SERVICES
RAG Development Services That Ship to Production
We design, build, and evaluate retrieval-augmented generation systems that ground LLMs in your data — with retrieval accuracy you can measure before a single user sees an answer.
Discuss your projectMost RAG projects stall after the prototype. The notebook works on ten documents, then retrieval quality collapses at scale, the model hallucinates, and nobody can say why. We build the other kind: production pipelines with versioned ingestion, hybrid retrieval, reranking, and an evaluation harness that tells you exactly how accurate your answers are before users ever see them.
We are a founder-led custom AI/ML development company. Gopal Sabhadiya — Toptal top-3%, Expert-Vetted on Upwork, ex-Infosys Specialist Programmer — leads every engagement with a team of 10. We have shipped retrieval-augmented generation into real products: Invoxbooks uses RAG for AI invoice parsing in a platform that saves its client roughly $200K per month, and Bar.Stream uses RAG to identify and classify product sizes for complete inventory reporting across 200+ B2B hospitality customers.
Production RAG, not prototypes
We build full production RAG pipelines: document ingestion, chunking, embeddings, vector indexing, hybrid retrieval, reranking, and LLM orchestration — engineered to stay accurate as your data grows, not only in a ten-document demo.
Retrieval you can measure
Every system ships with a RAG evaluation harness tracking context precision, context recall, and answer faithfulness, so quality becomes a number you can improve rather than a hunch.
Hallucination mitigation by design
We ground responses in your knowledge base with citations and retrieval guardrails, so the model answers from your data instead of inventing it.
Vector database integration
We integrate and tune the right vector store for your workload — Pinecone, Qdrant, Weaviate, or ChromaDB — with hybrid search (BM25 + vector) and rerankers for precision.
Proven in real products
RAG already runs in production in our work: Invoxbooks invoice parsing (~$200K/month saved for the client) and Bar.Stream product classification across 200+ B2B customers.
Founder-led delivery, worldwide
Gopal leads every project. Based in Surat, India, we serve clients worldwide in English, bill in USD, align 4+ hours with any timezone, and hold a 100% client-satisfaction record.
What we build into a production RAG pipeline
A retrieval-augmented generation system is only as good as what it retrieves. We start with document chunking and ingestion tuned to your content — splitting on semantic boundaries rather than arbitrary token counts, preserving structure like tables and headings, and enriching chunks with metadata for filtering. We then select embedding models suited to your domain and build the vector indexing layer for fast semantic search.
On top of that we layer hybrid search (BM25 + vector) so keyword-exact matches and semantic matches both surface, then add a reranker to push the most relevant context to the top. This combination is what separates retrieval that looks fine in testing from retrieval that stays precise as your knowledge base grows. The final stage is LLM orchestration: prompt augmentation that injects retrieved context with grounding instructions and citations, so each answer is built from your documents and traceable straight back to them.
This is the same backbone behind our shipped work — RAG for invoice parsing at Invoxbooks (a Python platform) and RAG-driven product size identification and classification at Bar.Stream (Java/Spring Boot backend with a React/Next.js frontend).
How we evaluate and de-risk RAG quality
We treat RAG evaluation as a first-class part of delivery, not an afterthought. Before launch we build a labeled evaluation set from your real queries and measure context precision and context recall — is the retriever pulling the right passages, and is it missing any? — alongside answer faithfulness to catch hallucinations where the model strays from retrieved context.
Because retrieval is measured, every change is testable. When we adjust a chunking strategy, swap an embedding model, or tune the reranker, we can prove whether quality went up or down instead of guessing. This is how RAG cuts down LLM hallucinations in practice: it grounds answers in retrieved knowledge and keeps verifying that the grounding holds.
For more complex workloads we build agentic RAG, where an LLM agent decides what to retrieve, issues multiple retrieval steps, reformulates queries, and synthesizes across sources — useful when a single lookup can't answer the question. We scope this only when standard RAG genuinely falls short, so you pay for complexity that earns its keep.
Proof
Invoxbooks
RAG-powered AI invoice parsing in a Python platform that saves the client ~$200K per month.
Tech we use
Frequently asked questions
What is retrieval-augmented generation (RAG) and how does it work? +
RAG connects a large language model to your own data. Instead of relying only on what the model learned in training, the system retrieves relevant passages from your knowledge base — using vector and keyword search — and injects them into the prompt. The model then answers grounded in that retrieved context, which keeps responses current, accurate, and traceable to source.
RAG vs fine-tuning: which is better for enterprise use cases? +
For most enterprise needs, RAG wins. Fine-tuning teaches a model a style or narrow task but is expensive to retrain and doesn't add fresh facts. RAG lets you update the knowledge base instantly, cite sources, and control what the model can see — ideal when your data changes or accuracy and grounding matter. The two can be combined, but we usually start with RAG and add fine-tuning only if a specific gap remains.
How do you build a production-ready RAG pipeline? +
We build it in stages: document chunking and ingestion, embedding generation and vector indexing, hybrid retrieval (BM25 + vector) with reranking, then LLM orchestration with grounded, cited prompts. Around that we add an evaluation harness measuring context precision, context recall, and faithfulness, plus monitoring. The result is a pipeline that stays accurate against real query volume rather than only on a small demo set.
What is the best chunking strategy for RAG? +
There is no single best strategy — it depends on your content. We favor structure-aware, semantic chunking that respects headings, tables, and paragraph boundaries, with overlap to preserve context and metadata for filtering. Chunk size is then tuned against your evaluation set, because the right choice for legal contracts differs from one for product catalogs or invoices.
How do you evaluate RAG retrieval accuracy and quality? +
We build a labeled set of real queries and measure context precision (are retrieved passages relevant?), context recall (did we miss anything?), and answer faithfulness (does the answer stay true to retrieved context?). Because these are concrete metrics, every tuning change — chunking, embeddings, reranking — can be proven to improve or regress quality before it reaches users.
How much does it cost to build a RAG system? +
It depends on data volume, sources, accuracy targets, and whether you need standard or agentic RAG. A focused pilot is far cheaper than a multi-source enterprise platform. We scope a fixed first phase that delivers a measurable, production-grade pipeline, then expand from there. Share your use case and we'll give you a concrete estimate. [founder to confirm specific pricing tiers]