Data & AI Consulting

Recent insights

• Computer Vision Inspection: Setting the Threshold
• Wrapping Legacy Systems Instead of Replacing
• Internal Knowledge Assistants: What Breaks
• Fine Tuning in 2026: The Economics Changed
• Choosing an AI Model Vendor You Can Leave

Selected client outcomes

• We consolidated 14 regional patient encounter definitions into one standard in 11 weeks, cutting reconciliation labor costs by $2.1 million. , Kaiser Permanente
• We recovered $4.8M a year in misrouted claims by lifting member match accuracy from 75 to 94 of every 100 records, restarting three stalled ML pilots. , Express Scripts
• Migrated 140 Crystal Reports to Power BI in 20 weeks, cutting report time from 4 hours to 8 minutes and saving $1.1M per year in licensing costs. , Ascension Health System
• We gave 340 clinical staff direct access to analytics, cutting ad-hoc report requests from 120 to 14 per week and saving $890K in analyst labor annually. , Community Health Network
• We improved patient data quality from 58 to 91 in 12 weeks, enabling a $3.2M population health management platform to move forward. , St. David's Medical Center

Annual reconciliation labour, fourteen regions on one definition in 11 weeks

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

Member match accuracy per 100 records, unlocking $4.8M annual claims recovery

Workbooks on the server, dashboard load down from 47 to 9 seconds

Annual infrastructure cost, fourteen business units on one mesh

Mostly yes. AI built on top of bad data produces confident wrong answers. The good news is 'clean enough' usually means fixing one or two metric definitions, one or two dashboards, and one or two integrations. It doesn't mean rebuilding the whole stack.

A chatbot answers a question. An agent takes a multi-step task. Read, classify, look up, draft, route, update. It runs with bounded autonomy and a human approves the steps that matter.

Approval workflows, access controls, audit logs, prompt and output monitoring, and clear ownership. We define these before the first agent ships, not after.

Audit: 30 days. BI cleanup: 4 to 6 weeks. AI workflow automation: usually 6 to 12 weeks for a first production workflow. Managed services: monthly retainer.

Yes. Headquartered in Austin, TX, working with mid-market and enterprise clients across the United States.

Thinklytics helps mid-market and enterprise teams fix messy data, modernize Tableau and Power BI, govern analytics, and ship practical AI automation. Austin, TX.

From broken reports to AI-ready data and practical automation. Built for teams that need trusted answers.

Most firms will build exactly what you asked for. We work out what you are actually trying to answer first, then build the thing that settles it. Reporting, data layers, migrations, governance, all of it still on offer.

Every one began as a question somebody could not answer. The figures are the client's own, before and after.

Enough to establish whether there is work here, whether it is ours, or whether you should be speaking to somebody else entirely.