Compiling the production AI ops questions we get asked most often from prospects and customers. Sharing the honest answers.

Q: How do you handle a model deprecation by the LLM vendor?

A: Multi-LLM routing means no single deprecation breaks our platform. Customers running BYOM are responsible for their own model lifecycle.

For founders looking at the enterprise AI market, sharing what I've seen from the platform side.\

Topics I can speak to:

Opening this up for the community. I work on enterprise AI agent deployments, mostly mid-market through enterprise BFSI, healthcare, retail, and logistics.

Topics I'm happy to go deep on:

- Pilot-to-production gap and how to close it

Stepping back to look at the quarter.

Aggregated learnings across 40+ deployments this quarter:

Marketplace milestone update for anyone tracking.

Top 5 templates by deployment volume in the past quarter:

Documenting an actual rollout to demystify enterprise AI timelines.

The deployment:

- Customer: mid-market healthcare network, 120 clinics

The 80% failure rate Gartner cites isn't because AI is bad. It's because production is hard in ways pilots don't reveal.

The real reasons I've seen projects die:

Audit trail is one of those features that sounds binary but isn't.

Levels I've seen requested:

- Basic: timestamp + user + action

Quick update on the BFSI vertical because the numbers came in better than expected.

Deployed across 7 regional banks and 3 insurance companies in the past quarter. Common pattern: KYC automation, AML monitoring, customer support tier-1.

Talking to enough mid-market buyers to spot patterns. What kills vendor selection early:

- Pricing not on the website

- No on-prem option mentioned