The answer exists. Nobody can find it
It is in a document somebody wrote two years ago, in a ticket thread, in a spreadsheet, in a policy nobody has read since onboarding. So the question gets asked in a channel, somebody answers from memory, and the same question gets asked again next month.
General purpose AI tools do not solve this, because they have never seen your information. What works is a system grounded in your own content, which is an engineering problem.
What we build
Internal copilots
An assistant that knows your product, your processes and your history, available where your team already works.
Knowledge assistants
Questions answered from your documentation, wiki, tickets and past decisions, with the source cited so the answer can be checked.
Document intelligence
Contracts, invoices, reports and forms read, extracted, compared and summarized, with the figures traceable to the page they came from.
Content generation tools
Drafting inside your templates, your terminology and your tone, for the people who publish it to review and approve.
Meeting and conversation intelligence
Calls and meetings turned into summaries, decisions and follow-up records that land in the system where they belong.
Analysis assistants
Natural language questions against your own data, returning an answer and the query behind it.
Internal chat interfaces
Where a conversation is the right way in, connected to real systems rather than to a static document dump.
Why grounding is the whole job
Get the content in
Ingestion from the systems where your knowledge actually lives, with permissions preserved, so a person only ever gets answers from documents they are allowed to see.
Make it retrievable
Chunking, embeddings, hybrid search and reranking.
Ground the answer and cite it
The model answers from retrieved content and shows where it came from, so a user can verify rather than trust.
What keeps people using it
Seven properties that decide whether an internal assistant becomes part of the day or gets abandoned in a month.
Permission-aware retrieval
The assistant respects the access controls your systems already have. Nobody sees a document through the assistant that they could not open directly.
Citations on every answer
Traceable to the source, so trust is verifiable.
Refusal when the answer is not there
A system that says it does not know is more useful than one that guesses well.
Freshness
Content re-indexed as it changes, because an assistant answering from last quarter’s policy is worse than no assistant.
Measured answer quality
An evaluation set of real questions with known answers, run on every change.
Your data stays yours
It is never used to train a model. Deployment inside your own boundary where compliance requires it.
With one team and one body of knowledge
A single department, a single document set, real questions from real people. Internal tools succeed or fail on adoption, and adoption is easier to earn in one team than across a company.
Pick one team and one question
The question your team keeps asking each other. We will tell you what it would take to answer it properly and where the content would come from.
Ready to Start Your Journey?
I would love to talk to you about your project or needs.
Fill in the form or send us an email to hello@somniosoftware.com
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Questions worth asking
Still have some doubts?
No worries, here are some frequently asked questions that may help you.
Mostly for making existing knowledge usable: assistants that answer from your documentation, tickets and past decisions, tools that read and summarize contracts, invoices and reports, drafting inside your templates and tone, and turning meetings into summaries and follow-ups.
RAG stands for retrieval augmented generation. Instead of answering from what a model learned in training, the system first retrieves the relevant passages from your own content and then has the model answer from them, with the source cited. Most of the engineering is in the retrieval.
Uploading files works for a few documents and one person. A production system ingests content from the systems where your knowledge lives, preserves permissions, retrieves the right passage with hybrid search and reranking, cites its sources and is measured against real questions. It also stays current as your content changes.
Retrieval is permission-aware by design: the assistant respects the access controls your systems already have, so nobody sees a document through the assistant that they could not open directly.
It says so. The assistant answers only from retrieved content, and when the answer is not there it refuses rather than guessing. A system that says it does not know is more useful than one that guesses well.
Content is re-indexed as it changes, so answers come from the current version of a document. We also keep an evaluation set of real questions with known answers and run it on every change to confirm that quality holds.
The ones where your knowledge actually lives: documentation, wikis, tickets, spreadsheets and your own data. Connecting each source is integration work we do as part of the project, with permissions preserved.
Yes. Where compliance requires it, we deploy inside your own boundary. Your data stays yours and is never used to train a model.