AI Dealigence resolves a business objective to the organisations, functions, and people who can actually make it happen — with auditable evidence behind every recommendation, not a static database lookup.
Objective resolution infrastructure for AI agents, too. Build with the API.
Real research, real API calls — takes a few minutes
Objective
Objective → organisation map → function → verified person → evidence. Click any stage to jump to it.
> objective: "Find the person who can approve this partnership."
Parsed into what actually needs to be true for this outcome to happen.
I run a buy-side property advisory sourcing off-market deals and capital between GCC investors and the UK. Every deal comes down to one thing: finding the single person inside a bank or fund who actually has the mandate to say yes, right now. Everyone else is noise.
Every tool on the market searches by job title. Type in “Head of Real Estate Finance” and you get several hundred names sorted by seniority, none of them telling you who has the authority and the timing to act. That’s not a database problem. It’s a research problem.
So I built what I actually needed: a system that starts with the objective, not a search box. Give it a goal, and it works out which organisations and functions matter, finds the person who holds real authority, and backs it with checkable evidence, not a match score.
Then it became obvious this wasn’t just my problem. AI agents doing outreach or deal sourcing hit the same wall, stuck searching lead databases by title. They don’t need a bigger spreadsheet. They need to hand over an objective and get back a resolved path: organisation, person, evidence, route.
That’s why AI Dealigence exists as infrastructure, not just a website. Use it directly, or connect your own agent through the API or MCP.
AI Dealigence — deal-grade diligence applied to every objective. Decision intelligence and objective resolution infrastructure, not a directory of names: a working answer to “here’s what I’m trying to achieve, work out who can make it happen.”
— Julian Noble, Founder
Search and lead tools return everyone who could plausibly match. Access Agent starts from your objective and works out who actually matters to it.
LinkedIn or Google search
Finds people who match a title. Doesn't know if they're still the right target for your objective.
Apollo or generic lead databases
Returns volume: hundreds of contacts at a company, ranked by nothing in particular.
CRM search or manual research
Only surfaces what someone already logged. Says nothing about who's actually relevant today.
Built for agents
Everything above is also available as infrastructure: an agent submits an objective, Access Agent runs the same organisation, function, and person resolution, and returns a structured, evidence-backed answer.
curl
curl https://aidealigence.com/api/v1/objectives \
-H "Authorization: Bearer dl_live_xxx" \
-H "Content-Type: application/json" \
-d '{
"objective": "Find the person responsible
for UK fintech partnerships at Gulf banks"
}'Not a black-box score. You see the claim, the source, how current it is, and how strong the evidence is, so you can judge the recommendation yourself before you act on it.
Publicly named as CEO across three independent sources, one first-party.
“...is currently Chief Executive Officer at Meridian Gulf Bank.”
Most objectives return an initial recommendation well within the same session.