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Your CRM tells you who is in the pipeline. It doesn't tell you who will convert, how much money is at risk, or what a call today is worth.
Every sales team has an open pipeline. Almost none of them have a conversion probability with a dollar figure. Managers don't act on a 0–100 lead score — they act on a consequence: "$18K at risk if this lead isn't touched this week" is a decision. "Score 72" is not. That gap — between a ranking and a number you can defend — is exactly what a Lead Scorer Agent closes. Not a chatbot that summarizes CRM notes. An agent that scores the lead records you already export into conversion probabilities, pipeline value, and a ranked daily CALL / NURTURE / VERIFY queue. This isn't a nicer pipeline dashboard. It's the difference between guessing who to call on Monday and knowing which open lead is worth an hour today.
Stop thinking of it as a lead-score column. Think of it as a standing analyst that scores the open book from the records you already have.
A Lead Scorer Agent, built the right way, gives you:
Raw lead records in — conversion probabilities out. Each open lead is ranked on expected value and sales priority, not a 0–100 score that means something different in every CRM.
Expected pipeline value, value at risk, value of contact, and pipeline exposure — with a 90% range around exposure when the sample supports it. Uncertain numbers are shown as ranges, not as fake precision.
Recency, frequency, and depth turn activity into labels a manager can use: Hot, Warming, Fresh / Unworked, Cooling, Cold, or Steady — not a black-box engagement score.
The agent fits conversion timing from your closed outcomes, then cross-checks it. When that fit isn't available, it falls back to stage conversion instead of inventing a close rate. Open leads stay in play — they are not treated as losses.
Lead → MQL → SQL → Opportunity → Won/Lost. You see the chance of reaching Won from where a deal sits today. When stage history is missing, it uses empirical stage conversion — it does not invent a funnel you didn't send.
Exact Shapley splits multi-touch credit across channels — not a last-touch guess. You see which channels actually moved the pipeline, not which one happened to be last.
This is what separates pipeline intelligence from a reporting tool: it doesn't just list who is open. It tells you the conversion probability, the money at risk, the value of a contact, and what to do today — before the lead goes cold.
The daily queue is three actions. Use the labels as returned. Manager cards sit next to them in plain language — money, days, and names only.
Rescue stalling deals. These leads are ranked for a same-day call — the ones going cold while they still have value.
Advance active opportunities. These leads are moving — keep them in nurture rather than burning a same-day call.
Manually check low-confidence or thin-data leads. Don't treat a thin sample as a certain close — verify before you act.
Manager and SDR cards stay in business language. No survival-model jargon on the card someone has to act on:
Who to pick up the phone for today — with the money and the days next to the name.
Who is already in motion and needs a push, not a rescue call.
Who still needs to be qualified before you treat the score as ready to run with.
That's the briefing: CALL / NURTURE / VERIFY for the floor, CALL TODAY / PUSH FORWARD / QUALIFY for the manager — not a Cox lecture.
This isn't built for data scientists. It's built for the people who are tired of guessing which open leads to call.
If you're looking for a chatbot to invent a 0–100 lead score, this isn't that, and it won't pretend to be. This is for people who make decisions based on conversion probability, money at risk, and a queue they can defend — not people who want a nicer way to rank a spreadsheet.
| Generic AI Agents / Chatbots | Us | |
|---|---|---|
| Consistency | Different answer each time you ask — probabilistic, hard to trust twice | Deterministic — same input, same output, every time |
| Cost to run | Re-processes your entire dataset through an LLM on every query | Purpose-built models, minimal token usage — cheap enough to run on the open book; pipelines larger than 500 leads can score locally |
| Reliability | A 0–100 score invented in the chat — confidently wrong when the closed sample is thin | Fits conversion from your outcomes, shows a range when it should, and refuses to fit when there isn't enough data — honesty instead of a bluff |
| Data privacy | Your data often leaves your systems, sits with a third party | Full SOC 2 compliance — no data ever leaves your premises |
In short: Other agents give you an answer. This gives you conversion probabilities, money at risk, and a daily queue you can defend, reproduce, and put in front of your security team without a fight.
