AI Knowledge Assistants (RAG)
We build assistants that answer your team's or your customers' questions using your own documents and data: manuals, policies, contracts, tickets, knowledge bases. Each answer cites the document it comes from and only uses the information the user is allowed to see. When the answer is not in your sources, the assistant says so instead of making it up.
Who It's For
Organizations whose knowledge is scattered across shared drives, intranets, wikis and email, where people lose time looking for the right version of a document or asking the same expert again and again. Typical teams: customer service and support, operations, HR, legal and compliance, sales and onboarding of new staff. Ideal for leaders who want an assistant that answers with company information, under its security rules, and that can be audited.
Deliverables
What You Gain
Answers in seconds, not searches in folders
Instead of opening ten documents to find a clause or a procedure, people ask in plain language and receive a concrete answer with the link to the source so they can verify it.
Verifiable answers with citations
Every answer shows which document and section it comes from. If the information is not in the sources, the assistant says it does not know. That builds trust and makes errors easy to spot and correct.
Each user sees only what they are allowed to
The assistant respects the permissions of the original systems: a person from sales does not receive information from HR or finance documents they cannot open today.
Faster onboarding and less dependence on experts
New team members find the answers to their questions without waiting for a senior colleague, and experts stop answering the same question every week.
You learn what people ask and cannot find
Questions without an answer reveal outdated or missing documentation. The usage dashboard turns that into a concrete list of content to create or fix.
A base for future agents
Well-organized, indexed and permission-aware knowledge is the foundation that AI agents need later to make decisions with reliable context.
Best Practices
Clean up the sources before indexing
An assistant is only as good as the documents it reads. Identify the official version of each document, remove duplicates and obsolete versions, and assign an owner to each source. Indexing everything "just in case" produces contradictory answers.
Measure with real questions
Collect the questions people actually ask, with the correct answer and its source, and use them to evaluate the assistant on every change. Measure whether it finds the right document and whether the answer is faithful to it.
Permissions from day one
Apply access filters at retrieval time, based on the user's identity and the permissions of the original system. Adding security at the end usually means rebuilding the index and redesigning the integration.
Keep the index alive
Documents change every week. Automate the update of the index when a source changes, and review unanswered questions regularly: they show which content is missing or outdated.
Patterns & Practical Cases
A reliable knowledge assistant combines three pieces: retrieval that finds the right fragments (often mixing semantic and keyword search), a model that writes the answer using only those fragments, and an evaluation layer that checks it. The quality usually depends more on how the documents are prepared and split than on the model chosen.
Internal assistants for teams
Typical cases: a support assistant that answers with product manuals and resolved tickets; an HR assistant that answers questions about policies, benefits and procedures; or an assistant for the legal or compliance area that locates clauses and obligations across contracts and regulations, always with the reference to the original text.
Assistants for customers and partners
An assistant on the website or the customer portal that answers with public documentation, conditions and FAQs, and hands over to a person when the question requires it; or an assistant for distributors and partners that answers with catalogs, technical sheets and commercial policies.
Key patterns
Technology Ecosystem
Managed AWS capability to connect documents from S3 and other sources, index them and answer with citations using foundation models, without operating the ingestion pipeline yourself.
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Open-source extension that adds vector search to PostgreSQL. It lets you keep embeddings next to your business data and permissions, using a database your team already knows.
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The standard language for document processing, ingestion pipelines and assistant evaluation, with mature libraries to extract text from PDFs and office files and to measure answer quality.
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Microsoft's search service with vector and hybrid search, integrated with SharePoint, Microsoft 365 and Entra ID. A natural option when your documents already live in the Microsoft ecosystem.
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Google Cloud's enterprise search and answer service, with connectors to Google Workspace and other sources. Recommended when your documents and data already run on Google Cloud.
Learn more →Do you like turning documents into useful answers?
We are looking for engineers with experience in search, data and LLMs who care about the quality of each answer: evaluation, permissions and maintenance included.
What to Avoid
Indexing everything without curating
Loading every folder with drafts, old versions and duplicates guarantees contradictory answers. Choose the official sources first, with an owner who keeps them up to date.
Ignoring permissions
An assistant that answers anyone with any document can expose salaries, contracts or customer data. Permissions must be applied in the search, not only in the user interface.
Answers without a source
If the user cannot see where the answer comes from, they cannot verify it, and a single convincing error destroys trust in the tool. Always show the citation and allow "I don't know".
Treating it as a one-time project
Without re-indexing, evaluation and someone reviewing unanswered questions, the assistant becomes outdated in a few months. Plan its operation from the start, with clear owners.
Which documents does your team search the most?
KNOWLEDGE,
ANSWERING
WITH
SOURCES