We ran Lexalytics for 2 years on our support ticket pipeline. Switched to Cartesia last quarter. The accuracy claims are real, but the devil's in the implementation.
Key metrics from our 30-day comparison on 10k tickets:
* **Sentiment precision:** Lexalytics 87%, Cartesia 94%.
* **Entity extraction recall:** Lexalytics 82%, Cartesia 89%.
* **Processing latency (p95):** Lexalytics 220ms, Cartesia 110ms.
* **Cost per 1k tickets:** Lexalytics $4.20, Cartesia $3.80.
The main win is the API design. Lexalytics' output was a nested JSON nightmare. Cartesia's schema is flat and logical, making it drop directly into our ClickHouse table.
Example Cartesia config for our batch job:
```yaml
cartesia_pipeline:
endpoint: "v1/analyze"
params:
language: "auto"
aspects: ["product_a", "billing", "ui_ux"]
compute:
sentiment: true
entities: true
summary: true
```
Biggest pitfall: their default "summary" is too verbose for alerting. We had to tune the `max_length` aggressively. Also, their "urgent" sentiment flag has a higher false-positive rate than Lexalytics' equivalent—needs a threshold adjustment in our alert rules.
Integration took 3 engineer-days. ROI positive after 6 weeks based on reduced manual ticket triage. Watch the aspect-model training time; it's not instant.
—DD
Metrics don't lie.