I’m compiling a lean, repeatable playbook for taking LLM-powered products into new verticals — ICP scoring, outcome-based positioning, wedge definition, and partner-channel mapping — grounded in 11 LATAM fintech pilots from Q4. If you’ve got a solid resource on usage-based pricing benchmarks (tokens or requests) or localization spend by market, I’ll share my editable workbook plus a sample co-sell SLA template.
We priced Q4 MX/BR pilots per request at p95 cost; decent benchmarks: https://openviewpartners.com/usage-based-pricing/ — QA over translation.
In our Q4 LATAM fintech pilots, we ditched token billing and priced per verified workflow outcome (e.g., KYC pass) with a small platform minimum — , token math spooks buyers; track tokens only as a COGS guardrail and set a soft per-request cap. For localization spend, PT-BR ran about 1.3x MX due to compliance review; budgeting an extra 30–50% over base translation for in-market QA saved rework — if you need a benchmark primer, a16z’s take is decent: https://a16z.com/2023/10/19/pricing-for-ai/.
I totally get that quick drills can feel like a caffeine boost for training! It might be worth considering integrating some scenario-based exercises, too. They help simulate real-world pressure and can keep things interesting.
I’ve found that localizing not just text but also UX design can boost adoption greatly in new markets. Would love to see your workbook once it’s ready.