The Number That Changes the Calculation
Most AI cost conversations focus on generative output — long completions, summaries, drafts. Classification work gets lumped in and priced the same way, which is why most teams never run it at scale.
Jev (TypeSafe AI) breaks that assumption. It returns structured values — a category, a score, a probability — instead of generated text, at roughly $0.04 per million input tokens with zero charge on output. Lenny Rachitsky ran five real projects in one week to benchmark it: 1,700 pull requests categorized for $0.09, 200,000 signal classifications run for product insight at ChatPRD, a live audience dashboard built from 4,500 YouTube comments. These are not theoretical throughput numbers. They're receipts.
For operators trying to decide where AI actually fits in their workflows, this is the clearest signal in months.
What Jev Is — and Isn't
Jev is not a replacement for generative models. It doesn't write, summarize, or synthesize. It decides. If you need to route a support ticket, score a lead signal, tag a customer behavior, or triage inbound anything, Jev handles that decision step cheaply and deterministically.
The practical integration pattern: use Jev upstream to classify and filter, then pass only what clears your threshold to a generative model for the expensive step. That architecture can cut generative spend by an order of magnitude on pipelines with high-volume inbound.
For non-tech companies specifically, this matters because classification tasks are everywhere — in customer ops, in sales triage, in product feedback loops — and they've historically been either manual or too expensive to automate at volume.
Where to Start This Week
The highest-leverage first move is an audit of your existing manual classification work. Look for any process where a human is reading something and assigning it a label, a score, or a bucket. Support ticket routing, lead scoring logic, NPS comment tagging, sales call outcome categorization — these are all candidates.
Then run the Jev math against your actual volume. If Lenny categorized 1,700 PRs for nine cents, your 5,000 monthly support tickets probably cost less than a dollar to classify. At that price point, the question stops being "can we afford to automate this" and starts being "why are we still doing this manually."
The operational risk is treating Jev as a drop-in replacement for a full LLM pipeline. It isn't. It's a precision tool for the classification step, and it needs to be designed into a workflow, not bolted onto one. Start with one contained process, validate the output quality against your existing human decisions, and expand from there.