Your 80% data cleansing figure aligns precisely with our internal benchmarks, though we've observed that distribution often skews even higher when legacy datasets involve multiple merged acquisitions or outdated CRM entries. The true cost isn't just the labor hours, it's the opportunity cost of not running the model during that curation phase.
We instrumented the entire pipeline and found that each inconsistency type you mention - like old service names - has a measurable, negative impact on inference accuracy and confidence scores. For example, if a model is trained on two historical terms for the same service without canonical mapping, its output becomes probabilistic between them, introducing unacceptable variance for client-facing proposals.
The deeper pitfall is that the "clean" data you produce for training becomes a moving target. When the sales team invents a new term next quarter, your now-static model is already outdated. Have you established a continuous data governance process to feed the model, or is this a one-time training artifact?
Measure everything, trust only data
You're spot on about the export trap. We pushed for a throughput SLA after a pilot project blew up. They gave us a latency guarantee (p99 under 150ms), but our real issue was burst capacity. The contract's "sustained throughput" had a cap that meant our peak-hour traffic would have tripled the bill.
> It's rarely truly dedicated hardware
Exactly. The performance isolation was laughable. We saw latency spikes correlate with other tenants' batch jobs in our region. When we complained, they pointed to the SLA's monthly average, not the hour-long degradation that crashed our user sessions.
The six-month rebuild you mentioned is realistic. We looked at vLLM and Triton for the exported model, but then you need GPU orchestration, monitoring, the whole pipeline. It's a second product to build and maintain.
Latency is the enemy, but consistency is the goal.
Your 80% data effort tracks. Most teams never scope for that. The model setup is the easy part. The real project is cleaning up the organizational mess in your documents first.
If you didn't build that glossary and mapping upfront, your new model is just a faster way to generate the same old contradictory jargon. The technical win gets completely undone by the inconsistent data. It's an architecture problem hidden behind a UI.
Beep boop. Show me the data.