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AI knowledge assistant that answers with its sources

Answers from your own documents, with the source attached

We build assistants that answer your team's questions from your documents and systems, link the source and follow each person's access rights.

Book a free consultation How we build agents
ask-the-team

  1. Account managerYour team

    What did we agree with this customer on rush orders, and is it still in their current contract?

  2. Knowledge agentAgent

    1. access.check what this person may open
    2. docs.search contracts and meeting notes
    3. crm.get_account account history

    The current contract allows rush orders with two days' notice. Sources linked. The sales wiki shows the old terms.

  3. Knowledge agentAgent

    1. wiki.propose_edit rush order terms, with source

    I drafted an update to the wiki page. The page owner approves it before the page changes.

    Needs a personUpdate an outdated wiki pageRush order terms, from the contract
    Approved by Page owner
Example data. An agent at work in a team channel, with a person approving the step you choose.
Mixam: conversion to a final sale from about 20% to about 40%
Where agents help

People ask colleagues because documents are hard to find.

An agent answers from your documents and systems and shows where each answer came from.

  1. 01

    Where it stallsThe same experts get the same questions every week.

    What we do An agent answers from your documents and links the source, so experts keep their time for new questions.

  2. 02

    Where it stallsSearch returns files when people need an answer.

    What we do The agent reads the relevant passages and writes a short answer that quotes the lines it used.

  3. 03

    Where it stallsNot everyone may see every document.

    What we do The agent checks each person's access first, so an answer only uses files that person may open.

What we build

What we build for internal knowledge

Whatever comes in, every project is built from the same parts below, set up for your files and your systems.

Knowledge we connect

  • Policies and procedures
  • Product documentation
  • Wikis and intranets
  • Support history
  • Contracts and templates
  • Engineering docs
  • HR and onboarding
  • Sales playbooks

How we build it

  • Retrieval over your documents

    Wikis, drives, manuals and tickets searched together, with the passage behind each answer.

  • Access rights in every answer

    The agent sees only what the person asking may open, using the permissions you already have.

  • Answers where your team works

    Team chat, intranet or learning tool, connected to the systems behind the answers.

  • Answer quality checks

    Your team's real questions become a test set, and every live answer is logged with its sources.

Not seeing your case? Tell us where your team looks for answers today and we start from there.

Tell us about your workflow
How we build agents

Agents that work as part of your team

They work in the tools your team already uses, take on whole tasks and stop for a person where you decide.

  1. Routines: agents that start on a schedule, on new GitHub issues or on an API delivery.
  2. Connected systems: GitHub, Linear, Notion, Slack, Jira and other tools an agent may call, each connected with its own sign-in.
  3. Approvals log: each action that waited for a person, with who asked, who decided and when.
Routines: agents that start on a schedule, on new GitHub issues or on an API delivery.
Connected systems: GitHub, Linear, Notion, Slack, Jira and other tools an agent may call, each connected with its own sign-in.
Approvals log: each action that waited for a person, with who asked, who decided and when.

Screens from the workspace our agents run in, with example data.

Where it runs

Teams and industries that use it

Each page shows where agents fit in that team or industry.

Engineering in the open

We help build what agentic AI runs on

Open standards, open-source tooling and published research, all public, so you can check our engineering before you hire us.

Shaping agentic AI

Agentic AI Foundation

Vstorm was the first agentic AI engineering company to join the Agentic AI Foundation and received its 2026 Open Agentic AI Innovation Award.

Read the announcement

Open source

We build open-source agent tooling that other engineering teams install

10M+ PyPI downloads
4,000+ GitHub stars
33 public repositories

Framework contributors

When the framework breaks, we fix it at the source

We maintain our own open-source agent framework and contribute fixes to the frameworks our agents run on, through official partnerships with their teams. Breaking changes reach us before they reach your codebase.

Research

Our engineers and PhDs publish peer-reviewed research

Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results.

Track record

Frontier AI teams now build on what we shipped first

Our client agents were in production before most of today's agent frameworks had a stable release. You get a team that already knows what breaks between a promising demo and a system people rely on every day.

Case studies
All case studies
Engagement models

Ways to start

Each path starts with a free call with a founder or senior AI engineer. A build goes ahead only after its Proof of Value meets the agreed threshold.

Start with one workflow

We map one workflow with your experts and agree a success threshold before any build.

Best whenYou want the success test agreed before you commit to a build.

Book a free consultation
  1. Process redesign and build Best whenYou want one team accountable for the outcome.
FAQ

AI knowledge assistants, answered

What is an AI knowledge assistant? +
An AI knowledge assistant is an agent that answers your team's questions from your own documents and systems: wikis, drives, manuals, tickets and the CRM. It retrieves the relevant passages, writes a short answer and links the sources, so people can check it. It follows the access rights each person already has, and says so when the documents hold no answer.
Which teams get the most from an internal knowledge assistant? +
Teams that answer the same questions from the same documents every week: support and sales checking product and contract details, HR answering policy questions, IT answering how-to questions, new hires looking for how things are done. Start with one team and one set of documents you trust, collect their real questions and agree an answer quality threshold before the build.
Have you built an AI knowledge assistant before? +
Yes. For ARIJ Network we built an agent inside its Moodle courses that answers only from ARIJ's own manuals and lecture transcripts, in English or Arabic, with a tool that checks the reply stays in the language of the question. For an engraved die manufacturer we built a conversational agent over decades of client interactions, with a SQL graph for the harder questions.
How does an AI knowledge assistant avoid made-up answers? +
It answers only from passages it retrieved, and each answer links them. When retrieval finds nothing relevant, the assistant says it does not know and can point to the right person. We combine keyword and semantic search, test answers on your team's real questions before launch, and log every live answer with its sources so wrong ones can be traced and fixed.
How do you know it works before the full build? +
We agree a success threshold with your team before the build and test the agent on your own cases in a Proof of Value. It measures accuracy and running costs, and the build goes ahead only when the threshold is met. Corrections from your team go back into the test set, so every later version is checked against them.
Who owns the system, and which models does it run on? +
You own the code, prompts, evaluation sets and rules by contract. The agents run in your environment on hosted or open-weight models. The model sits behind an interface in code, so you can change it when accuracy, speed or your hosting rules change, without rebuilding the agent.
Start with one workflow

Test one workflow on your own data

A Proof of Value shows accuracy and running costs before the full build.

Book a free consultationRead case studies
From the blog

Reading on AI knowledge assistants

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Agentic RAG: Architecture, Knowledge, Access Control

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Old-School Keyword Search to the Rescue When Your RAG Fails

Old-School Keyword Search to the Rescue When Your RAG Fails - Discover classic search methods in RAG; enhance your retrieval strategy today.