ProspectingConnector
FullEnrich
Submit a batch of contacts and get back phones and pro or personal emails from a 20+ provider waterfall, from Claude or ChatGPT, through Tulina's MCP server.
Official site: app.fullenrich.com
What it can do
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Enrich a contact by cascading through several data providers until one answers
Cascades through several enrichment providers in one call rather than one at a time — if the first provider in the waterfall doesn't answer, the next one tries, and the caller only sees the final result. The point is raising the hit rate on a batch without hand-rolling a fallback chain.
How do you connect FullEnrich to Claude and ChatGPT?
Through Tulina's own MCP server, not FullEnrich's own hosted one. Submit a batch of up to 100 contacts by name, LinkedIn slug and company, then pull back phones, professional and personal emails, titles and locations once the waterfall finishes, in the same chat as the list you're building or the CRM it feeds, whether that chat is Claude or ChatGPT.
Why through Tulina
The connection was never the hard part.
Most systems now publish their own way for a model to reach them, and that is good: it is what Tulina connects to. The hard part is deciding who may use it, what it is allowed to return, and what the model knows before it asks.
The keys stay in one place
YouTeamOrganisation
Connect FullEnrich once. The credential is encrypted in a vault, never held in clear, and never pasted into a chat or a workflow file. A person uses their own key where they have one, their team's where they do not, the organisation's after that, so nobody is blocked and nobody holds more access than they need. Revoke it once and everything running on it stops.
What leaves is filtered before it leaves
fields droppedmasked or hashedpseudonymised
Roles decide who can reach FullEnrich at all. Beyond that, a policy on the connector itself decides what comes back. The data is minimised at the source rather than after it has already reached a model.
Every call is on the record
whatwhowhen
Runs are logged for the organisation. That is what makes an automated action reviewable after the fact, and it is what a security review asks for first.
The run starts knowing your company
namingkey accountsdecisions
A direct connection gives a model access. It does not tell it how your company names things, which accounts matter, or what was decided last quarter. Tulina carries that across every tool, so the same request against FullEnrich returns something usable instead of something generic.
Processes that run on FullEnrich
Build a target list from a sentence
AI lead generation from one sentence: companies sourced and scored on dated signals, contacts verified, checked against your CRM and staged as a campaign.
6 steps →Follow champions who change jobs
Each week, champions at won accounts are checked for a new employer, the new company is scored on your ICP, and a warm deal opens with a note for the right rep.
7 steps →Outbound from buying signals
Each morning, companies with a new stack or hiring signal are scored on your rubric, checked against the CRM and live sequences, and given one opener per buyer.
7 steps →Outreach based on government data
Every candidate's website is verified before a credit is spent, tiered by signal strength, then written one grounded opener that is checked for repetition.
7 steps →Outreach based on Slack membership
Membership in a Slack community is the qualification, so everyone in it is in scope, resolved by name and corroborated against their own words.
6 steps →Social listening to qualified leads
Every morning, competitor-post engagers and companies reading your site are matched to the CRM, qualified on your ICP, ranked by intent and handed to their rep.
7 steps →
