The problems you already know about
Generic copilots cannot see your company knowledge. The ones that can usually leak it. We build the version that does both safely.
Knowledge is scattered across ten systems
Notion, Google Drive, Slack, Confluence, Jira, Salesforce, Zendesk. Critical knowledge exists, but no one can find it without asking three people. New hires take months to ramp.
Internal RAG over the systems you actually use, indexed with permissions intact. Employees ask in natural language and get answers grounded in real company documents, with citations.
Generic copilots leak data or hallucinate
Off-the-shelf AI either cannot see your private knowledge (so it makes things up) or sees too much (so confidential information shows up in the wrong context). Neither option is acceptable.
Permission-aware retrieval. The AI sees what the user is allowed to see, nothing more. Outputs cite sources so users can verify. Sensitive content classes get extra guardrails.
New hire ramp eats senior time
Onboarding documentation is always a quarter behind reality. New hires interrupt senior staff for context that should be self-serve. Senior staff end up doing onboarding instead of work.
A role-aware copilot that answers onboarding questions from current docs. Engineers, finance, sales, operations: each gets answers scoped to their role and access. Interrupt rate drops; senior staff get their week back.
Teams duplicate work because they cannot find prior work
Someone solved this six months ago. Nobody remembers who, where the doc is, or what they decided. The new project starts from scratch and probably hits the same wall.
Search and synthesis across your knowledge surfaces. The AI surfaces prior decisions, code patterns, and design docs relevant to the current question, with timestamps and authors so users can follow up.
What results look like
These are the improvements our clients typically see within the first 3 months.
How it works
We map your knowledge surfaces and access model
Where does knowledge live, who can see what, what are the highest-value questions teams ask. We design retrieval scoped to your real permission model, not a flattened version of it.
We ship a copilot scoped to one team first
Engineering, sales ops, customer support, finance. Whichever team has the highest pain. We measure usage, accuracy, and trust before expanding to the next team.
You expand to other teams from a working baseline
Each team gets the same retrieval foundation with role-scoped data and prompts tuned for their work. Governance, eval, and monitoring are reused. New rollouts ship in days, not months.
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Common questions
How do you keep confidential data safe?
Permission-aware retrieval is the foundation. The AI only sees documents the requesting user can already see, replicated from your existing access model (Google Workspace, Microsoft Entra, Okta, custom). We do not train on your data. We log every retrieval and response so security teams can audit. Sensitive document classes can get extra controls (mandatory citations, redaction, no-go lists).
Can we use this with our existing tools?
Yes. We build copilots that surface inside Slack, Microsoft Teams, your intranet, or as a standalone web app, depending on where your team works. The retrieval layer connects to Notion, Confluence, Google Drive, SharePoint, GitHub, Jira, Salesforce, and most major business systems via official APIs.
Will it just hallucinate company-specific answers?
Not when built correctly. We use retrieval-grounded generation with strict prompting that requires citations for any factual claim. If the source documents do not contain the answer, the AI says so rather than making one up. We test for hallucination behaviour explicitly with an eval harness against your real document set.
How long does this take to build?
A scoped copilot for one team typically goes from discovery to production in four to six weeks. Discovery (one to two weeks): knowledge mapping, permission model, success metrics. Build (two to three weeks): retrieval, evaluation, integration. Rollout (one week): gradual launch with feedback loop.
What happens when our knowledge changes?
We build incremental indexing into the retrieval pipeline. New and edited documents get re-indexed automatically (typically within minutes to hours, depending on the source system). We monitor for stale answers and surface them for review. The system stays current without manual maintenance.
Ship a permission-aware AI copilot for your team.
Book a free 15-minute call. We will scope which team has the highest-value first use case in your organisation.