Bring your knowledge
Choose a useful collection of documents. Start with a focused task, a clear owner, and material your team is allowed to access.
HANDBOOKS · REPORTS · PROJECT NOTESLOCAL AI. USEFUL BY DESIGN.
Your knowledge.
Closer to hand.
Turn the documents you already have into answers your team can use. Local AI, grounded in your knowledge and built around the way you work.
Your infrastructure. Your documents. A practical place to start.
THE LOCAL KNOWLEDGE WORKSPACE
↳YOUR KNOWLEDGETeam handbookPDF 12 pages
≋YOUR DOCUMENTSProject notesDOCX 8 pages
✳ Your local assistant ●
Where do I find the answer?
Right here, in your knowledge.↗ Source included
Ask your documents ↑
✳
⌂ Built to run locally
LESS SEARCHING. MORE KNOWING.
✳ Designed for your workflow↳ Answers with sources⌂ A local-first architecture↗ Start small. Build with purpose.
01 / THE APPROACH
Less digging through files.
More getting things done.
Your team’s knowledge is spread across handbooks, reports, and folders. Retrieval-augmented generation (RAG) gives an AI assistant relevant material to work from before it answers.
Find the relevant context
The system indexes your documents and retrieves passages related to a question, within the access boundaries you define.
RETRIEVE · FILTER · GROUNDGet an answer you can check
A local model uses those passages to draft an answer with references. Your team can open the source and review what it says.
ASK · VERIFY · KEEP WORKING02 / A LITTLE LESS ABSTRACT
Ask a question.
See the source.
Explore a small, fictional company handbook. See how retrieving a relevant passage makes an answer easier to check.
Interactive retrieval demo
This example searches sample passages in your browser. It uses no language model and sends no questions to a server. Production RAG adds a model, document processing, access controls, and evaluation.
▤ Sample team handbook 3 passages▤ Sample security guide 1 passage
A good answer starts with the right context.
What would you like to find?
✳ READY WHEN YOU ARE
Try a question above, or ask your own. If the sample documents don’t cover it, we’ll say so.
View source passage
03 / BUILT AROUND REAL WORK
A useful assistant starts
with a useful problem.
“Where is that policy?”
Help teams navigate procedures and handbooks, with a clear path back to the original document.
“What have we already learned?”
Surface relevant passages across a defined research library. Review evidence before drawing conclusions.
“What was decided, and why?”
Make project notes and approved documentation easier to find when people join or responsibilities change.
LOCAL IS AN ARCHITECTURE CHOICE.
Keep the knowledge
close to the work.
A local deployment can keep document processing, retrieval, and model inference on your own hardware. We start by mapping what stays local, who needs access, and which tradeoffs matter.
Map out your first use case ↗AN EXAMPLE LOCAL DEPLOYMENT
YOUR INFRASTRUCTURE
▤ Approved documents
↓⌕ Local search index
↓✳ Local language model
↓↳ Answers + source references
Privacy depends on the full configuration, including logs, backups, integrations, and network access.
04 / GOOD QUESTIONS
A few things
worth knowing.
What does “local AI” actually mean?
The model and supporting services run on hardware you control, such as a workstation or an internal server. A fully local setup also keeps embeddings, document processing, and retrieval there. Hardware requirements depend on the model, document volume, and number of users.
Does RAG stop AI from making mistakes?
No. Retrieval gives a model relevant context, but the system can still retrieve the wrong passage or misinterpret a source. Source references, realistic evaluations, and a clear “I don’t know” behavior are essential. High-impact decisions still need human review.
Can we use the computers we already have?
Possibly. A first step is to assess your existing hardware against the use case. Smaller models can fit modest workloads; larger models, long documents, or multiple concurrent users may call for more memory or a dedicated GPU.
Is this public website connected to private documents?
No. This website and its fictional demo are separate from any client deployment. A production assistant would need its own secured environment and explicit decisions about document access and data handling.
What would a first project look like?
Choose one workflow, prepare a small approved document set, and define questions with known answers. Build a pilot, evaluate retrieval and answer quality, review access controls, and decide what is worth expanding.
START WITH ONE GOOD QUESTION.
What would you put
on your desk?
Tell us about the documents, the workflow, and the problem you want to solve. Email hello@aionyourdesk.com, or start with a short project brief you can keep and share.