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Overview The AI never sets a price. Ever. Every lead arrives as a messy human conversation and leaves as a number your team can act on: a quality score, a set of tags, and a price. Here's how LeadLogiX gets there — and why it's built to be trustworthy, not a black box. Find it: Main Menu > Clearing Pool. where you see each lead's quality score and price
Two kinds of thinking, on purpose LeadLogiX deliberately splits the smart-but-fuzzy part from the exact-and-auditable part. AI reads the conversation Judgement The AI turns unstructured talk into tidy, bounded signals — how urgent someone sounds, where they are with financing, what they're actually looking for. It's great at understanding messy human language. Code does the math Exact Every score, probability, and price is then computed by plain, hard-coded logic — not by the AI. Given the same inputs it always returns the same number, and every step is written down.
Why split them? This is the whole point — and your best answer to “can I trust the number?” The AI never decides money Letting a language model directly set a price invites “hallucinated” numbers you can't reproduce or defend. So the AI only describes the lead; auditable code makes every financial and routing decision. Repeatable Same conversation in → same score out. Always. Auditable Every score keeps a record of the inputs, the version, and the rules applied. Predictable More positive signals never lower a score — the math is monotonic by design.
The Lead Quality Index (LQI) A single 0–100 score for how workable a lead is. The LQI is three things multiplied together , each a probability between 0 and 1: Factor The question it answers What moves it Reachable Will at least one recorded contact channel actually reach this person? Verified phone and email. Each distinct validated channel adds to it; a duplicate does not. Genuine Is this a real human prospect rather than a fake or a bot? Fraud and authenticity checks. A serious risk finding can suppress the lead outright, whatever the rest looks like. Intent Do they actually intend to transact? Stated budget, financing signals where the role makes them relevant, how clear the target is, and how complete the profile is. They multiply — a weak one cannot be made up for This is the part worth internalising, because it is the opposite of how a points-based score behaves. A perfectly genuine, highly intentful person nobody can contact scores near zero, and no amount of intent raises it. Fixing the weakest factor is always worth more than improving one that is already strong. Price then follows the LQI on a curve, so a small gain at the top is worth more than the same gain at the bottom. To feel which way each signal pulls, toggle them in the demonstrator on the LeadLogiX Tour , in either live line of business.
Grounded in real outcomes — the Statistical Machine LeadLogiX doesn't score in a vacuum. For each new lead, LeadLogiX quietly assembles a cohort of similar past leads and uses how those actually performed as the baseline. A lead's quality and price are anchored to comparable real history — not a guess — and leads that didn't process cleanly are left out of the baseline so the numbers stay honest. Your outcomes inform you — they cannot move your bill Connect your CRM and LeadLogiX reads your closed-deal history to project what each lead is worth to your business — your close rates, your deal sizes, segment by segment. That projection is kept structurally separate from the score and the price: your own outcome data can never quietly raise what you are charged. The baseline underneath the score moves only as the cohort of comparable real leads grows.
Every number is real — or honestly absent A statistic on a lead is in exactly one of three states. There is no fourth. Computed Real Calculated from real inputs, with the sample size recorded. This is the only state in which a number influences quality or price. Insufficient data Honest Not enough comparable leads to measure against — the lead shows that , along with how many were found and how many were needed. LeadLogiX never substitutes a guess or an industry average. Flagged Recoverable Something failed while computing. The lead is marked for reprocessing rather than shipped with a silent default. Why this matters when you're spending money Most scoring products quietly fall back to a default when data runs out — so a confident-looking number can mean “we had nothing to go on.” LeadLogiX's contract is the opposite: if a number is on the lead, it was computed; if it couldn't be, the lead says so, in the open.
How the baseline is built Each lead is measured against a cohort of comparable recent leads — never against a hunch. Assemble the cohort LeadLogiX gathers similar past leads — comparable intent, market, and situation — then narrows to the ones in the same role : buyers are measured against buyers, sellers against sellers. Role changes what a number means, so mixing them would compare a seller's buying intent against people who were shopping. A minimum cohort size is required before any baseline is computed, and it applies after that narrowing. Below the minimum, the statistic reports “insufficient data” instead of pretending — including when there are plenty of similar leads but not enough in this lead's own role. Weight for likeness and freshness Cohort members don't count equally: closer matches count more, and older leads count exponentially less as they age, so the baseline tracks your market as it is now — not as it was last quarter. Tame the extremes Averages are computed with the statistical extremes capped, not deleted (statisticians call this winsorizing). One freak lead can't drag the baseline, and no data is quietly thrown away either. Place the lead against it The new lead's signals are compared to that baseline as a standard-deviation distance and a percentile — “stronger than N% of comparable leads” — which is what feeds quality and pricing.
