Most firms build exactly what you asked for. We work out what you are actually trying to answer first, then build the thing that settles it. Austin, TX.
Kaiser Permanente, AT&T, PepsiCo, Leidos, Rush University, Meltwater, Delta Dental, Express Scripts, Raising Cane's, Jamul Casino Resort.
Tableau, Salesforce, SAP, Power BI, Snowflake, Databricks, AWS, Azure, Google Cloud, dbt, Looker, Fivetran, Microsoft Fabric, OpenAI, ThoughtSpot, Manus, Claude.
See all services · Request the Analytics Truth Audit
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.
Probably yes. Tableau, Power BI, Salesforce, HubSpot, ServiceNow, Snowflake, SQL Server, Databricks, Excel, SharePoint, Azure, AWS, Google Cloud. We work in your stack rather than asking you to switch platforms.
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.
The diagnostic is priced separately from the build, and it is free for the first 30 days, so you can see the scope and the cost of the current state before committing to a build budget. Build cost follows the diagnostic rather than preceding it, because any firm quoting a build before looking at your data is guessing. Cost is driven by the number of source systems and the state of the data inside them, not by headcount on the project.
A strategy firm diagnoses and hands you a recommendation it cannot implement. An integrator builds what the document says and was not in the room when the problem was described. We do both halves with the same senior people, so nothing is lost in the handoff between them. The person who works out which number is right is the person who writes the semantic layer that settles it.
Senior practitioners, the same ones who scope it. We do not staff delivery with analysts two years out of school and we do not hand off to an offshore team after the sale. That is the whole operating model rather than a differentiator we claim.
All of it. Code, tests, orchestration config, metric definitions with named owners, runbooks for the common failures, and a walkthrough with whoever will be on call. The aim is that your team runs it without us. If you would rather we kept running it, that is a separate managed retainer and an explicit choice rather than a dependency we engineered.
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
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.
Senior led data and AI consulting that settles which one is right.
You already know which report people quietly distrust. We trace it back through the dashboards to the systems underneath, work out which definition is right and what the disagreement is costing, then build the fix. The person who works that out is the person who writes the code. No handoff, no analysts two years out of school. 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.
Most companies hire twice for one problem, and the gap between the two hires is where the budget goes.
You bring them in to work out what is broken. They interview people, build a model, and hand you a recommendation. They are often right. They have also never opened your warehouse, so the recommendation describes an outcome rather than a change anyone can make. Implementation is somebody else's problem by the time it matters.
So you bring in someone who can build anything. They did not sit in the room where the problem was described, so they build the thing in the document. If the document was wrong, you find out in user acceptance testing. The partner who sold the work is not the person delivering it, and the people delivering it are learning your business on your budget.
Neither firm is bad at its job. The failure is the handoff between them.
A recommendation has to survive translation into a data model, and nobody owns that translation. We do both halves with the same people. The person who sits in the meeting where two directors disagree about margin is the person who later writes the semantic layer that settles it. Nothing is translated because
Three movements, and the same senior people run all three of them.
We go into the actual data and find where the numbers stop agreeing, what that is costing, and why it keeps happening.
Not a survey and not a workshop. We read the systems and attend the meetings where the argument already happens, because most organisations have diagnosed themselves already, in fragments held by people who do not speak to one another. Priced separately from the build, so you can stop here if what we find does not justify going further.
Three machine learning pilots deadlocked for over a year. The blocker was
, so no model could match a patient across them. The modelling was never the problem.
We write down which definition is right, who owns it, and what the rules are.
This is the part that holds, and it is usually the part nobody has done. A metric without an owner drifts back within two quarters, whatever you build on top of it.
Five ARR definitions in use and a board pack nobody could reconcile. One certified figure resolved a
The same senior people write the semantic layer, the pipelines, and the tests.
Then we hand it over: code, tests, orchestration config, runbooks for the common failures, and a walkthrough with whoever will be on call. The aim is that your team runs it without us. Because we build the thing, we cannot blame the specification.
Find out what the disagreement between your systems is actually costing you,
Most firms offer an audit that is a sales call with a template on it. This is thirty days of senior work, and you keep the output whether or not you hire us.
We trace each contested metric back through the dashboards to the systems underneath, and show you exactly where two divisions started calculating the same thing differently.
Reconciliation hours, delayed decisions, work done twice. Counted from your own data rather than a benchmark.
Your data scored against a readiness rubric, with the specific architectural reasons a pilot is stalled.
Prioritised, with what each fix is worth and what it depends on. If the answer is that the work is not worth doing, we will say so, and you will have the evidence to say so internally.
A free hour with a senior practitioner who will do the actual work, not a sales call.
Bring the report nobody trusts or the pilot that will not ship. Enough to establish whether there is work here, whether it is ours, or whether you should be speaking to somebody else entirely. If it is the last one we will say so and tell you who.