Lead Generation
AI Lead Qualification Agent
Qualifies inbound leads with a short conversation and scores them against your criteria.
Extensions
Lead Scoring Model Add-on
Each lead your product handles gets a score based on what actually closed for you before.
When every lead looks the same in the list, your team calls them in order and the best ones go cold. This add-on trains a scoring model on your own won and lost leads, then scores each new lead your AI product handles. The main reasons appear beside each score, and the model is tested on leads it has never seen.
- Scores new leads on how closely they match past wins
- Shows the main reasons behind every score
- Tested on held-back leads before your team relies on it
$2,500
10–20 working days
What it is
The Lead Scoring Model Add-on adds a scoring model, trained on your own historical outcomes, to an AI product you already run or are buying now. New leads are scored on how closely they resemble the leads you won, so your team calls the likeliest ones first. Each score shows its main reasons.
Why businesses buy it
Sales time is limited, and spending it on the wrong leads is expensive. Generic points-based scoring rarely reflects what actually closes for your business. A model trained on your own history learns which signals matter, so your team calls the likeliest buyers first. The written scope covers checking and preparing your history, training, a held-back test that shows how well it separates wins from losses, and reasons shown with each score. You see the evidence before anyone depends on it.
Problems this solves
Sales staff call leads in arrival order, not by likely value
This add-on scores each new lead on how closely it resembles the leads you have won before. Your team can call the likeliest ones first instead of working down the list.
Generic scoring rules ignore what actually wins deals for you
The model is trained on your own historical outcomes, not a general template. It reflects the patterns in your past wins and losses, using the records you provide.
Reps distrust a score when they cannot see why it was given
Each score shows its main reasons alongside the number. Reps can see what pushed a lead up or down, and the documentation explains how the model was built.
A good fit if
Your product brings in or qualifies leads, and your team still guesses which to call first.
You have a history of leads with known outcomes, won and lost, in your CRM or a spreadsheet.
Your product is a sales, revenue or analytics build, such as an SDR, lead qualification or pipeline product.
Someone on the sales side can explain which fields mattered in past wins.
Not the right fit if
You want a scoring product on its own, scoring accounts and leads on fit and intent. AI Customer Scoring is that standalone build.
You want leads sent to the right rep by territory or capacity. AI Lead Routing Agent routes them.
You want leads qualified through a short conversation. AI Lead Qualification Agent holds that conversation.
You have no history of won and lost leads yet. A model needs past outcomes to learn from, so start by recording them.
How it works
- We gather your history. Past leads with their outcomes, exported from your CRM or a spreadsheet.
- The data is checked. Missing outcomes, duplicates and fields that leak the answer are found and dealt with.
- The model is trained on your historical outcomes, using fields your product will also have for new leads.
- It is tested on leads it has not seen, so you can see how well scores separated wins from losses.
- Scores are added to the product. Each new lead gets a score and the main reasons behind it.
- You review the results with one revision round, then get documentation, a handoff note and a written walkthrough.
What you provide within 72 hours
Your portal lists these the moment you order. Delivery starts the working day after everything is in and your access is checked.
- The order reference of the product that will show scores
- An export of past leads with their outcomes, won or lost
- A note on which fields your team thinks mattered
- CRM access if the export comes from there, shared securely in the way we agree at intake
- One sales lead who reviews the results
The 72-hour rule. Send everything on your checklist, including access, within 72 hours of ordering so your build starts on time.
Timeline
Start
Historical leads and outcomes received
Week 1
Data checked and cleaned
Week 2
Model trained on your outcomes
Week 2
Tested on held-back leads, scores added to the product
Week 2
Your review and one revision round
Go-live
Scores live, model documented
| Option | Delivery time | Price |
|---|---|---|
| Standard | 10–20 working days | $2,500 |
What you receive at handover
- A lead scoring model trained on your historical outcomes, added to your existing product
- Scores with their main reasons shown on each new lead
- Updated documentation describing the data used and how scores are produced
- A test report, a handoff note and a short written walkthrough
Expected benefits and how to measure them
- Helps your team spend more time on leads that look like past wins
- Can make follow-up order more consistent across reps
- Makes it easier to explain why a lead was prioritized
- Win rate by score band
- After a few months, compare how often high-scored and low-scored leads became customers, using your CRM outcomes.