What the numbers on a lead mean Plain-language definitions — the full glossary is folded below. These are the statistics LeadLogiX computes — there are no others behind the curtain. Every one is deterministic: the same cohort and the same lead always produce the same value, and each carries the sample size it was built from, so you can always see how much weight a number deserves. Consistency checks A lead's story is tested across channels — does the stated intent match the behavior, the pace, the tone? A large gap between them is flagged against a calibrated threshold, not eyeballed. Cohort profile Every baseline records how many leads were behind it and how fresh they were, so you can judge the evidence, not just the verdict. Spread measures (quartiles, robust ranges) ride along with every baseline so an average can never hide a wild distribution. All of it is computed by the same auditable code path — the AI that reads conversations never touches these numbers . The headline score and its three factors Lead Quality Index (LQI) 0–100 The headline score, worked out as 100 × Reachability × Authenticity × Intent . Because the three multiply rather than add, a weak showing on any one of them holds the whole score down — they cannot compensate for each other. A perfectly reachable, entirely genuine lead with no real intent still scores low, which is the intended behaviour. Reachability (R) Factor The probability that at least one recorded contact channel actually reaches this person. Channels combine as "at least one works" , so each additional distinct channel raises it. The same phone number recorded twice counts once — duplicates are collapsed before the calculation, so padding a record with repeats cannot inflate the score. Authenticity (A) Factor The probability that the lead is a real, non-fabricated person and story. Low values come from internal contradictions, placeholder or test data, and security signals — never from a judgement about the person themselves. Intent (I) Factor How strongly the conversation shows genuine buying or selling intent. Read from what was actually said, then damped by how well the rest of the record backs it up — so a confident claim with nothing supporting it does not carry full weight. The centre — what the cohort typically looks like Weighted mean (the baseline) Centre The cohort average, in which closer matches and more recent leads count for more than distant or older ones. This is the figure a lead is measured against. It is always an average of real recorded values from real leads — never a default, an estimate, or a seeded number. Winsorized mean Centre The same average with the most extreme values pulled in to a percentile cap rather than deleted. If it differs noticeably from the plain mean, a few outliers were dragging the average around. Capping rather than discarding means the sample size never silently shrinks. Median (p50) Centre The middle value — half the cohort sits above it, half below. Compare it with the mean: a wide gap between the two means the distribution is lopsided, and the average on its own would mislead you. Spread and shape — how much the average is worth Standard deviation Spread The typical distance from the average. Small means the cohort broadly agrees, so being above average is meaningful. Large means the cohort is genuinely mixed, and "above average" says less than it appears to. Quartiles (p25 / p75) and IQR Spread The boundaries of the middle half of the cohort, and the width between them. A robust view of spread that a single extreme value cannot distort — which is why it is shown alongside the standard deviation rather than instead of it. MAD (median absolute deviation) Spread The typical distance from the median. Preferred over the standard deviation when the data contains outliers, because one extreme value barely moves it while it can visibly inflate a standard deviation. Outlier density Spread The share of cohort weight sitting beyond the calibrated threshold. A high value means the baseline rests on a lumpy population rather than a consistent one, so comparisons against it deserve more caution. Skewness Shape Whether the cohort leans high or low instead of sitting symmetrically. Near zero is balanced. Positive means a long tail of high values pulling the mean upward; negative means the reverse. Strong skew is your signal to read the median rather than the mean. Excess kurtosis Shape How much of the cohort sits out in the tails versus bunched in the middle. Near zero behaves like a normal bell curve. High positive means fat tails — extreme leads turn up more often than a bell curve predicts, so "unusual" is less unusual than it first looks. Where this lead sits — percentile, deviation, divergence Percentile Position The share of the cohort this lead scores above. "84th percentile" means stronger than 84% of comparable recent leads. The most intuitive single comparison, and the one to quote to someone who does not want the statistics. Deviation (z-score) Position Distance from the cohort average, measured in standard deviations. Around 0 is ordinary; beyond roughly ±2 is genuinely unusual. Unlike a percentile it tells you how far from normal, not just how many leads were beaten. Divergence verdict Position Whether the gap between this lead and its baseline exceeds a calibrated threshold. Within band is ordinary. Divergent flags a metric worth a human look. Not performed means there was no comparable population to judge against — an honest absence, not a quiet pass. How far to trust a baseline Cohort size (usable / matched) Confidence How many comparable leads were found, and how many survived filtering into the baseline. A wide gap between the two means most candidates were unusable — worth investigating rather than trusting. Minimum sample size Confidence The floor below which no baseline is computed at all. Underneath it, every dependent statistic reports insufficient data . This is why a new workspace sees honest blanks instead of confident-looking numbers built on three leads. Effective sample size Confidence The sample size once uneven weighting is taken into account. If a handful of members carry most of the weight, twenty leads can behave statistically like five — and this number says so, where a raw count would quietly overstate your confidence. Freshness decay Confidence How much the baseline is discounted for age, falling off as its members get older. 1.00 means current. Lower values mean the baseline leans on older leads, and so describes the market as it was rather than as it is today. Weighting is deliberate — and it is always reported Cohort members do not count equally: closer matches and fresher leads carry more weight. That makes the baseline more relevant to your lead, but it also means a raw count overstates how much evidence is really behind it. That is precisely why the effective sample size is published next to the plain count rather than instead of it.
