Think inside your AI world.
Dovetail through Unl
One striking quote in Dovetail can feel like proof. Whether it's a validated pattern or a single loud voice depends on a bar someone has to set before the quote turns up.
Dovetail through Unl reads project insights and research data against the validation bar you have ratified, so 'is this a validated pain point?' comes back not yet validated with the condition it hasn't met.
What Dovetail holds
Dovetail through Unl can genuinely read:
- Every project in the workspace, and the insights logged against each one.
- A specific insight's full content, in readable markdown, not just its title.
- Data entries: the underlying interview or session records an insight draws on.
- Full-text search across the entire workspace, so a claim can be traced to its source.
What the naked read gives you
Dovetail will surface, accurately, an insight titled 'users abandon the onboarding checklist' with three linked quotes and tags from a research project: real, sourced material, pulled straight from interviews the team actually ran.
The frame judges the data it is given; it does not verify the source’s accuracy.
What changes when Dovetail is read measured
Research ops ratified that a pain point only counts as validated once it's backed by tagged evidence from at least three distinct participants across two or more research projects, because a single project's framing can produce three quotes that all say the same thing for the same local reason.
'Is the onboarding-checklist pain point validated?' returns, measured: not yet validated - the insight has three quotes, but all three come from one project, short of the two-project spread in the ratified rule. Dovetail supplied the insight and data content; your ratified rule supplied the validation bar.
And back again
The next researcher who searches the workspace for onboarding friction finds the same call already attached to the insight, built from the same bar, rather than having to re-count quotes and decide afresh what 'validated' means this time round.
The answer comes back measured against what you already decided, and why.
The lane is live and open to this tool today: one box, paste anything. If it speaks MCP, Unl can reach it. Readings arrive unprompted, the data beside the criterion; Unl is a courier, not a warehouse, and keeps only your keys and the frame.
Questions people ask
Does the validation verdict change once a second project turns up the same finding?
Yes. Unl re-reads Dovetail's projects and insights on each query, so once a matching insight appears in a second research project, the same pain point clears the two-project bar and returns validated.
How do I connect Dovetail to Claude?
Add Dovetail's own MCP server as a source in Unl, either the hosted endpoint with an API token or a local instance if your client only supports STDIO. Either way, it reads whatever your Dovetail account already has access to.
Can Unl create or edit a Dovetail insight through this connection?
No. Unl's access to Dovetail stops at reading: projects, insights and data content, pulled in to test against your ratified validation bar. Nothing gets created, tagged or edited in your workspace as a result.
What this is
Think inside your AI world — you stay in command
Save the thoughts, decisions and targets worth keeping, each with its reasoning, carried into every AI session the moment they matter. A new unit of exchange between you and your AI: the Settled Why with standing that travels. Unprompted.
Your whole AI world. What you decided at the epicentre. It plugs into Claude, Claude Code, ChatGPT and Cursor as an MCP connector — quick to connect, in a couple of steps.
MCP native·Human settled·Model agnostic·Your data
Measured Context
Connect Unl to bring the right information into the moment.
Your sources, read against the criteria you set.
The full product, open. Free at launch.