How to analyze Haltech ECU datalogs using AI (Alternative to manual CSV viewing)

If you have ever spent a track day or a dyno session pulling data off a standalone ECU, you know how painful manual log analysis can be.

Traditional ECU tools like MegaLogViewer require setting up custom scatter plots, math filters, and formulas just to spot something simple like a lean spike or an intermittent ignition misfire across thousands of rows of CSV data.

I recently stumbled upon TuneWorks (https://tuneworks.ai/) and thought it was an interesting case study of vertical AI applied to a niche engineering workflow.

Instead of replacing the tuner, it acts as an AI Race Engineer that parses Haltech CSV files and lets you query the telemetry conversationally.

Here is why this workflow stands out from standard desktop log viewers:

• Conversational troubleshooting: You can ask specific questions about engine vitals, sensor spikes, and fueling behavior instead of manually scrubbing through 40 individual channel traces.
• Context-aware garage tracking: Telemetry alone usually lacks build context. TuneWorks links the datalog to chassis setup notes, suspension alignments, and parts modifications so analysis matches the specific car.
• Log comparisons: You can overlay runs to see directly how ambient temperature changes or boost tweaks impacted air-fuel ratios.
• Usage-based pricing model: Instead of charging a hefty recurring monthly SaaS fee for software used only on race weekends, it runs on pay-as-you-go credits starting at $10.

For indie founders building in vertical SaaS or automotive telemetry, what do you think about using LLMs to simplify dense time-series sensor data?