How a lead's price is built The four terms behind the amount you are charged — each one visible on the lead. Price is not a judgement call and it is not a rate card. It is four terms multiplied together, all four shown on every lead so you can reproduce the figure yourself: base value × role multiplier × conversion curve = gross price . Country base value Term 1 What one lead is worth in this country before quality is considered. Set per country during calibration, because the same lead is genuinely worth more in some markets than others. Role multiplier Term 2 Adjusts the base value for what the person is actually trying to do — a buyer, seller, renter and landlord are not worth the same. ×1.0 leaves the base value unchanged. Conversion curve Term 3 Turns the 0–100 quality score into a price factor, as (LQI/100) raised to an exponent above 1. Because the exponent is above 1, price falls away faster than quality does — a lead at half the quality is worth well under half as much. That is deliberate: it stops mediocre leads being priced as though they were nearly good. Minimum LQI for pricing Floor The quality floor. A lead scoring below it is not priced or sold at all — it is recorded as below the pricing floor rather than quietly offered cheap. Gross, reserve and net — only one of them is a charge Gross price is the lead's sealed clearing price — the number the four terms above produce, recorded on the lead and never recomputed afterwards. The platform reserve is the platform's share of that gross price, recorded for bookkeeping — it is already inside the gross price, never added on top. Net value is the gross price minus that reserve. Reserve and net are bookkeeping views of the same charge, so adding them to the gross price would count the same money twice. The gross price is not, by itself, what you are billed What reaches the invoice depends on your plan. On Pay-Per-Lead the gross price is billed for each lead you procure, and any contract discount you hold is applied at settlement as its own separate line — the lead's own recorded price is the undiscounted one, so a lead never carries an inflated "before" figure. On an Unlimited plan every lead is still itemized, but at $0.00 and marked as included in the plan, because the subscription fee already paid for it. Either way the lead's gross price is the price of the lead ; the invoice is where it becomes a charge. See Billing & Plans .
Why you can defend these numbers The properties that make the math trustworthy — and hard to game. Reproducible forever Every lead stores which engine version scored it and what the cohort looked like. Any historical number can be recomputed and checked — months later, in a dispute, exactly. No magic constants Every threshold and weight is a named, versioned calibration setting — reviewed and tuned as real outcomes come in. Nothing is hard-coded folklore. No silent fallbacks Zero variance in a cohort? The statistic says “insufficient data” — it does not invent a spread to divide by. Every check that couldn't run is listed on the lead by name. AI describes, code decides AI-extracted signals pass a strict validation gate (ranges, allowed values) before statistics ever see them. A misreading can be caught; it can never place itself directly into a score. The exact recipe stays calibrated, on purpose The mechanics above are public because they're what make the numbers defensible. The specific weights and thresholds are calibrated to live market outcomes and aren't published — that's what keeps scores meaningful and ungameable, the same way a credit bureau publishes its factors but not its coefficients.
From score to price Pricing is an expected-value calculation, not a sticker. Estimate the deal What a transaction at this budget is realistically worth. Expected deal value The firm's net value on a deal like that. Adjust for what the lead is A buyer, seller, renter and landlord are not worth the same to a firm, so each carries its own multiplier. Weight by how likely it closes Multiply by the lead's real close likelihood (this is where the LQI and history come in). Quality doesn't scale in a straight line — a strong lead is worth proportionally more than its score alone suggests, because good leads close at a disproportionately higher rate. Charge only an affordable slice Take the fraction of that expected revenue a firm can rationally spend to acquire a lead, then apply a safety margin. That's the fair price . Why buyers like this Every dollar of a lead's price traces back to expected deal value × probability. It's a defensible number, not a vibe — and the exact rates are calibrated to your market, not published for competitors to copy.
A score per lead — a trajectory per customer The arc is the signal A lead's LQI scores a single conversation. When a returning customer has had several, their Customer Profile shows the score climbing over time — from a first curious visit to a decision-ready return. That trajectory is often a stronger buying signal than any one number (see Customer Profiles ). Scores are per line of business, and never mixed If your workspace handles more than one line of business — say real estate and life insurance — the quality score is tuned separately for each, because what makes a strong property enquiry is not what makes a strong life-insurance application. So a customer profile shows a section per line of business , each with its own best score, role and budget. We never combine them into a single number: a "top score" that silently came from a different line of business would look authoritative and mean nothing. Conversations also carry a small badge naming their line, so you can always see which business a given call was about.