Deep Industry Expertise. Enterprise-Grade Technology.

"Our fraud detection was catching 62% of fraudulent transactions at the point of authorisation. After Sourcemash's ML-based fraud model went live — trained on 36 months of transaction data with 200+ engineered features across merchant category, velocity, device fingerprint, and behavioural patterns — we are catching 91% with a false positive rate that has actually declined. The number of legitimate transactions declined for fraud reasons dropped 44%. Our fraud team can focus on the cases the model flags with genuine uncertainty rather than reviewing thousands of false positives daily."

"We implemented Salesforce Health Cloud and the patient journey automation programme for our 8-location oncology network. The impact on patient experience has been significant — every patient receives a follow-up call from a nurse navigator triggered by their post-consultation digital feedback score, every missed appointment triggers a re-scheduling outreach within 2 hours, and every patient who has not attended a follow-up within the protocol window receives a clinical alert to their treating oncologist. Our patient-reported experience scores have improved by 32 points on the national benchmark. The technology did not replace the human care — it made sure the human care was delivered consistently to every patient."

"We had accepted that 8–12 unplanned equipment outages per year were normal for our chemical processing plant — each one costing ₹25–40 lakh in lost production plus maintenance. Sourcemash's predictive maintenance model, trained on 18 months of vibration, temperature, and pressure sensor data, identified the failure signatures that precede each of our three most common failure modes — typically 10–18 days before the failure. In the first year of operation, we had 3 unplanned outages that the model had correctly flagged but where the maintenance team had deprioritised the recommended intervention due to production pressure. We had zero outages on equipment where the model recommendation was acted on. We have restructured our maintenance scheduling process around the model output."

"We had built our e-commerce site in-house over five years and it had become a liability — page load time above 4 seconds on mobile, a checkout funnel converting at 0.8%, and a search function that returned irrelevant results for 35% of queries. Sourcemash rebuilt the platform on a modern stack with Core Web Vitals as a non-negotiable, implemented AI-powered search using semantic similarity rather than keyword matching, and redesigned the checkout flow from 7 steps to 3. Conversion rate is now 3.1%. Mobile page load is under 2 seconds. Our Google organic traffic has grown 2.3x because the Core Web Vitals improvement recovered positions we had lost to technically superior competitors. The platform rebuild paid for itself within 4 months."

"Our AI dropout early warning system identifies at-risk students 8 weeks before the predicted dropout event — with enough precision that our learner success team can intervene effectively with the 300–400 highest-risk learners rather than spreading attention across 3,000 marginally at-risk ones. Course completion rate went from 18% to 46% in 12 months. The model continues to improve with each semester's data. The ROI was positive within the first semester from retained subscriber revenue alone."

"Our OTT platform had 600,000 subscribers and an 18% 30-day churn rate. Sourcemash rebuilt our recommendation engine with genuine collaborative filtering — the difference in engagement was immediate. Average weekly watch time went from 2 hours to 6.8 hours per subscriber. 30-day churn dropped from 18% to 13.2%. Trial-to-paid conversion improved 38%. The recommendation system is now the single most important technology asset we have. We talk about it in board meetings in the same breath as content investment strategy — because it determines whether the content we commission is actually watched."