Agents shouldn’t guess. Neither should your team.
ax1om started with an access gap: the teams that needed predictive scoring most had no data science function to build it, so prioritization ran on gut feel and unvalidated rules. The agent era has the same gap in a bigger frame. There is no learned-prediction primitive in an agent session - no training, no validation, no memory of what converted - so agents guess, and present the guess with confidence. ax1om exists so your team and your agents run on real models: trained on your own outcomes, validated with the result surfaced before anything acts, explained down to the field.
Four principles, applied consistently.
Transparency
Every prediction ships with SHAP factors and a Feature Stability Score. Whether a rep reads the score in the CRM or an agent calls it over MCP, anyone can answer “where did that number come from?” down to the specific fields that moved it, and know whether that reasoning holds up when the model retrains. No black boxes, no guessing.
Reliability
Validation gates stand between raw data and a callable model: leakage detection and label-maturity checks block the model that would be confidently wrong. Models retrain on schedule, scores write back to the CRM on time, and the platform stays live. The operational boring parts matter more than the demo parts.
Simplicity
This level of capability shouldn't require a data science team or a platform team. Connect your data, run the guided setup, and your first model trains without SQL, notebooks, or a hire. Your agents connect with a config snippet. If setup takes longer than a coffee break, something is broken.
Customer focus
We listen closely to RevOps and GTM teams, and to the operators putting agents to work on revenue. We ship fast and prioritize what helps them convert more of what they already have - and prove every number along the way. The backlog is shaped by real conversations with real operators.
Founder note.
ax1om is built by Luis Esquivel. Before starting ax1om, I spent years watching revenue teams make prioritization calls from spreadsheets, intuition, and scoring rules that nobody had validated against outcomes. Every team I worked with was solving the same problem the same way, and every team was losing deals because of it.
ax1om is the version of predictive scoring I wanted to exist when I was running ops: first-party data only, explainable on every score, writes back to the CRM where reps already work, and priced so a solo operator can try it without a procurement cycle. The same conviction carries into the agent era: the models your team trusts are now models your agents can call. Agents reason. ax1om predicts.
Luis Esquivel
Founder & CEO, ax1om LLC
Put a real model under your next call.
Connect Salesforce or HubSpot, train a model on your own history, and see scores flowing in minutes. Then point your agent at the same models over MCP. Free to start, no credit card required.