SalesCue β€” objection

ObjectionPreClassifier module from the SalesCue sales intelligence library.

Status: untrained β€” architecture only, random initialization. Use as a starting point for fine-tuning.

Research Contribution

3-Way Pre-classifier with Coaching Cards

Most objection handling treats all objections the same. ObjectionPreClassifier first classifies into genuine_objection / stall / misunderstanding using a 3-way pre-classifier, then routes each to specialized handling. Each of the 12 objection types gets a coaching card with a recommended response framework.

Usage

from salescue import SalesCueModel

model = SalesCueModel.from_pretrained("v9ai/salescue-objection-v1")
result = model.predict("your sales text here")
print(result)

Labels

  • price_too_high
  • no_budget
  • not_the_right_time
  • need_to_think
  • happy_with_current
  • no_authority
  • too_complex
  • dont_see_value
  • bad_experience
  • need_more_info
  • feature_missing
  • contract_locked

Architecture

  • Backbone: microsoft/deberta-v3-base (shared encoder, 768-dim)
  • Head: ObjectionPreClassifier
  • Parameters: head only (backbone loaded separately)

Intended Use

  • Primary: B2B sales intelligence β€” lead scoring, email analysis, conversation insights
  • Users: Sales teams, RevOps, GTM engineers building sales automation
  • Input: English sales text (emails, call transcripts, prospect communications)

Limitations

  • Untrained weights: This release contains the architecture only. Weights are randomly initialized and must be fine-tuned on domain-specific data before production use.
  • English only: Designed for English sales text. Performance on other languages is untested.
  • Domain-specific: Optimized for B2B sales communications. May not generalize to other text domains.
  • Shared backbone: Requires microsoft/deberta-v3-base loaded via the SalesCue library.

About SalesCue

SalesCue is a sales intelligence library with 12 ML modules sharing a single DeBERTa-v3-base encoder backbone. Modules can be composed via Unix-style piping:

from salescue import Document
result = Document("interested in pricing") | ai.score | ai.intent | ai.sentiment

All modules: score intent reply triggers icp objection sentiment spam entities call subject emailgen

See the SalesCue documentation for details.

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