Lead the pipeline. We'll handle the scoring.
ax1om trains on your own conversion history and scores every lead, account, and opportunity, with the reason behind each one. Your reps work what converts, and it stays current as your pipeline moves. You keep the team sharp; we keep the model current.
Right company · Right person · Right time, kept separable in one explainable score
A score your reps can act on, not a number they ignore.
Every score writes back to the record with the exact CRM fields that drove it, including the reasons it is not higher. A rep sees why an account ranks where it does, so the score gets worked instead of ignored.
Illustrative. Each score ships with SHAP attributions like these. A weight of 1.9x means that signal makes conversion 1.9x more likely than your baseline; below 1.0x it makes conversion less likely.
A real model. A public dataset. Honest numbers.
We cannot show your pipeline yet, so here is an honest run on a public benchmark. The UCI Bank Marketing dataset is a standard public ML set: 36,168 records, 11.7% baseline conversion rate. ax1om trained on it in 88 seconds and hit 0.80 AUC with 3.14x lift on the top 20%. In plain terms, the top 20% of scored records holds about 63% of the conversions, so a rep working that slice reaches most of the winnable deals first. Every field also gets a Feature Stability Score so you know which signals hold up when the model retrains. Your own numbers replace these in about ten minutes.
Every score comes with a reason.
SHAP feature importances on every field, Feature Stability Scores that tell you which signals survive retraining, and natural language insights that translate model output into operator decisions.
Most scoring tools collapse three different questions into one opaque number.
ax1om keeps them separable. You still get one score per record, but SHAP breaks it back down, so you can see how company fit, person fit, and timing each moved it.
Right company
Firmographic and behavioral fit measured against the accounts you actually close, not a generic ICP template.
Right person
Persona and role fit, so a rep can tell whether they are looking at a likely champion or a dead end.
Right time
Engagement momentum from your own CRM activity, meetings, campaign responses and cases, weighted so recent behavior counts for more.
Four steps to scores your reps actually trust.
Connect your CRM
Authorize Salesforce or HubSpot in one OAuth click. Read-only by default, OAuth tokens encrypted at rest. ax1om reads your schema and record counts automatically.
Define what you predict
Pick the object and what counts as a conversion. ax1om auto-classifies which fields are predictive by population and stability, so you rarely have to touch it.
Train on your pipeline
ax1om trains a dedicated LightGBM model on your closed-won and lost history. No cross-customer data, no shared models. Usually under 10 minutes.
Deploy where reps work
Send scores out by CSV, CRM writeback, live API, or scheduled weekly rescoring. SHAP shows exactly why each record ranked where it did.
Backed by enterprise-grade infrastructure
ax1om runs on providers that hold independent SOC 2 Type II, ISO 27001, ISO 27017, ISO 27018, and PCI DSS Level 1 certifications. Our own SOC 2 Type II audit begins Q4 2026.
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Google CloudSOC 2 Type II · ISO 27001 · ISO 27017 · ISO 27018 -
SupabaseSOC 2 Type II -
VercelSOC 2 Type II · ISO 27001 -
CloudflareSOC 2 Type II · ISO 27001 -
StripeSOC 2 Type II · PCI DSS L1 -
SentrySOC 2 Type II
- SOC 2 Type II Audit Q4 2026
- GDPR DPA with SCCs available
- CCPA Compliant as service provider
- US data residency GCP us-central1 only
What teams ask before they start.
We already have Einstein / HubSpot scoring.
Those tools are trained on general patterns across their entire customer base, not on your closed-won outcomes. ax1om trains on your history, so the score reflects your conversion patterns, not an industry average.
We're not big enough for this.
ax1om is specifically built for Series A-C companies. The guided setup adapts to your data volume and trains a working model on as few as 50 closed-won opportunities. You don't need years of CRM history, and the model improves as your data grows.
Our RevOps team doesn't have data science resources.
That's the point. ax1om handles the entire ML pipeline: feature engineering, model training, scoring, CRM writeback, and weekly retraining. No code, no notebooks, no data science expertise required.
How is this different from a spreadsheet scoring model?
A spreadsheet model uses weights someone guessed. ax1om uses weights the data determined, specifically your closed-won outcomes. The difference shows up in the accounts that surprise you: the ones that score high because they match a pattern your team never articulated.
What lift should we expect on our own pipeline?
It depends on your data, so we will not promise a number. What we can say: the model learns the patterns in your closed-won history, and the Feature Stability Score tells you how much to trust the explanation before you route pipeline on it. On the public UCI benchmark, the top 20% of scored records held about 63% of conversions. Your result depends on how much signal your CRM actually carries, which the free tier shows you in about ten minutes.
What happens when a score is wrong?
Scores are advisory, never automated actions, so a wrong score costs you a judgment call, not a lost deal. Every score ships with the SHAP fields that drove it, so a rep can see the reasoning and override it. ax1om also down-weights sparse and noisy fields automatically and flags likely target leakage before training, which catches the most common causes of confidently-wrong scores up front.
Will our reps actually use the scores?
That is what the explanation is for. A black-box 91 gets ignored; a score that reads "↑ VP-level title, ↑ 3 meetings last 14d, ↓ free email domain" gets acted on. Scores write back into the Lead and Account records your reps already work, so there is no new tool for them to adopt.
Ready to see your own pipeline, not someone else's benchmark?
Give your team a prioritized list they'll work, and keep it sharp every week.
Real-data testers join the founding cohort · founding-member rate locked for life.