AI lead qualification

AI evaluates and tags inquiries by potential before anyone starts calling back. The team starts with the best inquiries, not the newest.

• individuallyScope
• AI and automationsArea
• 6 hours – 2 daysLaunch

Who this is for

Companies with many enquiries and limited sales time.

Problem

Team wastes time on contacts without potential. This applies to companies with high inquiry volume and limited sales time: mailbox is full, they call back the newest submission, and the most valuable one sits a few positions lower waiting until tomorrow. Nobody does it wrong — there's just no way to tell which is which.

What we do

We set up automatic lead scoring and tagging by potential. This isn't a conversation with the client: the client sees nothing here, only the order in which your team takes submissions from the list changes.

• We set evaluation criteria with people who actually call back
• Each submission gets scoring and tags in CRM along with the reason for such rating
• We calibrate the model on sample leads before it starts ordering the queue

What's included

Three elements; the last one decides if the rating is yours or generic:

• Lead quality criteria agreed with sales
• Scoring, tagging and prioritization model
• CRM integration and calibration on sample leads

Timeline

Implementation takes 6 hours – 2 days. The stages are fixed: criteria, evaluation model, integration, calibration. The evaluation works from day one, but at the start it's worth comparing it with what sales says — calibration is there so the model counts by your definition of a good lead, not the general one.

Cost

The cost comes out of three things: how many criteria are really needed for the scoring to be accurate, how many sources the data about an enquiry has to be gathered from, and how deeply the result has to be wired into your CRM. This is not a one-off job — the scoring runs on every new enquiry and learns from the ones that ended in an order, so it lives for as long as AURA runs this area for you.

What you get

Priorities: the best leads first. The same team and same number of calls start hitting the inquiries that matter most to you. Meanwhile, the database stops being just a list of names: since each inquiry has a score and tag, you can finally see which sources bring contacts worth your time, and which just bring traffic.

How to calculate what contact order alone is worth

Scoring doesn't talk to the customer — it only changes the order in which the team picks up inquiries. The value of that change can be calculated from your own CRM:

• Value of a week of better ordering = number of inquiries per week × difference in the closed-deal share between inquiries handled within the first hour and those handled the next day (calculated on your own historical data) × average value of one deal

You take the difference in the middle of the formula from your own CRM, comparing inquiries handled earlier and later — it's the only number that can't be assumed up front, because it depends on how fast competitors respond in your industry.

How AI scoring differs from a FIFO queue, manual review and a simple conditional rule

Four ways of ordering inquiries give very different results, even though from the outside they all look like 'a lead list':

• FIFO queue — whoever inquired first gets called first, regardless of how much they're worth
• Manual review by a salesperson — works while there are few inquiries; with more volume, someone still picks by feel
• A simple conditional rule — priority based on one field, such as budget, but ignoring the rest of the inquiry's content
• AI scoring — reads the entire inquiry, combines several signals at once, and assigns points against criteria agreed with you

The difference grows with the number of inquiries: with a few per day, every method gives a similar effect; with dozens, only the last one keeps working as precisely as on day one.

Where lead scoring uses a language model, and where it's plain arithmetic

This distinction affects how much of this service is actually artificial intelligence, and how much is arithmetic.

• The language model reads the inquiry's free-text content and extracts signals that aren't visible in a single form field — the only part that can honestly be called artificial intelligence
• The weight of each signal and the threshold above which a lead is 'hot' are numbers agreed with your sales team — plain arithmetic, not an AI decision
• Writing the score and tag into the CRM is automation, not reasoning

It's exactly the weights and thresholds from the second point that decide whether the score matches your definition of a good lead, or some generic one.

When AI scoring doesn't pay off

There are situations where scoring won't change anything, and it's better to say so directly.

• There are only a handful of inquiries a day — the team will review each one anyway before scoring has a chance to reorder anything
• Nobody in the company can clearly describe what separates a good lead from a weak one — without that, scoring criteria become guesswork
• Data in the CRM is incomplete or outdated — the model scores whatever it has, so the score will only be as good as the input data
• Sales calls everyone in the order inquiries arrived regardless of score anyway — without changing how the team works, scoring stays just an extra column

What you need to prepare before the start

The model is calibrated on your own data, not on general assumptions:

• Access to the CRM with inquiry history, including already closed ones — won and lost
• Sample leads from both groups to check whether the score matches what sales already knows
• Someone from sales who actually formulates the criteria for a good lead, rather than rubber-stamping them
• The team's agreement that the work order changes by score, not by inquiry date

How to check after a month whether scoring paid off

You calculate the metrics from your own CRM, without our involvement:

• Share of closed deals among high-scored leads compared with low-scored ones
• Time from inquiry to first contact for the highest-scored leads
• Number of cases sales flagged as a wrong score — that's a signal to recalibrate, not a reason to give up
• Score distribution across all inquiries — if almost everyone gets the same score, the model isn't differentiating anything

What AI scoring doesn't do

It assigns scores and tags, but doesn't talk to the customer and doesn't decide on its own who gets a special offer or a discount. It doesn't replace a sales conversation or the judgment a human should apply to an unusual inquiry. A low score doesn't remove a lead from the list — it only changes its place in the queue, and any result can be checked and overridden by someone on the team before anyone calls the lead, or decides not to.

The most common mistake when implementing AI scoring, and whose data stays with you

The most expensive mistake isn't a bad threshold at the start, it's the lack of recalibration when the offer or target audience changes — the model keeps scoring against the old definition of a good lead, and nobody notices because the numbers still look familiar. The second mistake is ignoring low-scored inquiries entirely — it's sometimes exactly among them that an unusual but valuable case shows up, one no rule anticipated.

The whole scoring, the tags and the enquiry history are saved in your CRM, not in a separate system — that is your data, available at any time, whether or not the cooperation with us continues.

What you get

• Lead-quality criteria agreed with the sales team
• Scoring, tagging and enquiry-prioritisation model
• CRM integration and calibration on sample leads

When the problem lies elsewhere

If your problem sounds different, neighbouring areas of the same system stand right next to it — AURA connects them to each other rather than selling them separately:

• AI follow-up — Message sequences for those who didn't respond. Second and third reminder go out on their own, at set time and with stop condition.
• CRM and automations — Requests, reminders and client data work on their own, without re-entry from paper to calendar.
• AI Reports — Weekly summary of leads, campaigns and sales, written in simple language and sent on its own. Instead of a dashboard that nobody checks.
• Integrations — We connect what you already use: POS, calendar, spreadsheet, warehouse, booking system. Instead of replacing with new.