AI Agents & Chatbots
A private RAG knowledge assistant that answers your team's questions from SOPs, contracts, wikis and past tickets, with a link to the source every time.
What you get
Every growing company has the same hidden cost: people asking each other where things are. The answer to "what's our process for a customer who wants to cancel mid-contract?" exists somewhere, in a Google Doc, a SharePoint folder, a Notion page from two years ago or the head of a colleague who is on holiday. New hires take months to learn where to look.
A RAG knowledge assistant answers those questions directly. RAG, or retrieval-augmented generation, means the assistant first searches your own documents for the passages most relevant to the question, then writes an answer based only on what it found, with links back to the sources. It is not a model trained on your data. It is a search engine that can read and summarize, so it stays current when documents change.
We build these for operations, finance, HR, customer support and sales teams, usually inside Slack or Microsoft Teams so there is no new tool to learn. The hard parts are not the model. They are getting clean content in, keeping it fresh, respecting who may see what, and being honest when the answer is not there. If you would rather your customers use it, the same foundations power our customer support agents.
The problem
Documentation sprawl is normal. SOPs live in SharePoint, product specs in Google Drive, decisions in Slack threads, customer history in the helpdesk, and the definitive version of the returns policy is whichever one finance updated last. Search inside each tool works only if you already know the right words and the right tool.
The result is interruptions. Senior people answer the same questions repeatedly. New hires guess. Teams in different offices follow slightly different versions of the same process because they found different documents.
Plain enterprise search helps a little. A RAG knowledge assistant goes further: you ask a question in normal language, it finds the relevant passages across sources, and gives you a short answer with links so you can check. When the documents disagree or the answer is missing, a good assistant says so, which in itself tells you where your documentation needs work.
What we build
Every answer links to the documents and sections it came from, so people can verify it and read further.
Pulls from Google Drive, SharePoint, Confluence, Notion, your helpdesk and uploaded PDFs in one place.
Mirrors access rules from your source systems, so a question from sales never draws on HR or board documents.
Re-indexes documents when they change and can prefer the newest version when two documents conflict.
Declines to guess when nothing relevant is found, and logs the question so a content owner can fill the gap.
Available as a Slack or Teams bot, a web app, or embedded in an internal tool you already use.
Scenarios
A warehouse supervisor asks in Teams how to handle a short shipment from a supplier. The assistant answers from the receiving SOP, lists the steps and links the exact section and the claim form template.
Staff ask 'what notice period do we have in the Acme master services agreement?' and get the clause quoted with a link, instead of opening ten PDFs. Access is limited to people on that client team.
Support agents ask about edge cases, and the assistant searches internal macros, product docs and solved tickets to suggest how similar cases were handled before.
New starters ask about expense rules, holiday requests and system logins in Slack during their first weeks, and HR sees which questions come up most so they can improve the handbook.
How we build it
We start by auditing the sources. Which folders matter, which are archives, where the duplicates and contradictions are, and who owns each area. Then we build ingestion: parsing PDFs, Word files, slides and spreadsheets, keeping headings and tables intact, and splitting documents into passages that make sense on their own. Scanned documents get OCR. This step decides quality more than any model choice.
We combine vector search, which handles meaning ("time off" finds "annual leave"), with keyword search, which handles exact terms like part numbers and contract names. A reranker then orders the results before the model sees them. Everything is stored in Postgres with pgvector unless you have a reason to use a dedicated vector database. Metadata like document date, owner and department lets the assistant prefer current, authoritative sources. The wider architecture follows our LLM integration patterns.
Access control happens at retrieval time: we filter passages by the user's permissions before the model ever sees them. We do not rely on telling the model to keep secrets. Data stays in your cloud account or ours under agreed terms, and model providers are used on business API terms that exclude training on your data.
We collect real questions from your team before launch and record the correct answer and source for each. We measure two things separately: did retrieval find the right passage, and did the answer use it correctly. That tells us whether to fix content, search or prompts. After launch, thumbs-up and thumbs-down feedback and the list of unanswered questions keep improving both the assistant and your documentation. See our process for how pilots are run.
FAQ
No. Nothing is trained. The assistant searches your documents at question time and answers from what it finds. That means updates show up as soon as documents are re-indexed, and removing a document removes it from answers.
Yes, with the right parsing. Scanned documents need OCR and spreadsheets need care to keep rows and headers together. Very poor scans or handwritten notes may not extract reliably, and we will flag those during the audit.
We sync permissions from the source systems and filter search results by the user's access before the model sees anything. Highly sensitive areas can also be excluded from the index entirely.
The assistant can be set to prefer the newest or the officially owned version, and to point out the conflict when it finds one. Those conflicts are logged so the content owner can resolve them.
From a few hundred pages to hundreds of thousands. Larger collections need more care around ingestion time, metadata and search tuning, but the approach is the same.
No. We can deliver it as a private web app with single sign-on, or embed it in an existing internal tool. Chat apps are simply where most teams already ask questions.
Tell us where your team's knowledge lives, roughly how much there is and which questions come up most. We will suggest a pilot scope, often one team and one or two sources, so you can judge the results before going wider. Related: operations solutions.
We pick tools for your situation, not ours. These are the ones we reach for most on this kind of work.
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Retrieval-augmented generation (RAG) lets an AI assistant answer from your own documents instead of guessing. Here is how it works, what goes wrong, and how to build one that people trust.
Tell us how it works today. We'll tell you honestly what's worth automating, what it would take, and what it would cost.