Machine learning pointed at revenue
ML fails in business when it starts from the algorithm instead of the decision. Nexargate scopes machine learning around revenue decisions — which lead to call next, which customer is about to churn, which account will expand — proves value small, and scales only what works.
Why business ML projects die
Gartner-famous failure rates come from the same three mistakes. All are scoping problems, not technology problems.
- closeStarting with 'we should use ML' instead of a decision that needs better prediction.
- closeSix-month model builds on data that a two-week pipeline cleanup would have made unnecessary.
- closeModels that ship but never reach the CRM screen where the decision actually happens.
What you get
Use-Case Discovery
Structured audit of your funnel and data to find decisions where prediction pays — ranked by revenue impact and data readiness.
Predictive Models
Lead scoring, churn risk, expansion propensity — built on your history, validated against holdouts, explained in operator language.
Data Pipelines
The unglamorous 80%: clean, deduplicated, continuously-fed data from CRM and product into the model and back out to the tools your team uses.
Evaluation & Monitoring
Accuracy tracked in production, drift alerts, and quarterly retraining criteria — so the model stays honest after launch.
How it works
- 1
Frame (Weeks 1–2)
Pick the decision, define the prediction target, audit data sufficiency. If ML isn't warranted, you hear it now — free.
- 2
Prototype (Weeks 2–5)
Baseline heuristic first, then the model. It ships only if it beats the simple rule by a margin worth its upkeep.
- 3
Integrate (Weeks 5–8)
Scores surfaced where decisions happen: CRM fields, routing rules, alerts. A model nobody sees is a model nobody uses.
- 4
Operate (Weeks 8+)
Monitoring, drift detection, retraining cadence, and documentation. Optionally handed to your team with training.
Frequently asked questions
Do we have enough data for ML?add
Often yes for scoring use-cases: a few thousand historical leads with outcomes usually suffices. The week-one audit answers this definitively before you spend anything on modeling.
Won't our CRM's built-in AI scoring do this?add
Sometimes — and when it will, we say so and configure it instead. Custom models earn their keep when your funnel, segments, or data live outside one tool's assumptions.
What stack do you build on?add
Python-standard tooling, deployed light: scheduled jobs or serverless inference wired to your CRM via API. No data-platform rebuild required to start.
How is this different from the AI consulting service?add
AI consulting automates workflows with LLMs and agents. Machine learning builds predictive models from your historical data. They compound — scored leads feeding automated outreach — but they're distinct disciplines.
Related services
Start with the decision, not the model
Free scoping call: bring the decision you wish you could predict, and we'll tell you if your data can support it.