Mid-Market SaaS Platform · Technology & SaaS · Seattle, WA · 6 weeks
Mid-Market SaaS Platform AI Enablement case study
A mid-market SaaS company stalled three machine learning projects after 6 to 14 months of development. While the data science team insisted the models were ready, the underlying data infrastructure couldn’t support deployment. We ran an AI readiness assessment, identified specific data layer issues blocking progress, and created targeted 12-week plans to fix them.
Challenge
The company spent $2.4M developing three ML projects: churn prediction, recommendations, and anomaly detection. After six months, none were live. The data science team repeatedly rebuilt the models. The issue wasn’t with the models themselves.
Outcome
We found 14 data layer failures across the three projects. The churn model couldn’t run because customer IDs didn’t match consistently. The recommendation engine lacked complete product interaction data. The anomaly detection system failed due to unstable data pipelines. After fixing these issues, all three systems were live within 12 weeks.
Results
- 3 Blocked ML initiatives diagnosed and unblocked
- 14 Specific data layer failures identified
- 12 weeks Time from remediation start to production for each initiative
- $2.4M Prior ML investment recovered through production deployment
We had already invested $2.4 million into machine learning development without getting any models live. Our data science team thought the models were ready, but Thinklytics found in six weeks that the real problem was with the data layer. Once we fixed that, all three projects were up and running within 12 weeks.