion
- Golden question sets
- Citation rendering
Engagement models
A dedicated RAG developer, part-time, or hourly.
Retrieval quality drifts as documents change, so RAG features benefit from a steady owner.
Part-time dedicated
Reserved days each week to improve retrieval quality, add sources and keep costs in check.
Hourly RAG help
A review of answers that cite the wrong document, a slow index, or a LangChain upgrade that broke something.
Hiring process
How a RAG hire starts on your documents.
We start with the questions people actually ask, not with a vector database.
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1
Intro call
Thirty minutes with the founder about the documents, who may see what, and which wrong answers would cause harm.
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Written proposal
Dedicated, part-time or hourly, and whether RAG, better search or a simple FAQ is the honest first step.
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Sample documents and questions
Under NDA, a sample of real files and twenty to fifty real questions with the answers you expect.
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First measured pipeline
Ingestion, retrieval and cited answers on the sample, scored against your questions.
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Weekly demo
The same questions re-run, what improved, what still fails and the week’s cost, in your overlap hours.
RAG work across US, UK, EU and Australian hours.
We work from Ahmedabad, India. Large ingestion and re-embedding jobs run during Indian hours, so your team starts the day with an updated index. Reviews of answer quality happen in overlap: afternoons for the UK and Europe, early mornings or late evenings for the US, and mornings for Australia.
Source documents stay in your storage and region. If data residency matters, we agree where embeddings are stored before ingesting anything.
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United States
Morning updates waiting in your inbox when the US workday starts.
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United Kingdom
Same-day overlap for calls, reviews, and decisions.
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Europe
Afternoon overlap with Central and Eastern European teams.
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Australia
Early-morning calls that fit the Australian workday.
Why hire RAG developers from DayDecoder.
Retrieval treated as an engineering problem with tests, and written guidance that refuses to oversell.
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Citations by default
If an answer cannot point to a source, the user should know. We design for that from the first version.
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Tenancy taken seriously
Multi-tenant SaaS is common in our portfolio, so per-customer filters are a habit, not an extra.
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Tested, not vibes
A golden question set re-run after every prompt, chunking or model change.
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You own the pipeline
Code, prompts, index and evaluation set are yours after payment. Your documents never train anything for other clients.
Relevant work
Search, extraction and AI work related to RAG.
Projects with knowledge search, document extraction and AI content features. Client names stay confidential.
Related reading from our blog.
All articlesAdding AI Features to a Laravel or Node.js SaaS Product
A technical guide on how to integrate AI features (OpenAI, Anthropic) into existing Laravel and Node.js SaaS products using vector databases and background queues.
Oct 26, 2026 · 4 min read
Read
Custom AI Software Development vs Buying AI Tools
When to choose custom AI software development versus buying SaaS AI tools: data advantage, process rules, review needs, and cost tradeoffs.
Oct 26, 2026 · 1 min read
Read
How to Integrate AI (LLMs) into an Existing Laravel or Node.js App
Architecture, RAG, cost control and security for adding OpenAI or Claude to a production Laravel or Node.js app, written for overseas founders and CTOs.
Oct 28, 2026 · 14 min read
Read
Related services and hires.
Services
Related hires
FAQ
Questions about hiring RAG and LangChain developers.
Straight answers on the stack, ownership and time zones. Email us if yours is missing.
The system first finds the passages in your documents that relate to a question, then asks the model to answer using only those passages, with links back to them.
Not always. LangChain and LlamaIndex help with loaders and retrievers; for a focused feature, plain SDK calls can be simpler. We choose per project.
Not if retrieval is filtered by tenant and permission before the model sees anything. That filter is part of every RAG design we propose.
pgvector when you already run PostgreSQL, or a hosted store when scale or features require it.
For answering from documents that change, usually yes. Fine-tuning changes how a model writes, not what facts it can look up.
We do not publish rates. Email contact@daydecoder.com or book a 30-minute call for a written proposal.
Let's talk
Need a RAG developer on your product?
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Free 30-minute call
We talk through your goals, users, and budget, and answer every question.
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Proposal & fixed quote
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contact@daydecoder.com
Ahmedabad, India
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