We design and stand up the retrieval layer: the vector database, the chunking and embedding strategy, and the ranking that decides which of your documents the model actually reads before it answers.
We turn the messy reality of PDFs, contracts, tickets, and wikis into structured, retrievable knowledge, so the answer that already exists somewhere in your systems can finally be found.
We wire the model to retrieve first and cite its sources, so every answer is traceable to the document it came from and a reviewer can check it instead of trusting it blind.
We enforce document- and row-level permissions on retrieval, so the model surfaces only what a given user is allowed to see, and a chatbot never leaks a document to the wrong person.
An LLM with no grounding invents plausible answers from its training data. On your policies, contracts, and product details, plausible and correct are not the same thing.
The knowledge is real and already written down, scattered across wikis, drives, and ticket histories. People re-ask, re-derive, or escalate because search never surfaces it.
A retrieval system with no access controls will happily return a salary sheet or a contract to whoever asks. Governance on retrieval is not optional in an enterprise.
RAG connects a language model to your own documents and data so it answers from your sources instead of its training data. The model retrieves the relevant material first, then answers with a citation. It is how you get an AI assistant that knows your policies, contracts, and product details rather than guessing at them.
Usually yes. A vector database stores your documents as embeddings so the model can retrieve the passages closest in meaning to a question, not just keyword matches. We select and stand up the right one for your scale and latency needs, and design the chunking and embedding strategy that decides retrieval quality.
A general chatbot answers from what it was trained on, which is not your business. RAG grounds the model in your own documents, returns a citation, and respects who is allowed to see what. The difference is a system you can trust with an internal or customer-facing question, instead of one that sounds confident and is sometimes wrong.
How do you stop the model from leaking documents to the wrong people?
We enforce access controls on the retrieval layer, not just the interface. The model can only retrieve and cite documents a given user is permitted to see, mirrored from your existing permissions. Anything sensitive stays behind the same wall it already sits behind.
Yes. Intelligent document processing is part of the work. We turn PDFs, scans, contracts, and wiki pages into clean, structured, retrievable content, because retrieval quality is only as good as what you feed it. Skipping this step is why most internal AI search tools disappoint.
A scoped RAG capability on a defined document set typically ships in 6 to 10 weeks, including retrieval design, access controls, and evaluation. Broader rollouts across many sources take longer, but we sequence the work so you get a governed, usable result on the first document set early.
RAG consulting: retrieval-augmented generation grounded in your documents, with vector databases, citations, and access controls, so AI answers from your data.
There is no flat rate. These are the factors that move the effort and the price.
A few clean wikis is a different job than millions of scanned contracts across formats.
A tolerant internal tool is lighter than a customer-facing answer that must be right every time.
Document- and row-level permissions across many roles add design and testing.
Wiring into your systems and proving answer quality against a benchmark is part of doing it right.
You want an AI assistant that answers from your own documents, with citations.
The answer already exists somewhere, but people cannot find it.
A general chatbot is too risky because it makes things up on your content.
You need answers on certified numbers, not documents: see Semantic Layer Engineering.
You need the agent to take actions, not just answer: see AI Agent Consulting.
The underlying data is not modeled yet: start with Data Foundation.
A language model on its own answers from what it was trained on, which is not your policies, your contracts, or your product docs. Retrieval-augmented generation grounds the model in your own sources, so it answers from what is true for your business and cites where the answer came from. We build the retrieval layer, the vector store, and the governance that make it trustworthy.
Retrieval-augmented generation (RAG) connects a language model to your own documents and data, so it answers from your sources instead of its training data, with citations. Thinklytics builds the full retrieval stack: document processing, the vector database, the semantic layer the model reasons on, and the access controls that keep each answer grounded and governed.
When the AI needs to take an action, not just answer. Bounded scope, approval gates, and audit logs.
For questions about numbers, not documents. One certified definition the model reasons on.
The access controls and ownership that decide what the model is allowed to retrieve and show.
Start with an audit. We review your documents, your sources, and your access model, then give you a scoped RAG plan before any work begins.