Retrieval-augmented generation
Grounding answers in your own documents with chunking, embedding and reranking tuned for your content — the part that determines whether RAG works or embarrasses you.
Our services / Generative AI and LLM Solutions
Retrieval, evaluation, citations, cost
A convincing LLM demo takes an afternoon. A system that stays accurate across thousands of real queries, cites its sources, behaves predictably when it does not know, and does not quietly cost more than the problem it solves — that is a different job.
The difference is almost entirely retrieval quality, evaluation discipline and cost design. That is where this work concentrates.
The essentials
What we can build
Citations and verifiability
Every answer traceable to source passages, so users can check it. Unverifiable output does not get trusted, and untrusted systems do not get used.
Evaluation sets and regression testing
A scored test set built from real questions, so you can tell whether a prompt or model change helped. Without this you are guessing.
Content and drafting pipelines
Generation with structure, brand constraints and a human approval step, for teams producing high volumes of repetitive written material.
Cost and latency engineering
Model routing, caching, prompt compression and batching, because naive implementations are commonly several times more expensive than they need to be.
Guardrails and safe failure
Refusal behaviour, out-of-scope handling, injection resistance and clear labelling of generated content.
In practice
Where this makes a difference
Your policies live in 400 PDFs
Classic RAG territory. The engineering effort goes into document processing and retrieval quality rather than the model, which is the part most implementations underinvest in.
Support answers the same questions endlessly
An assistant grounded in your documentation with citations deflects routine volume while keeping the judgement calls with people.
Sales needs tailored documents quickly
Proposal and summary generation from structured inputs with a review step is a well-bounded, high-value use of generation.
Your first RAG attempt gave confident wrong answers
Almost always retrieval rather than the model — poor chunking, missing reranking, no evaluation set. It is fixable, and usually without changing models.
Your questions
Before we begin.
It depends on accuracy needs, latency, privacy constraints and volume economics, and the sensible answer changes every few months. We evaluate candidates against your task rather than defaulting to whichever is currently fashionable.
Yes, with open-weight models. Quality is below the strongest hosted models on hard reasoning tasks and entirely sufficient for most retrieval and extraction work. We will measure both on your data and show you the gap.
Grounding answers in retrieved passages, requiring citations, tuning refusal behaviour and evaluating against a known-answer set. Hallucination cannot be eliminated, but it can be made rare, visible and checkable.
A prototype against your real documents typically takes two to three weeks, and that prototype tells you far more about feasibility than any amount of further planning.
Your next move
Test it on your documents
Send a description of the documents or knowledge involved and the questions people need answered. A prototype settles it faster than a proposal. Email info@octafusion.in or call +91 8780601826.