[ THE PROBLEM ]

When Clients Start Questioning the Tools They’re Paying For

Traditional BI platforms deliver insights too late - retrospective reports that tell operators what already happened, not what’s about to.

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FIG. 01 — THE 30-MINUTE REALITY CHECK

Across healthcare and enterprise clients, analytics platforms often deliver insights too late — retrospective reports that tell operators what already happened and not much about what to do next. HPT’s clients needed something that surfaced risk proactively, in plain language, without requiring a data analyst as an intermediary. Most people who hear about TextQL assume it converts natural language to SQL. Ana does that, but it also runs the full analytical process.

What Ana is very good at is the analytical process — doing the exploration that’s necessary, figuring out what data it needs to answer questions in the right way, and iterating on that. Being able to join data sets that come from different places is a superpower that Ana just has natively. It doesn’t just query a single table; it reasons across your entire data environment.

As you watch the analysis happen, the mistakes that you might expect probably do get made. But then as you continue watching, you see Ana learn from those mistakes and self-correct. In almost every scenario Brad has come across, Ana has been able to self-correct enough to overcome the obstacles and actually provide the analytics that were needed.

[ THE SOLUTION ]

Generating Direct Client Impact

HPT embedded Ana into client data environments, adding a fast-deploy analytics layer to their existing consulting portfolio.

HPT partnered with TextQL to embed Ana into client data environments — adding a fast-deploy, AI-native analytics layer to their existing consulting portfolio. The partnership expands what HPT can deliver: clients get proactive, conversational analytics on top of the infrastructure HPT already manages, without replacing what’s working.

The real proof came when Brad connected Ana to his own AWS environment and asked it to find opportunities to save money. Within minutes, it surfaced several savings opportunities — and showed the receipts. Within the first 10 minutes of setup, Ana identified thousands of dollars in savings, making it very easy for the platform to justify its own costs.

A Tableau partner’s 30-minute reality check.

Brad showed Ana to a Tableau partner, and within the first 30 minutes of that call, they were asking, “What do we even need Tableau for?” That question keeps coming up. Once someone watches a full AI analyst work through a real problem on their actual data, it tends to follow.

Traditional BI tooling requires teams of analysts to build dashboards, maintain data pipelines, and translate business questions into technical queries. Ana eliminates that entire layer. It explores, iterates, self-corrects, and delivers analytics that would have taken a human analyst days — in minutes.

For HPT’s clients, the difference is practical. A dashboard answers the question it was built for. When the business question changes — and it always does — the dashboard stays where it is. Ana answers whatever question comes up, pulls the data it needs, and updates its answers as conditions change.

Tableau Pulse and AWS QuickSight are still built around the same model: pre-built dashboards, rigid data structures, limited ability to improvise on a question. Ana works differently — it explores, pulls from multiple sources, corrects itself when it gets something wrong, and keeps going until it has an answer.

If someone asks Brad whether this is real, his response is simple: “See for yourself. Connect it to a data set of your own and watch it behave like an analyst behaves — exploring and iterating and understanding what data you’ve got and performing the analytics against that.”

• Instant deployment into client environments

  Ana connects directly to client data environments quickly and easily. No infrastructure overhaul required — it works with what’s already there.

• Cross-platform data joining

  Ana natively joins data sets from different sources — a superpower that lets it reason across an entire data environment, not just a single table or dashboard.

• Self-correcting analytical process

  Ana makes mistakes like any analyst would — but then learns from them and self-corrects. In almost every scenario, it overcomes obstacles to deliver the analytics needed.

• Receipts-first analytics

  Ana doesn’t just give answers — it shows its work. Every insight comes with the supporting data and methodology, so clients can verify and trust the results.

• Self-justifying ROI

  Within the first 10 minutes of setup, Ana identified thousands of dollars in savings opportunities. It’s very easy for the platform to justify its own costs.

The setup is straightforward: HPT points Ana at a client’s existing data environment, and within a week the client has a fully operational AI analytics layer. No data leaves the client’s environment. The existing tools still work. Ana just starts answering questions — and doing it better than the tools that came before.

[ THE RESULTS ]

Deeper client value from existing engagements

Ana — AWS Cost Analysis

AWS Cost Explorer

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FIG. 03 — ANA SHOWS THE RECEIPTS

With Ana deployed into client environments, HPT’s customers get proactive, conversational analytics that translate live data into actionable alerts — giving end users real-time answers without the complexity of traditional BI tooling.

For HPT, the addition is straightforward. TextQL works on top of what is already in place — clients keep the infrastructure they have invested in, and HPT keeps the relationships they have built. What changes is what HPT can offer within those relationships.

BUSINESS QUESTION

Which customers have contract values that don't match billing, and what's the revenue leakage?

Tableau Pulse

Pre-built dashboard required

Single data source only

No cross-system joins

Cannot answer

AWS QuickSight

Rigid data model

Limited NL capabilities

Manual pipeline setup

Cannot answer

Ana

Connects to any data source

Cross-system joins natively

Self-correcting analysis

Shows the receipts

Answer in minutes

FIG. 02 — HEAD-TO-HEAD

The initial deployment focused on AWS cost data. Once that was running, clients started asking about other things — healthcare reporting, financial operations, operational data they had never had good visibility into. Brad did not pitch those use cases. Clients asked for them after seeing what the first one could do.