Thinklytics

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.

Approach

Over six weeks, we assessed the data infrastructure behind each initiative, focusing on five areas: data completeness, metric consistency, pipeline reliability, feature engineering reproducibility, and inference infrastructure readiness. We pinpointed exact data layer issues blocking progress and created a clear, prioritized plan to fix them.

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.

The problem wasn’t the model itself, it was how we put it to work.

The models actually did their job pretty well. But man, the data setup had serious problems. Customer IDs didn’t line up across systems, key features were frequently missing, and the pipelines kept failing unexpectedly. We rolled up our sleeves, worked through the underlying issues, and surfaced these issues clearly for the first time.

How We Jumped Right In and Solved the Toughest Challenges, Fast

We cooked up 12-week roadmaps for each project. These laid out what data needed tweaking, the task order, who was owning what, and how we’d know when it was all done. The engineering team grabbed these plans and ran with them. We stuck around, ready to jump in whenever they hit a bump.

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.

Chief Technology Officer, Mid-Market SaaS Platform

In the client's terms

The question

Is the data in a state that will survive what we want to build on it?

The situation

Two point four million dollars had gone into three ML initiatives, churn prediction, recommendations and anomaly detection. Six months in, none were live. The data science team kept rebuilding the models.

What we found

Rebuilding the models was the wrong response to every one of the three failures, and it had been the only response tried. The blockers were all underneath the models, in data nobody had been asked to examine.

What it produced

Fourteen specific data layer failures were identified. Churn could not run on inconsistent customer IDs. Recommendations lacked complete product interaction data. Anomaly detection sat on unstable pipelines. All three reached production within 12 weeks of remediation starting, recovering the $2.4M already spent.

The roadmap

Six weeks assessing the data infrastructure behind each initiative across five dimensions: completeness, metric consistency, pipeline reliability, feature engineering reproducibility and inference infrastructure. That produced named failures per initiative and a prioritised remediation plan rather than a general verdict on readiness.

The work behind this result

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Frequently asked questions

Why do multiple ML initiatives stall at the same company?

Because they share a data layer. This mid-market SaaS platform had $2.4M in three initiatives and none live after six months. Fourteen specific data layer failures were behind all three: inconsistent customer IDs, incomplete product interaction data and unstable pipelines.

How long does an AI readiness assessment take?

Six weeks here across three initiatives, scoring data completeness, metric consistency, pipeline reliability, feature engineering reproducibility and inference infrastructure. That produced named failures per initiative rather than a general verdict.

Can stalled ML investment be recovered?

All three initiatives here reached production within 12 weeks of remediation starting, recovering the $2.4M already spent. The client's own engineering team executed the roadmaps.