Generative AI Integration (LLM/RAG)
Answers grounded in your documents, with a citation attached — not a fluent guess.
The fastest way to ship "generative AI" is to wrap an API call around a general-purpose LLM and point it at your product. It sounds fluent. It also confidently states things that are not in your documentation, cites policies that do not exist, and gives no indication to the user that anything is wrong — because a fluent wrong answer looks exactly like a fluent right one. That gap is where trust in the feature dies, usually within the first week of real usage.
We treat retrieval as the actual engineering problem, not the LLM call. A generative AI feature is only as trustworthy as what it is grounded in — so we build the retrieval pipeline around your real documents and data, tune it so the model answers from what it actually found rather than what it predicts is plausible, and surface the source it is citing so a wrong answer is checkable, not invisible. Where hallucination risk is genuinely high, we add verification steps rather than trusting the model's own confidence.
What's Included
Retrieval pipeline built on your data
A document ingestion and retrieval system indexed on your actual content — internal docs, product data, knowledge base — tuned for relevance, not a generic vector search default.
Deployed LLM feature with source citations
The generative feature live in your product or internal tool, with every answer traceable back to the source document or data it drew from.
Hallucination and accuracy evaluation report
A structured test of the system against a representative set of real questions, measuring how often it answers correctly, cites correctly, or correctly declines to answer when it does not know.
Prompt and retrieval tuning documentation
Documented prompt structure, retrieval configuration, and the reasoning behind them, so your team can maintain and adjust the system without starting from scratch.
Our Approach
Ground the system before tuning the prompt.
We build and validate the retrieval pipeline against your actual documents first — chunking strategy, indexing, relevance ranking — because a well-grounded system with a mediocre prompt beats a brilliant prompt with weak retrieval.
Measure hallucination directly, do not assume it away.
We build an evaluation set from real questions your users would actually ask and measure the answer and citation accuracy against it, rather than relying on the model feeling fluent in a quick manual check.
Add verification for high-stakes answers.
Where a wrong answer has real cost — compliance, financial, or medical-adjacent contexts — we add an explicit verification or confidence-scoring layer rather than shipping the raw model output.
Frequently Asked Questions
Retrieval-augmented generation means the model answers based on documents it retrieves from your actual data at the time of the question, rather than only what it learned during training. This is what lets it answer accurately about your specific product, policies, or internal knowledge instead of general internet knowledge, and it is what makes citations possible — the model can point to the exact source it used.
We build a test set of realistic questions with known correct answers pulled from your actual content, run the system against it, and score each response on whether it answered correctly, cited the right source, or correctly said it did not know. We report that number honestly rather than a marketing claim, and we retest after any change to the retrieval or prompt configuration.
A focused RAG system over a defined document set — for example, an internal knowledge assistant over your policy and product docs — typically costs ₹9-16 lakh and takes 7-10 weeks including evaluation. Systems requiring integration across multiple live data sources, or verification layers for regulated content, run ₹20-40 lakh over 14-20 weeks.
Generally yes — we design the retrieval and application layer to be model-agnostic where possible, so you are not locked into one provider. We will recommend a specific model based on your accuracy, latency, and cost requirements, and explain the tradeoffs rather than defaulting to whichever is newest.
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