A generic LLM answers from what it was trained on, which means it guesses when it does not actually know your business. Retrieval-augmented generation fixes that by grounding every answer in your real documents, databases, and records before the model responds. Akoode builds RAG applications for teams in India and abroad who need an AI system that is accurate about their business, not just fluent in general knowledge.
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A chatbot that sounds confident and a chatbot that is correct are two different products. RAG is the difference between them.
We build the retrieval layer, not just the chat interface: how your documents get indexed, how the right ones get found, and how the model uses them to answer instead of filling gaps with a guess.
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We build the full retrieval stack for a specific use case rather than bolting a vector database onto an existing chatbot and calling it RAG.
Service 1 of 6: RAG Application Development
Connecting a vector database to a language model takes an afternoon. Making the retrieval actually accurate, for your real documents and your real questions, is the part that takes engineering.
We treat retrieval quality as its own discipline, separate from the model. Chunking strategy, embedding choice, and search method all get tuned to your content, not left at whatever a tutorial defaults to.
RAG went from a research technique to standard enterprise practice faster than most teams updated their approach to it. Here is what has actually changed.
The documents differ by industry: clinical notes, policy wordings, product catalogues, contracts. What stays the same is the need for an answer that traces back to something real. Here is how that plays out sector by sector.
We select the retrieval architecture based on your content type, query volume, and accuracy requirements. Not whichever vector database has the best marketing this quarter.
LangChain
LlamaIndex
OllamaMost RAG projects that fail do so quietly: the system launches, sounds fine, and is wrong more often than anyone notices until a customer catches it. Our process is built to catch that before launch.
Choose how you want to work with us. Every model includes dedicated engineers, full IP ownership, transparent communication, and direct access to the people building your retrieval system.
Best for: scope that is already nailed down, and a price you want nailed down with it
Best for: products that will keep evolving long after version one ships
Best for: a skill gap today, or velocity you need by next sprint
Every RAG system we build starts with the same question: what does this business need answered correctly, and from what source.
A RAG system that sounds fluent but retrieves the wrong document is worse than no AI system at all, because it is wrong with confidence. We build for retrieval accuracy first and fluency second.
Recognised by leading platforms, startup ecosystems, and global technology communities.
Practical thinking from the Akoode team on retrieval accuracy, hallucination, and what actually makes a RAG system trustworthy.
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Straight answers on timelines, team shape, security, and how we plug into your existing delivery process.
Retrieval-augmented generation is a way of making an AI system look up relevant information from your actual documents or data before it answers, instead of answering purely from what a model learned during training. It is what keeps an AI system accurate about your specific business rather than just fluent in general knowledge.
Fine-tuning changes the model itself and is expensive to update every time your information changes. RAG keeps the model as is and retrieves current information at answer time, so updating your knowledge base means updating documents, not retraining anything.
In most cases yes, though not always alone. Many production systems now use hybrid search, combining a vector database for semantic matching with traditional keyword search for exact terms like account numbers or product codes.
A focused RAG application on a well-organised document set typically takes five to eight weeks from data audit to deployment. Messier document sets, or systems needing permission-aware retrieval across multiple sources, usually run ten to fourteen weeks.
It depends on the volume and complexity of the documents involved, whether permission-aware retrieval is needed, and how much evaluation and tuning the accuracy bar requires. We quote a fixed price after reviewing your actual data rather than a generic number.
Meaningfully more accurate for questions about your own business, since it answers from your actual documents instead of general training data. Accuracy still depends heavily on retrieval quality, which is why we test and tune that stage specifically rather than assuming it works out of the box.
In most cases yes. We can add a retrieval layer to an existing LLM feature so it starts answering from your data, without necessarily rebuilding the whole interface.
We design retrieval with access controls so the system only surfaces documents a given user is allowed to see, and we can keep the vector store and documents entirely within your own infrastructure or region if that is a requirement.
It should say so rather than guess. We design the generation step to recognise when retrieval did not turn up anything relevant and respond accordingly, instead of filling the gap with a plausible-sounding but unsupported answer.
Yes. Documents change and retrieval quality drifts as content grows, so we set up monitoring at launch and offer ongoing tuning plans, with a recommended review on a set cadence rather than a one-time handoff.
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