- Time to first contact for top leads
- Compare the gap between lead arrival and first call for high-scored leads before and after launch.
- Overall lead-to-customer rate
- Compare the quarter before launch with the quarter after, using the same lead sources.
Before you start: Export your recent leads with their outcomes and first-contact times. Note your current lead-to-customer rate for the last quarter.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
Training happens during the build, and any compute or AI model usage it needs is billed to your own accounts. Scoring new leads adds little ongoing usage. We charge once for the model and nothing per lead scored.
Ask about running costsCustom requirements
Several models for different products or regions, scoring accounts rather than leads, or regular retraining on a schedule are beyond this add-on. Note them at checkout for a written Change Quote. Larger or bespoke projects go through the Custom AI request form instead.
Request a custom versionKeep it running
The model is covered by the parent product's maintenance, which grows in line with the add-on's price. Retraining with newer history can come out of its monthly change hours where they are enough, or be quoted separately.
Adds $125/mo to maintenance
Covered by the main product's maintenance, which grows by this amount.
How maintenance worksHow this compares
| Do it yourself with a SaaS tool | Hire a custom agency | Upmark Labs | |
|---|---|---|---|
| Price | A scoring feature tied to a CRM tier, billed monthly | An agency quotes data science work | Ours is one fixed price, shown before you buy |
| Basis | Generic rules or points you set by hand | Agreed in the project | Ours is trained on your own won and lost leads |
| Proof | Trust the score | Depends on the brief | Ours is tested on leads the model has not seen |
| Explanation | A number | Depends on the brief | Ours shows the main reasons behind each score |
Questions about this product
How much lead history does the model need?
Enough won and lost leads for a pattern to show, with the outcome recorded for each. We review your export during intake and tell you honestly whether it is enough before training starts. If it is too thin, we say so rather than build a model that cannot be trusted.
How do I know the lead scores are any good?
We hold back some of your past leads and test the model on them after training. The test report shows how well high scores matched leads you actually won, compared with low scores. Your sales lead reviews those results before the scores go live.
Why does a scoring model take more work than a connection add-on?
Because it is trained on your data, not set up from a template. Checking and cleaning your history, training the model and testing it on held-back leads all take real effort. That is why it sits at a higher fixed price than a connection add-on, shown before you pay, with nothing added later for the training itself.
Will the model explain why a lead scored high?
Yes. Each score comes with the main reasons behind it, such as company size, source or the answers the lead gave, so your team can judge whether to trust it. A score with no explanation is easy to ignore, which is why the reasons are included.
Which products can take the lead scoring model?
Sales, revenue and analytics builds: action agents such as the AI SDR Agent or AI Lead Qualification Agent, analytics products, and growth systems. Products that do not handle leads, such as document processing, cannot take it. Knowledge chatbots and voice receptionists are not on the list either, because they do not manage a pipeline of leads with outcomes to learn from.
Does the model update itself as we win more deals?
Not automatically. It is trained once during the build. As your outcomes change, retraining it with newer history can be done under monthly maintenance where the change hours allow, or scheduled retraining can be quoted at checkout. Either way, the test on held-back leads is repeated, so you can compare the new model against the old one before switching.
Call the leads that look like your past wins first.
Add the Lead Scoring Model Add-on and every lead your product handles gets a score trained on your own outcomes, with the reasons shown beside it.
Works well with
Outreach
AI SDR Agent
Researches, writes and sequences personalized first-touch outreach for rep approval.
Lead Generation
AI Sales Agent
Engages, qualifies and nurtures leads through conversation and hands sales-ready deals to reps.