Am I the only one who thinks the keyword detection is too generic?

Having extensively evaluated the Read AI API for an event-driven middleware layer that orchestrates data between our CRM and ERP systems, I've encountered a significant and persistent challenge with its keyword detection feature. The abstraction it provides, while seemingly convenient for simple use cases, fails to deliver the necessary precision for reliable business process automation. My core thesis is that the keyword categorization operates at too high a level of generality, leading to ambiguous triggers that compromise data consistency—the cardinal sin in integration architecture.

In our implementation, we configured webhooks to listen for specific product-related discussion markers to automatically generate support tickets. The keyword "issue," for example, is classified under a broad umbrella like "Problem." This classification lacks the contextual granularity required for operational routing. Consider the following payload snippet from a meeting analysis:

```json
{
"keywords": [
{
"text": "issue",
"category": "Problem",
"timestamp": "00:12:45"
},
{
"text": "launch",
"category": "Project",
"timestamp": "00:15:20"
}
]
}
```

The term "issue" could semantically refer to:
* A software bug (route to engineering)
* A shipping delay (route to logistics)
* A billing discrepancy (route to finance)
* A philosophical topic (should not be routed at all)

Without sub-categorization, sentiment context, or domain-specific tuning, this generic flag forces us to implement additional, complex parsing logic downstream. This negates the efficiency gains promised by the AI layer and introduces points of failure.

The ramifications for a data sync pipeline are non-trivial:
* **False Positives:** Creating records and triggering workflows for irrelevant mentions, polluting systems with noise.
* **Context Loss:** Inability to distinguish between a discussed historical problem and an active, escalating one requiring intervention.
* **Integration Debt:** We are forced to maintain a supplementary natural language processing microservice just to re-parse the transcripts for the nuance the initial AI service should have provided.

I am interested to know if other members architecting similar integrations have faced this. Have you developed robust workarounds, or have you found configuration nuances that improve specificity? My current approach involves using the keyword detection only as a first-pass filter, followed by a custom entity extraction model, but this seems an inefficient duplication of effort.

-- Ivan