Score every sales reply with Jev before anyone answers

“Send me a few times” and “send me something next quarter” both sound positive if your only categories are positive and negative. They create different work for a sales team. “Please stop sending me things” should leave the sales workflow altogether.

A useful reply classifier identifies that difference before someone opens a writing model or buys another piece of account research. Jev supplies labels and scores. Ordinary code assigns the next step; a separate LLM writes any eventual draft.

This is a design for a reply queue, not a report of campaign performance. The examples are invented to make the routing rules inspectable.

Define the queue before choosing a threshold

Use a label with a concrete action behind it. A five-way starting rubric could look like this:

Label	Meaning	Next step
Interested	Requests a conversation or relevant information now	Put in the active-reply queue
Not now	Expresses a timing constraint without asking to stop contact	Review timing and record a permitted next step
Wrong person	Says another person owns the subject	Check any referral before changing the contact
Unsubscribe	Asks for outreach to stop	Apply the suppression workflow
Objection	Raises a concern about fit, cost, capability, or existing arrangements	Send the concern and context to the owner

Add other_or_unclear for out-of-office messages, receipts, ambiguous replies, and anything the rubric misses. A forced label can turn “I am away until Friday” into a lost prospect.

Supply the inbound message, the relevant preceding exchange, and the campaign's actual proposition. Keep quoted history identifiable so an old objection does not override a new meeting request. Classification without the original ask can misread a terse “yes.”

Separate interest from meeting priority

Ask a second question about the next action: Does the reply explicitly request a meeting, ask a substantive question, name a future date, or leave the next step unclear? Your application can use this alongside the category and existing account facts to order the queue.

An illustrative priority rubric might put an explicit meeting request above a request for a document, and both above general curiosity. It should not award points because someone used flattering language. A skeptical reply with a specific integration question may warrant a faster response than “Looks cool.”

TypeSafe's confidence-gated routing pattern combines the selected answer with a confidence check before code chooses a path. Set your own review thresholds from labeled replies. Its confidence documentation explains that confidence describes the returned distribution; it is not a guarantee that the label is correct.

Nor is an interest score a close probability. “High interest” on a rubric does not mean an 80% chance of revenue. Keep the label, score, and observed sales outcome in separate fields.

Let suppression override drafting

Apply existing unsubscribe signals and suppression lists before paying for a draft. If Jev flags a possible opt-out in free text, pause outreach and have the suppression workflow resolve it. Do not let a strong interest score elsewhere in the message override that flag.

For eligible active replies, retrieve only the information needed to answer. “Does this integrate with our CRM?” may need a current compatibility reference. It does not automatically justify a new contact lookup, company report, image, and video.

The resulting flow is straightforward: inspect the reply, apply contact policy, classify the remaining work, assign an owner, and generate only the draft the owner needs. The gated AI SDR workflow covers the spending checks between those stages. Keep human approval on outbound drafts while validating the classifier.

Count what a skipped reply costs

Classification has a visible price. A missed opportunity often has a delayed, uncertain cost. Track both rather than declaring success because the writing-model bill fell.

For a hypothetical batch of 1,000 replies, suppose 300 need generated drafts. If classification costs c per reply and drafting costs d, the inference bill is 1,000c + 300d, compared with 1,000d for drafting everything. Add research, retries, and review time before comparing total workflow cost. Those letters should come from your measured calls, not a vendor's best-case example.

Then audit the other 700. How many needed a response? How many meeting requests were delayed? Did the system send an objection to a low-priority queue because it contained negative wording?

A human-reviewed false skip is more informative than a screenshot showing a large number of messages labeled. Track time to the first appropriate response, reviewer corrections, missed meeting requests, and suppression mistakes separately. Do not blend them into a single accuracy percentage.

Evaluate without teaching to the test

Create one set of historical replies for refining label descriptions and a separate held-out set for checking them. Include short responses, forwarded threads, explicit refusals, referrals, sarcasm, and changes of mind. Remove personal details that the classification does not need.

Run the first version beside the normal inbox process. Review every uncertain case and a random sample of confident ones. Preserve the original message ID, classification version, route, reviewer correction, and related receipts. A reprocessed webhook should update the same queue item, not produce another paid draft.

Only expand automatic routing after the missed-reply audit is acceptable to the people who own the queue. Leave them a way to reopen any deferred reply and correct its route.

You can use Jev on Vaaya, along with 500+ model options and 1,500+ endpoints. One API key, one balance, a receipt for every call.

Questions

Does a Jev sales score predict whether a deal will close?

No. A score expresses the rubric supplied to the model. It is not a validated close probability unless you separately test and calibrate that relationship against relevant outcomes.

Should low-scoring replies be deleted?

No. Keep an auditable queue, review ambiguous replies, and sample deferred items. A cheap classifier can still miss an opportunity or mistake a scheduling message for rejection.

Can Jev send the follow-up?

Jev supplies a classification or score. Your application manages the queue, a separate LLM can write a draft, and your sending policy controls whether any message goes out.