From notebook to production. From pilot to trusted system.
Analytically Inclined runs AI/ML strategy workshops, operationalizes data science models, and hardens AI workflows with guardrails and observability.
Sound familiar?
Many ideas, zero consensus
You have a dozen AI ideas floating around leadership and no way to know which one to fund first.
“Almost in production”
Your data scientist’s model has been almost in production for months, and nobody can say why.
Works great until it doesn’t
Your AI pilot works great in the demo, until someone asks it something it can’t safely answer.
One low-friction day in. Two ways to get to production.
1
AI/ML Strategy Workshop
1 day, on-site
A single 6-hour, on-site session that turns competing AI ideas into a prioritized, funded roadmap before you commit budget to the wrong one.
2
ML Operations
10 weeks
Your data scientist’s model, out of the notebook and into a real MLOps pipeline; registry, CI/CD, monitoring, retraining.
3
AI Operations
12 weeks
Verified queries, a real semantic layer, guardrails, and observability make the difference between a pilot and a system you’d trust in front of a customer.
Real World Results
Strategy Workshop
ProblemA national extended warranty company had a long list of possible AI/ML use cases and no way to know which were actually worth pursuing.
What workedRan a single strategy day, working through the full list, narrowing it to the two with the most promise, and scoping both so the team understood the cost and effort to get each to production.
ResultBy day’s end, the team knew what data needed to be in their warehouse, what “clean” meant for their use cases, which platform to build on, and enough MLOps fundamentals that everyone understood the process ahead.
ML Operations
ProblemA flavor and fragrance manufacturer had a chemical analysis model that worked in a notebook but had never made it to production.
What workedTook it from notebook to full production deployment on Snowflake, training their chief data scientist on the platform as the project progressed.
ResultModel training time dropped from 48 to 22 minutes with no drop in metrics, and their chief data scientist came out with a real understanding of MLOps on Snowflake.
AI Operations
ProblemAn internal copilot that answered confidently, including when it was wrong.
What workedAdded a verified-query semantic layer, guardrails, and an automate eval pipeline.
ResultSecurity signed off, and the copilot is in front of real users instead of stuck in pilot purgatory.
Not sure which of the three you need?
Start with a Strategy Day. It’s the fastest way to find out.