What I deliver

Problem scoping — determine whether ML is the right approach and define success criteria
Model development — build, train, and validate custom models suited to your data and use case
Pipeline deployment — production-ready ML pipelines with monitoring and retraining workflows
Integration — connect model outputs to your existing tools and workflows
Performance tracking — dashboards and alerts to monitor model accuracy over time

Project examples

B2B SaaS — churn prediction

A recurring challenge for subscription businesses: by the time a customer cancels, it’s too late to intervene. The customer success team is reactive, working from anecdotal signals rather than data — and there’s no systematic way to prioritize outreach across hundreds of accounts.

This type of project involves building a churn prediction model trained on behavioral, billing, and engagement data. The model flags at-risk accounts weeks in advance, with outputs surfaced directly in the CRM so the CS team can act without touching the underlying model. The result is a shift from reactive firefighting to proactive retention.

B2B SaaS — lead scoring and conversion prediction

A common growth-stage problem: the sales team has more inbound leads than capacity to work them, but no reliable way to prioritize. Reps default to recency or company size, which misses high-intent prospects and wastes time on poor fits.

This type of project involves building a lead scoring model that combines product usage signals, firmographic data, and historical conversion patterns. Scores are synced back to the CRM automatically, giving the sales team a ranked queue that reflects actual conversion likelihood rather than gut feel.

Who this is for

B2B SaaS companies with enough customer data to train a model and a clear retention or growth problem to solve
Product teams looking to add intelligent features — recommendations, scoring, classification
Sales and customer success teams that need data-driven prioritization across large account lists
Founders exploring AI/ML who want an honest assessment of what’s realistic for their data and stage

Why Selvi Data

Working with a solo practitioner means no account managers, no handoffs between teams, and no overhead baked into the price. You work directly with the person doing the work, which keeps costs lower and timelines shorter.

I’m not aligned with any particular platform or vendor, so recommendations are based on what’s right for your situation, not what generates the largest implementation. And as an independent, I have no incentive to scope a project larger than it needs to be.

Machine Learning