The intelligence layer for the partner channel.

Every enterprise AI project has a shape. Most systems cannot see it.

When you ask a partner directory to match you with a provider, it gives you filters. Industry. Company size. Region. Maybe a vague slider for budget. The assumption is that three or four categorical inputs are enough to describe a project.

They are not. Buyer Intelligence models the project across seven dimensions at once: vertical and sub-vertical, regulatory posture, integration surface, data and tech stack depth, operational scale and peak patterns, geographic footprint, and timeline urgency. We also map your buying committee, so when the brief reaches a partner, it already knows who is going to approve, who is going to block, and who is going to own the rollout.

The output is a profile specific enough that two enterprises in the same industry, at similar revenue, can end up matched with two entirely different partners. Which is what you want.

Most matching engines do their job and disappear. Ours only starts working once the project begins.

A match is only as good as what happens next. Execution Intelligence instruments that "next." From the day the statement of work is signed through go-live, it tracks the signals that actually predict whether a project will land: milestone adherence against the original plan, frequency and root cause of scope changes, how the partner handles escalation, and how the buyer rates the engagement at defined checkpoints.

All of that becomes training data for the next match. Partners who consistently deliver move up in the graph. Partners whose execution patterns start to slip get caught before you do. The effect compounds quietly. Over time, a LookAcross match is not just a match based on who looked right at the start. It is a match based on who has actually shipped enterprise AI projects like yours, recently, and landed them.