RAG Pipeline Development
RAG pipelines that give AI the right answers.
Retrieval-augmented generation lets an AI model look things up in your own documents and data before it answers. We engineer the whole pipeline: ingestion, chunking, embeddings, search, permissions and evaluation, so answers are accurate, cited and safe to put in front of customers or staff.
What we do
One team of designers and engineers, from first idea to long-term support.
- 01
Document ingestion
PDFs, Word files, websites, Notion, Google Drive, Confluence and databases, cleaned, parsed and kept in sync automatically.
- 02
Retrieval engineering
Chunking, embeddings, hybrid keyword and vector search, and re-ranking tuned to your content, so the right passage is found first.
- 03
Permission-aware search
Users only get answers from documents they're allowed to see, matching the access rules you already have.
- 04
Answers with sources
Responses that cite the exact document and section, so people can check them and trust them.
- 05
Evaluation & accuracy testing
Test sets built from real questions that measure retrieval and answer quality before and after every change.
- 06
Fixing an existing RAG system
Already have a RAG prototype that gives poor answers? We diagnose where retrieval fails and rebuild what's needed.
How we work
Audit your data
We review your sources, formats, update frequency and access rules.
Prototype
A working pipeline on a sample of your data, with accuracy measured.
Harden
Scaling, permissions, monitoring, cost controls and automatic re-indexing.
Evaluate & improve
Ongoing evaluation as your content and questions change.
Tools we use
- LangChain
- LlamaIndex
- OpenAI
- Claude
- pgvector
- Pinecone
- Qdrant
- Elasticsearch
- Python
Selected work
All case studies →
Questions, answered
RAG is a technique where an AI model first searches your own documents and data for relevant information, then uses what it found to write its answer. It makes AI accurate about your business without retraining a model.
For answering questions from your documents, RAG is usually the better choice: it's cheaper, stays up to date as content changes and can cite sources. Fine-tuning suits changing a model's style or format. We'll recommend the right mix for your case.
Usually, yes. Most problems come from how documents are split, searched or ranked rather than the model itself. We measure where it fails and fix retrieval first, then prompts and the model.
Yes. We can use providers that don't train on your data, deploy in your own cloud account, or run open-source models fully self-hosted when needed.
Flucks is a team of designers and engineers, not a solo freelancer. Every project gets design, frontend, backend and QA covered by people who work together daily, with one point of contact, written scope and regular demos, so nothing depends on one person being available.
Yes. We're based in India and work with clients across India and worldwide, including the US, UK, Europe and the Middle East. We plan overlap hours with your time zone and keep everything documented, so distance never slows the work down.