Futurelab builds RAG (retrieval-augmented generation) chatbots and AI knowledge bases. Content from SharePoint, Drive, wikis, PDFs, tickets and databases is collected, cleaned, indexed and retrieved so the model answers only from your documents, cites each source, respects permissions and is tested against real questions.
RAG chatbots and AI knowledge bases
We build RAG chatbots and AI knowledge bases that turn your documents, policies and data into answers people can trust: retrieved from the right place, respecting who is allowed to see what, and cited every time.
Futurelab Studios: OpenAI Select Partner · Claude Partner Network
Trusted by teams at PepsiCo · ITC · SBI · Syngenta
RAG means the model reads your documents before it answers, instead of guessing from memory. Quality depends on every step, not just the model. Step through them.
Connect the sources that hold the answers, such as SharePoint, Google Drive, wikis, PDFs, tickets and databases, and keep them in sync as they change.
Remove duplicates and outdated versions, read tables and scans properly, and split documents into passages that keep their meaning and their permissions.
Store each passage so it can be found by meaning as well as by keyword, with metadata such as owner, date and who may see it.
For each question, search for the most relevant passages the person is allowed to see, and rank them so the best evidence comes first.
The model answers only from the retrieved passages, cites each source, and says so plainly when the documents do not contain the answer.
Test against a set of real questions with known answers, track accuracy over time and fix gaps in the content or the retrieval.
Futurelab builds and runs retrieval products of its own. Client knowledge bases start from what we learned shipping these; status is shown as it stands today.
A RAG chatbot over your own documents: accurate answers instantly, with the evaluation work that makes them trustworthy.
An AI knowledge base: keeps what your organisation knows searchable, organised and up to date.
An AI second brain for enterprises: captures and connects ideas, meetings and decisions so they can be recalled, now in beta.
Retrieval over the outside world: gathers and summarises market research with sources kept, now in beta.
Most knowledge is written down somewhere. The problem is finding it in time. A good AI knowledge base fixes that without replacing the systems you already have.
People ask in plain language and get the answer with its source, instead of searching folders or waiting for the one colleague who knows.
Answers come from current, approved documents, so teams stop working from old copies and conflicting advice.
Expertise held in documents and past work stays available when people move on, and new joiners get up to speed faster.
No source, no answer A knowledge base people trust shows where every answer came from, and admits when the documents do not say. That is a design choice, and we make it on day one.
Each one starts from a defined set of sources and the questions people really ask, and is tested against those questions before launch.
Instant, cited answers from HR, finance, IT and operations policies.
Help agents and customers find the right answer from manuals and past tickets.
Make specialist knowledge in reports, research and past projects easy to reuse.
Combine document answers with figures from your databases and spreadsheets.
Four steps, each with a clear output, so you can test answer quality on your own content before you roll it out.
Map the sources, owners and permissions, and collect the questions people actually ask, with the right answers.
You get: A source map and an evaluation set
Connect, clean, split and index the content, and build retrieval and cited answers on top.
You get: A working knowledge base on your content
Score accuracy, citations and refusals against the evaluation set, and fix content or retrieval until it holds up.
You get: An accuracy report you can share
Launch in the tools people already use, keep sources in sync and review unanswered questions regularly.
You get: A live knowledge base with owners
Embedded deployment with the full advisory team—custom AI tools that shortened planning cycles and improved coordination.
Technology leadership and product delivery—from AI Sachiv at state scale to meeting intelligence and learning platforms adopted as a digital public good.
RAG stands for retrieval-augmented generation. Before answering, the chatbot searches your documents for the most relevant passages and writes its answer only from them, with citations. It is how you get answers grounded in your own knowledge rather than the model’s memory.
An AI knowledge base lets people ask questions in plain language across your documents and data and get direct, cited answers, instead of searching folders and reading files. It sits on top of the systems you already use.
Common sources include SharePoint, OneDrive, Google Drive, Confluence and other wikis, PDFs including scans, support tickets, and databases or spreadsheets. We keep them in sync so answers reflect the current version.
Permissions are carried through from the source systems to every passage, and retrieval only searches what the person asking is allowed to see. We test this explicitly before launch.
We build an evaluation set of real questions with known answers, score accuracy, citations and correct refusals against it, and re-run it whenever content or models change.
For answering questions from documents that change, usually yes. RAG keeps answers current without retraining, shows its sources and respects permissions. Fine-tuning is better suited to teaching a model a style or a narrow task.
Tell us where your knowledge lives and who needs it. We will come back with a practical view of the sources, the questions to start with and what good accuracy looks like.