We design the routing logic your team uses to find what it already knows: where to look first, when to use AI-assisted search inside existing tools, and when to ask a human owner. This is not an AI copilot deployment or an enterprise search platform.
Common pain points
Engineers ask the same questions in Slack every quarter because they have no idea where the answer lives or whether to trust the docs.
The team turned on AI search inside existing tools, and the answers come back inconsistent or partly wrong.
Nobody knows when to trust an AI search result, when to check the source doc, and when to ask a human — so the team defaults to asking a human every time.
New hires can't tell the difference between current docs and stale ones in search results — both look the same.
Your team has search inside multiple tools (Slack search + Notion search + Google Drive search + AI search features) and no rule for which to use first.
Leadership wants an AI copilot to "fix this", but the underlying knowledge is unstructured and the routing has no rules — so the copilot would inherit the same problem.
An AI-assisted internal search workflow is the routing logic your team uses to find what it already knows. It defines where to look first (docs, search, Slack, a human owner), when to use the AI-assisted search features that already exist in your tools, what AI search should and should not be trusted for, and when to escalate to a human. We design the routing rules with the people who search every day, set up guardrails per tool, and write the escalation path your team can apply that same day. AI-assisted search depends on the maintained knowledge your team has — it does not replace structure or ownership. We do not deploy AI copilots, AI knowledge assistants, or enterprise search platforms.
What we deliver
Area 1
We map the questions your team actually asks (operational, technical, customer, process) and write a one-page routing matrix: which kind of question goes to docs first, which to AI-assisted search, which to Slack, which to a human owner. Output: a matrix your team can pin in the channel and apply that same day.
Area 2
For each search and AI-assisted search feature your team uses (tool search, AI search inside Notion, AI search inside Slack, AI search inside Google Workspace or Confluence), we define what it should be used for, what information it should and should not use, and what its answers should and should not be trusted for. Output: a per-tool guardrails document your team can edit when the AI features change.
Area 3
AI-assisted search returns confident-sounding answers even when the underlying knowledge is incomplete, stale, or contradictory. We write explicit rules for which answers can be trusted as-is, which need a source check, and which always need a human review. Output: a short trust rubric your team applies before acting on an AI answer.
Area 4
When the docs and AI search don't return a confident answer, your team needs a clear path to the right human. We design the escalation rule: which human owner gets the question, how the question is phrased, and whether the answer should become a new doc afterwards. Output: an escalation flow that respects the named owners from your knowledge base design.
Area 5
We write a one-page "how this team searches" guide for new hires — the matrix, the guardrails, the trust rubric, the escalation flow, in plain language. New hires read it on day one and follow the rules from week one. Output: a short guide your team links from onboarding.
Area 6
Once a month, the owner of the search workflow reviews what kinds of questions still get asked in Slack instead of searched, which AI-search answers were wrong or misleading, and which docs are missing. The review feeds back into the routing matrix and into the knowledge base design itself. Output: a 30-minute monthly review agenda and a runbook the owner can run without us.
Your team knows where to look first instead of defaulting to "ask in Slack".
AI-assisted search is used only where it is useful, and avoided where a human owner or source check is safer.
Engineers stop asking the same question every quarter because the matrix tells them where the answer lives.
Nobody acts on a wrong AI answer because the trust rubric tells them when to check the source.
New hires understand how the team searches from day one because the rules are written down once.
Your search behaviour stays useful in a year because the monthly review keeps the routing rules current.
No. AI copilots are assistants that help a person complete a task. This page defines the routing logic that tells the person where to look first, when to use AI-assisted search, and when to ask a human owner. Different decision class. If you genuinely want an AI assistant deployment, that's a different engagement and a different specialist.
No. We don't deploy AI knowledge assistants or enterprise AI search platforms. We define how your team should use the AI-assisted search features that already exist in the tools your team already has, what those features should and should not be trusted for, and when to escalate to a human owner.
No, not alone. AI-assisted search helps when the underlying knowledge is structured, current, and owned. If the knowledge is unstructured, stale, or contradictory, AI search returns confident but inconsistent or wrong answers and the team stops trusting it. The routing rules in this engagement assume your knowledge base has structure and ownership — if it doesn't, the structure work comes first.
Because turning on AI search features doesn't decide where your team should look first, what those features should be trusted for, or when a human owner should be asked. Without the routing rules, AI search becomes one more place to ask — not a workflow. We define the workflow; the AI features are tools inside it.
No. We work with the search and AI-assisted search features your existing tools already have. If your team has genuinely outgrown those features and an enterprise AI search platform is the next step, we'll tell you on the call — but that's a different engagement and a different specialist.
AI copilot deployment, AI knowledge assistant implementation, enterprise AI search platform deployment, vendor implementation, custom search index engineering, software development, cybersecurity of search systems, or vendor selection consulting. We do one thing: practical AI-assisted internal search workflow design for B2B teams of 10–500 people.
We help your B2B team organise the knowledge you already have, make it findable, assign clear owners, and keep it updated — using the tools your team already…
Read moreStructure and ownership, not a platformWe design the structure of your internal knowledge base — what areas exist, what lives where, who owns each area, what gets archived, and how it stays usable…
Read moreBook a call to scope your AI-assisted internal search workflow work. We'll talk through how your team finds things today, where AI search helps, where it gets in the way, and what the routing rules would need to look like. If what you actually need is an AI copilot deployment, an enterprise AI search platform, or a vendor implementation, we'll tell you and point you to the right specialist.
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