An AI intake score should not be a vanity number. It should help your team decide what deserves attention first. That means the score needs to reflect actual buying or fit signals, not just activity. Lawgical uses a likelihood to convert concept across calls and forms. That is directionally right. But for firms implementing a score, the important question is what inputs should drive it. A useful intake score usually blends five factors. Fit: does the matter align with the firm’s practice and acceptance criteria?
Urgency: is the issue active, time sensitive, or emotionally hot right now? Intent: is the prospect actually trying to hire someone, or just casually browsing? Completeness: did the intake surface enough details to move forward confidently? Responsiveness: is the lead engaging quickly with follow up? The score should never replace review. It should improve queue order. It is also worth tying the score back to outcomes.
Which scores actually become consults? Which become signed clients? Which scores are inflated because your questions are wrong? If you cannot answer those questions, the score is probably theater. A good AI intake score helps humans spend time where probability is highest. That is all it needs to do. But that is a big deal.