We didn't build a new lead-score formula and hope your pipeline behaves. Everything underneath is documented statistical science — the same lineage of math used where you cannot afford to invent a conversion probability.
| What It Does | In Use Since |
|---|---|
| Splits multi-touch credit fairly across channels — not last-touch | 1953 |
| Times conversion while treating still-open leads as in play, not as losses | 1958 |
| Shows which factors speed or slow a close | 1972 |
| Puts a 90% range around pipeline exposure instead of fake precision | 1979 |
| Falls back to stage conversion when a survival model cannot be fitted | — |
Why this matters to you: every layer here has been published, cited, and run against real pipelines for years before we ever touched it. We didn't ask an LLM to "guess who will close." We score your lead rows, refuse to invent a figure when the closed sample is too thin, and treat observational uplift as a signal — not proof of causality.
That's what "deterministic" actually means. Not a marketing word. A math word.
ChatGPT and other LLMs are built to talk. Ask them a question, and they generate the most statistically likely-sounding answer based on patterns in text — which is exactly why they're brilliant at drafting a follow-up email and unreliable at telling you which open lead will convert.
Here's the practical difference:
In short: ChatGPT can help you write the follow-up. It can't reliably tell you who converts, what's at risk, or who to CALL today. That's the gap this agent exists to close.
In plain terms — it looks at your open lead records and returns:
That's the whole point of the agent — it turns an open pipeline into a to-do list, with a dollar sign next to every name, so you know exactly where an hour of your time is worth the most.
No — this isn't an engineering project. If your CRM can export a CSV (Salesforce, HubSpot, or any CRM), you already have what the agent needs to start: lead_id and created_date. Stage, status, closed_date, deal_size, and source help. Touches and stage history improve engagement, the funnel, and Shapley attribution — they are optional.
Just follow the step-by-step tutorials we provide, and you're up and running in minutes — not weeks, not a data science team, not an IT ticket. For 500 open leads or fewer, the rows can be scored directly. For larger pipelines, the agent returns a local scoring script so thousands of rows never go through as tokens.
In short: If you can export a spreadsheet, you can have this running today.
Yes. This is built to be fully SOC 2 compliant, and your data never leaves your premises.
There's no black-box vendor holding your pipeline as a training set. For large books, scoring can run locally from the export — the rows don't have to pass through as tokens, which is what you want when outbound posting is blocked. For a business, an open pipeline is sensitive by definition; this was built with that as a requirement from day one.
In short: You get the intelligence, not the exposure.
It's based on statistical models that have been researched, tested, and used in production for decades — not a new algorithm built and hoped to work.
Conversion timing treats still-open leads as in play, not as losses. Funnel outlook follows how deals actually move from Lead to Won. Multi-touch credit is split fairly across channels. Expected pipeline value is time-discounted. Value at risk and value of contact sit on those probabilities. When there aren't enough closed outcomes, the model refuses to fit instead of hallucinating a curve. Observational uplift is a signal, not proof of causality. Low-confidence predictions should not be treated as certain.
In short: It's not guessing based on patterns in text, like a general AI chatbot would. It's calculating, using math that's had decades to prove it works — and guardrails that say so when the sample is too thin.
Non-deterministic means: ask the same question twice, get two different answers. This is how most general AI chatbots work — they generate the most likely-sounding response each time, which can shift even when nothing about your pipeline changed. Fine for writing a birthday message. Risky when the "answer" is how much pipeline is at risk.
Deterministic means: same lead rows in, same scores out — every single time. No extra randomness, no "today the number felt different." If the result changes, it's because the underlying leads, touches, or stage history changed — not because the model rolled different dice.
What we use: deterministic, every time. The numbers this agent gives you come from fitted statistical models, not a language model guessing at a plausible figure. That's what lets you show pipeline exposure to finance or your boss and know it'll hold up if someone re-runs it tomorrow.
In short: We don't give you an answer you have to hope is right. We give you the same, defensible answer, every time you ask — and we refuse to invent a score when the data can't support the fit.
Ready to Stop Guessing Which Open Leads to Call?
You already have the data. Your CRM export has everything this agent needs — lead_id and created_date to start. We'll walk you through exactly how to connect it, step by step.
How to Build an AI Agent →No credit card. No data science team. Just your CSV and a few minutes.
