Knowledge Pods routes every question to the right tool — a search across your documents, an analysis that writes and runs real code over your spreadsheets, a drafted report with its sources attached. Around it: Pods, permissions, alerts, automations, dashboards, batch reading, a drafting suite and small internal apps your team builds by asking. One shared context, ready to move on‑premise.
Your admin picks the models. These are the tools your team actually uses every day, all sharing the same files, permissions and audit log.
Each Pod holds one area’s documents with its own AI. Ask a question in plain English and get an answer with the exact source attached. Ask across several Pods at once when you need the bigger picture.
Give each person Owner, Editor or Viewer rights, Pod by Pod. The AI, dashboards and automations follow the same rules, so nothing slips out through a summary. Sharing stops at your organization’s edge.
Describe what you want to watch for in a sentence. When a new document or email meets the condition, you get an email with a link to the signal — never the document, never the matched text.
Turn any question into a standing order. Run it every morning or every Monday. It plans the work, retrieves and computes in parallel, drafts the result and emails you a link.
Most of what you need to know lives in PDFs, contracts and emails that normal reporting can’t read. Knowledge Pods pulls the figures out and keeps a dashboard current, with every number traceable.
Scans, phone photos, handwritten notes, foreign‑language invoices. It recognizes each document, pulls out the fields that matter, and files the whole batch into a Pod you can search right away.
Ask for a board pack, a client report or a forecast. It uses your own figures and your templates, and runs each part on the model best suited to it: research, writing, design and checking.
Describe the thing your team keeps doing by hand. It builds a working page, shows you what it wants to reach, and you approve every connection before it has one.
No AI expertise needed on your side. We do the setup the way a good consultant would — in a working session on your real files — and you keep editing it afterwards without us.
A session about your files, your team and the numbers you watch — and what eats the week.
Google Drive, SharePoint and OneDrive, read‑only. You pick the folders. Nothing is used to train AI.
Set up with you on your own files — and yours to edit afterwards, without us.
We test models on a set built from your real tasks, and change the model for a job without touching a prompt.
Each job is pinned to a capability, not a vendor. When something better ships, change the model for a job in Settings — no prompts to rewrite, no migration.
Before we change a model on you, we run it against a test set built from your real tasks — not public benchmarks.
A draft from one model, a check from another. Pipelines are tuned as a whole.
Add your own provider keys or self‑hosted endpoints. Any job can run on them.
A document is read a single time and the answer is kept. Every dashboard refresh, batch re‑run and computed alert after that replays a saved program — no model, no tokens. Routine questions go to a small model; only real analysis reaches a frontier one.
fewer tokens per month,
for the same work.
And the AI follows the same rules. A summary can’t leak what the reader couldn’t open — the model never decides access, and reaches a document only through a short‑lived token naming the Pods you can open.
Point the whole deployment at your own endpoint — your cloud contract, your self‑hosted model — and nothing reaches a provider you haven’t chosen.
Read‑only connections, providers contracted not to train on your data, or your own model — where the question doesn’t arise.
Every model and tool in the harness is chosen so it can run inside your walls. Same workspace, same Pods, same automations — it just moves.
We provision your organization; you manage members, roles and seats. Encrypted, isolated per organization.
one organization, your peopleDeploy into your own Azure subscription. Use your own model contracts and keys.
docker compose up -dPods, dashboards and automations on your hardware, with a model you host. Fully air‑gapped needs a local OCR service — talk to us.
models: local-onlyTeams are adopting AI one tool at a time — nothing shared, nothing private, everything re‑explained. We’re building the missing common harness: one place where a whole team works from the same context, the best models plug in as they arrive, and the whole thing can come home to your own servers.
A pilot on your own files. Your models or ours. Move on‑prem whenever you’re ready.
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