Generative AI development company in India
We build assistants, internal copilots, drafting tools and search that work from your own documents. Answers cite their sources, and access rules sit inside the search index, so people only get answers from documents they may read.
What we build
Question answering over your contracts, policies, tickets, manuals or codebase, often called enterprise RAG. We build all of it, from loading the documents to the screen people use, with citations so a person can check the source. More on RAG development.
Reports, summaries, responses and proposals drafted from structured inputs. A person reviews each draft before it goes to anyone outside your company.
Assistants for your own staff, grounded in your internal documentation. They follow the same access rules the documents already carry.
Two behaviors matter most: handing over to a person cleanly, and saying “I don't know” rather than inventing a policy. More on AI chatbot development.
Search that understands what people mean instead of matching exact words. It finds the right documents and writes nothing itself.
An evaluation harness: a scored test set, often called a golden set, built from your real queries. It shows whether a change made answers better or worse. More on LLM development.
This is one part of our AI development work. If you need AI that takes actions in your systems rather than drafting, see AI agent development.
How it works
Here is the path a question takes. An answer can go wrong at any of the steps before the model writes a word.
Retrieval-augmented generation (RAG) means the model answers from passages of your documents that a search step hands it, not from its own training. That is also how you get citations.
The system has two halves: search finds the passages, then the model writes from them. RAG development shows how we measure the search half on its own.
Not sure you need retrieval at all? Depending on how complex your data is and how fast answers must come back, we help you choose between a better prompt, retrieval over your documents, fine-tuning (training the model further on your own examples) or a chat assistant. LLM development explains how we decide.
What grounding can't promise. Retrieval makes wrong answers rarer and easier to catch. It does not end them. A model can still be confidently wrong, so every build needs evaluation, human review where the stakes justify it, and a plan for what happens when it is wrong. Our pre-launch security review is a point-in-time check, so later changes to prompts, data or the model need a review of their own.
Related questions
This page covers AI that drafts and answers from your own documents. These pages answer the questions next to it.
How we work
You see answers from your own documents early, and nothing goes live before our security team has checked it.
We start with the documents the AI must draw on, who may read them, and the questions people really ask. Those questions become the test set.
We bring the documents in, split and index them with their access rules, and check that the right passages come back before the model writes anything.
The assistant, copilot, drafting tool or search screen your people use, built on top of the search.
Each change to a prompt, the search or the model is scored against the test set, so an edit can't quietly make answers worse.
A separate security team checks the system before it goes live, including whether a restricted document can reach an answer. The method is on generative AI security testing.
For your engineers
The engineering behind the path above, for the people who will run the system.
Source documents come in with their access control lists (ACLs: the record of who may read each one). We split them into chunks along context boundaries, embed each chunk as a dense vector, and store the vectors with their ACLs in a vector store: pgvector when you already run PostgreSQL, Qdrant at larger scale.
Hybrid retrieval: BM25 keyword search and vector search run together, so exact terms and paraphrases both count. A cross-encoder reranker then scores the top-K candidates for relevance before any of them reach the model.
Each query carries the user's identity and, in a multi-tenant system, the tenant's ACL. Both the keyword search and the vector search filter on those permissions inside the index, so a document the user may not read is removed before the model's context is built. The index holds a copy of your permissions, so we plan how a change in your source system reaches it. RAG development explains the delay this can cause.
FAQ
What it is, when you need retrieval, and which model. For anything else, ask us directly.
Generative AI development means building on models that produce text, images, code or audio rather than a score or label. Commercially that covers chatbots, copilots, summarization, document drafting and semantic search. The engineering difference from ordinary software is that output is not deterministic, so you need evaluation, human review where the stakes justify it, and a decision about what happens when the model is confidently wrong.
RAG, retrieval-augmented generation, means the model answers from your documents rather than its own training. You need it when answers depend on information the model was never trained on. It is also how you get citations, which let a person check the source.
Yes. The build covers the retrieval half, from how documents are split to how results are reranked, and the application a person uses on top of it. Usually on pgvector when you already run PostgreSQL, or Qdrant at larger scale. Which approach a problem needs is set out on LLM development, and how the retrieval half is measured on RAG development.
Usually, yes. Whether the right passages came back is a different question from whether the answer was right, so we score retrieval on its own. RAG development sets out how the retrieval half is measured.
It depends on your task, your data residency rules and how fast answers must come back. We compare two or three candidate models, such as Claude, GPT, Gemini or Llama, on an evaluation set built from your own questions. LLM development sets out whether a given failure then needs a better prompt, retrieval or a fine-tune.
Yes. What an assistant is built to refuse, such as inventing a refund policy, and the criteria that check it are set out on AI chatbot development. How a public assistant is tested is on AI security.
Let's talk
Tell us what you want AI to draft or answer, and which documents it should work from. You hear back from someone who does the work, not a sales rep. We reply within one working day.