An AI agent that answers every internal question from your company's own knowledge.
Ask in plain language, get an answer in seconds, with the source attached.
The problem
You already have the answer. It's just no one can find it.
Every company accumulates knowledge: policies, handbooks, runbooks, product docs, meeting notes, project archives, contracts, wikis. The knowledge exists. Finding it is another matter.
So the same internal questions get asked over and over: what's the expense policy?, who owns this service?, how do I request access?
More often than not, it's the same few people who answer them by hand, again and again, instead of doing the work they were hired for.
The usual places those answers live don't hold up very well:
- The wiki goes stale the moment a process changes, and nobody trusts a page with a two-year-old timestamp.
- Internal search finds documents, not answers. It hands you twelve PDFs and wishes you luck.
- The people who know become the help desk. Every senior person spends part of their week answering the same twenty questions, and when they leave, the answers leave with them.
What a company actually needs is a company brain: its knowledge made answerable, so anyone can ask it anything in their own words and get a straight answer with a source attached.
How we approach it
We point the agent at your knowledge base.
Here's what we don't do: we don't train a custom model on your data.
We stand an agent up on top of the documents the company already produces, curating what it knows and how it talks.
We make your documents the source of truth
Everything the agent says traces back to the materials you own, like handbooks, policies, runbooks, project notes. Those documents go into a knowledge base as they are (PDF, text, Markdown, web pages), and the platform reads them, breaks them into searchable passages, and indexes them by meaning.
Ask "how many days of remote work are allowed?" and you get the number from the actual policy, not a plausible-sounding hallucination. Ask a follow-up like "and does that apply to contractors?" and it understands that "that" means the policy you just asked about.
When a policy changes, you update the document, not the platform.
We make it answer with receipts
An internal agent that guesses its answers is worse than no agent at all. So ours doesn't guess. It looks up the relevant material first, answers only from what it finds, and shows you the source.
A great model is useless without the right information in front of it, and that's where we put the work: getting the information right.
We keep humans in the loop
The agent knows what it doesn't know. When it isn't confident it has the right answer, it says so and hands the conversation to a person, with the full history attached. Those handoffs are useful on their own. Each one points to a gap in the knowledge base, and that's how the agent keeps improving.
The approach
Ground it in your own knowledge · answer with sources · know when to hand off to a human.
What we build
One agent, the whole company's knowledge behind it.
The screen stays deliberately plain: a greeting, a few things to try, and a box that says ask me anything. The knowledge search happens underneath. What the team sees is an answer in seconds, in their own language, with the source a click away.
Starter questions that break the ice
A blank chat box is intimidating. People don't know what it's allowed to answer. So the agent opens with starter questions drawn from what the team actually asks. And they're configurable, so the ice-breakers match your company, not a template.
It knows its limits
Every answer carries a confidence check. When the agent isn't sure it has the right information, it doesn't improvise. It escalates to a human, alerting the team with the full conversation attached so nobody has to ask "can you repeat the question?". We learned early that this boundary is a feature, not a limitation: every escalation reveals a gap in the knowledge base, and closing those gaps is how the agent earns the team's trust.
Live where your team already works
The same agent ships several ways, and a company can use any or all of them:
- A standalone page you can share as a single link in an onboarding email or an internal portal.
- An embeddable widget, a floating "ask" button that drops onto the intranet or any internal tool with a short snippet.
- Chat channels (e.g. email, Slack, WhatsApp), so the agent answers where the conversation already happens: a message away, on desktop or phone.
- An API, for teams that want the agent inside their own apps and back offices.
One knowledge base, one configuration, many front doors. Set it up once, then meet people wherever they already are.
Proven in production
We've shipped this pattern before.
Knowledge Intelligence isn't a slide, it's a pattern we've put into production more than once:
- A support agent for TAIKAI, answering platform questions from a curated knowledge base and escalating to humans when unsure, running for a fraction of the cost of commercial tools. We wrote about how we built it.
- A community assistant for DeHouse, answering members' questions where the community already talks.
- The assistant inside Cadence, where an operator can ask anything about a panel, a prediction, or the production history and get an answer from the factory's own data.
- Ask SIM, a public concierge that turned a conference program into something attendees can just ask.
Different companies, different knowledge, same machinery: documents in, grounded answers out, humans in the loop.
How it works
Turning a pile of documents into something you can just ask.
You don't need to see the plumbing to trust the answers, but here's what happens underneath. It's the same machinery for any company, document set, or knowledge base.
Making knowledge answerable
When a document goes into the knowledge base, the platform:
- Reads it. Clean text out of PDFs (including scans and tables), text files, Markdown, or a web page.
- Breaks it into passages. Overlapping chunks, precise but still meaningful.
- Indexes it for meaning. Each passage becomes a vector in a vector index, so search matches on what a question means rather than its exact words.
That's why "can I work from abroad?" can land on a remote-work clause that never uses the word "abroad."
Answering a question
- The question is turned into the same kind of searchable vector.
- The most relevant passages from the knowledge base get pulled back.
- Those passages go to the model as ground truth, with an instruction to answer from them and cite the source.
- The answer streams back word by word, so it feels instant on any device.
A follow-up gets rewritten to stand on its own first, so the search still finds the right passage. And when confidence is low, the agent hands off to a human instead of guessing.
Security & privacy
Built to be safe inside a company.
An agent with access to internal knowledge comes with guardrails from the platform. Access control keeps answers inside the company, rate limiting absorbs spikes and abuse, an append-only audit log records what was asked and answered, and GDPR and EU AI Act considerations are handled at the platform level, with EU hosting available.
And when documents can't leave the building, the whole stack can run inside the company's own perimeter, on-premise and with local inference: the models run on your infrastructure, and nothing is sent to a third party.
What it delivers
Questions answered the moment they're asked, with a human in the loop.
| Layer | Choice |
|---|---|
| Knowledge | Documents (PDF, text, Markdown, web pages) chunked, embedded, and stored in a vector index for semantic retrieval |
| Answering | Retrieval-augmented generation. Relevant passages retrieved, then answered from, with sources, conversation memory, and streaming responses |
| Human in the loop | Confidence thresholds · escalation with full conversation history · alerts to the team's channel of choice |
| Surfaces | Standalone chat page · embeddable widget · chat channels · API |
| Safety | Access control · rate limiting · append-only audit log · GDPR / EU AI Act handling · EU hosting · on-premise deployment with local inference |
The repetitive questions about policies, processes, tools, and history get answered the moment they're asked. Onboarding stops depending on who's free to help, senior people stop being the help desk, and the knowledge stays when people move on.
Beyond internal Q&A
Any body of knowledge, made answerable.
Internal questions are the showcase, not the limit. The same capability fits any document set people have to dig through.
Wherever there's a pile of information and a stream of people asking the same questions, you can turn it into a company brain that answers in plain language with the source attached.
Let's build your company brain.
We build the AI and knowledge systems that turn a company's own information into something anyone can just talk to, pairing real depth in AI and search with modern software practice and built to be safe, secure, and compliant for the EU.
If that sounds like your situation, let's talk.
Let's Talk