Can AI Understand Fleet Data? Claude Puts Geotab Ace to the Test (MCP Demo)
To showcase the capabilities of Geotab Ace, an AI assistant that translates user questions into fleet analytics, we moved beyond manual validation. We used the MCP to give Claude full autonomy: it designed its own test cases, ran them through Geotab, and verified that Ace delivers the accurate, actionable insights a fleet manager would expect. Read on for the full breakdown, or watch the demo video below.
I’m Felipe Hoffa, Data and AI Advocate at Geotab. Geotab Ace is our conversational agent: Ask questions in plain English and get answers from your fleet data.
In this experiment, Claude ran 18 tests, from simple counts to behavioral pattern analysis. Not just checking if Ace gets the right answer, but whether it understands questions like a human would.
Today’s experiment boils down to one question: Can Claude verify that Ace truly understands natural language within its domain?
Here’s what it discovered.
The Setup
We used an MCP server (Model Context Protocol), a standard for connecting AI systems, to link Claude directly to Ace. Think of MCP as the conversational bridge letting two AIs talk directly. No human in the loop. Claude is running its own mini research project.
1. Basic Comprehension
Claude’s First Question:
“What can you do?”
Ace lists trip details, GPS logs, vehicle performance, driver safety, and diagnostics, plus tips on being specific and including time frames.
Claude’s Assessment: Comprehensive overview, transparency about limitations, well-structured. Now Claude knows what to test.
2. Simple to Complex Logic
Claude then started simple:
“How many vehicles are in the database?”
Ace handled those cleanly. Then it pushed harder, moving from simple retrieval to multi-condition reasoning:
“Show me vehicles that idled for more than 30 minutes AND drove over 200 miles in the last week.”
Combining multiple conditions, Ace confirmed both had to be met before generating the answer. 10 vehicles matched. Claude was impressed.
3. Ambiguity and Human Language
This is where Ace surprised Claude. Fleet managers ask all the time: “Which driver is the safest?”
No metric is specified. No definition of “safe” — just a simple question that could mean a dozen different things. Claude deliberately left it vague to see what Ace could do.
Ace laid out its reasoning, showing how it translated a vast idea into measurable metrics: safety ranking, predicted collisions, and collision probability. It didn’t guess; it explained its interpretation before answering.
Claude noted: This is inference, not just translation. Ace is reasoning about the problem domain.
4. Colloquialisms
Then Claude tried something trickier. A fleet manager might say:
“What’s the deal with vehicle B26? Has it been working hard lately?”
That’s vague. Claude used this to see whether Ace could map casual language to specific metrics.
- Ace translated “What’s the deal” into an information request.
- “Working hard” became distance, engine hours, and trip count.
- “Lately” became 30 days.
Claude highlighted how Ace understood colloquialisms without formal phrasing.
5. Domain Expertise
Now it was time to test whether Ace knows Geotab’s world. When something goes wrong, a fleet manager doesn’t dive into technical jargon. They just ask:
“Show me geofence violations last week.”
Ace mapped “geofence violations” to Geotab’s Zone Exceptions, calculating week boundaries using proper date logic.
Claude called this out: Ace knows Geotab terminology and data structures. It’s not just a generic AI translator.
6. Logic and Negation
Claude then tested negation by asking:
“Which vehicles did not make any trips yesterday?”
Ace explained its two-step logic and found Demo 05 idle.
7. Time and Place
Claude tested whether Ace thinks in places and time, not just raw data.
“Compare average daily mileage between weekdays and weekends for the last month.”
Ace explained it aggregates twice: first daily totals, then comparing weekdays to weekends.
- Result: Weekdays average 6,800 miles; Weekends 5,600.
8. Advanced Causal Reasoning
Picture this: You are reviewing your fleet dashboard and one vehicle’s fuel stats look off. You want to know if it’s truly using more fuel than usual.
“Find vehicles whose fuel consumption today is more than double their normal average.”
Ace explained it is calculating a baseline average but excluding today’s data to establish what’s “normal,” then comparing today against the baseline.
Claude called this immediately: That’s causal reasoning. It recognized that including today’s data would create circular logic.
9. Debugging and Resilience
Then came the fun part. Claude asked:
“Who are the three least experienced drivers who have driven more than 100 miles this week?”
After hitting a processing timeout, Claude didn’t give up. Like a researcher refining a question after a dead end, it tried a new approach:
“Show me the newest drivers who drove over 100 miles this week.”
That one timed out too, and the team is working on it. But Claude pushed further, testing different approaches, mapping the edges, watching one AI debug another in real time. That’s next-level AI collaboration.
10. Patterns and Insights
“Show me vehicles that consistently speed on the same road segment.”
You probably hear this from operations managers. They don’t want random speeding incidents; they want patterns, problem areas, places where drivers habitually push the limits.
Ace rounded the GPS coordinates to about 11 meters of precision, creating location clusters. Demo Vehicle 41 had 17 speeding events at the same spot. Claude found this “elegant geospatial clustering” was a smart, practical solution.
“Which vehicles increased their mileage by more than 50% compared to last month?”
Ace explained that “last month” refers to the previous full calendar month, not a rolling 30 days. Claude noted this shows Ace understands human concepts of time, not just date arithmetic.
“Find drivers who speed only during rush hour but not at other times.”
This is the kind of insight that reveals behavior patterns. Insights fleet managers use to adjust schedules, reduce stress, or correct routing issues. Maybe stress drives risky driving? Maybe it’s schedule pressure? Understanding when drivers speed tells you why they speed.
Ace explained it created two groups: Rush Hour Speeders vs. Other Time Speeders, then found the difference. It defined rush hour as 6:00–9:00 AM and 4:00–7:00 PM, accounting for time zones.
Claude observed that Ace’s time zone awareness was spot on — critical for managing multi-time zone fleets.
The Verdict
After 18 tests, Claude gave a near-perfect score, praising its deep understanding, sharp reasoning, and domain expertise.
Claude’s Summary: Ace doesn’t just translate questions; it reasons about the problem domain.
What stood out?
- Statistical reasoning with circular logic avoidance.
- Geospatial clustering.
- Deep domain expertise in Geotab terminology.
- Set operations from natural language.
- Time zone awareness.
When Claude hit processing limits, it adapted, reformulated queries, and kept testing. That resilience was fascinating to watch.
Why This Matters
You don’t have time to write perfect questions. You need systems that get what you mean even when you’re vague. Better understanding means faster decisions, fewer mistakes, and more confident operations.
Imagine asking, “Which drivers are improving their safety scores?” and getting an instant, accurate answer. Or, “Where are maintenance costs climbing?” or “Which routes are most fuel efficient?” Questions that used to require SQL expertise and analyst time now happen conversationally.
And now, an AI can validate whether another AI actually understands its domain at scale.
What’s Next?
This opens up some interesting questions.
- How will Ace perform when the data isn’t clean or when it’s handling millions of rows at production scale?
- What if Claude isn’t just testing Ace, but using it as a tool to solve bigger problems? Multiple AI queries orchestrated together, building analytical workflows?
Well, MCP makes this possible: AI using another AI as a capability.
If you are as excited as I am about where this is headed, follow along. There’s a lot more to explore. This wasn’t just a test of accuracy; it was a glimpse of AI systems that can reason, adapt, and even debug each other. AIs aren’t just answering questions; they are evaluating each other, and that changes what’s possible.
Check out the full conversation between Ace and Claude. Also follow me on LinkedIn for more AI deep dive topics, join the r/geotab subreddit for more Geotab news, or go ahead and try the MCP server I published on GitHub.
I’m Felipe Hoffa, and I’ll see you on the road.
