Sales Scoring without a black box.
Objective. Evidenced. Auditable.
Other tools let a language model guess a score. sip.coach evaluates every conversation across 5 dimensions with self-consistency (3 scoring runs, median), aggregates the overall score server-side deterministically, and backs every sub-score with evidence and line references. You choose the scoring framework: needs-question method, qualification grid, budget-&-decision check, pain-first method, teach & lead, or closing mindset.
Reproduciblealigned with the EU AI Actworks-council-friendly
Three runs. One rule. No randomness.
The path from recording to a reliable score: server-side, not inside the language model.
Scored three times, not once
Instead of a single assessment, the model evaluates the same conversation three times independently. Outliers surface, stability becomes visible. The server computes the median and reports the scoring spread. This reduces run-to-run variance and makes it transparent.
Aggregate server-side
The three runs are merged deterministically on the server, not in the client, not by the language model. From the 5 dimensions a weighted overall score of 0 to 100 is produced. The aggregation follows a fixed procedure: transparent and auditable.
Back every score with evidence
Every sub-score is backed by a quote from the transcript, including the line reference. You see not just that something was missing, but exactly where. Instead of gut feeling, a traceable, auditable reason.
Prospect: "Honestly, that's too expensive for us." Rep: "I understand. Let me show you the next feature …"
The price objection was not addressed: it was sidestepped. Recommendation: first isolate the objection ("What exactly feels too expensive, the price or the value ratio?"), then reframe the value. This keeps the score anchored to a specific moment, no blanket criticism.
5 dimensions that actually carry a conversation.
No single catch-all score. Each dimension is evaluated individually and calibrated to the chosen methodology.
Needs Analysis
Was the real need uncovered? Open questions, implications, depth, not a pitch monologue.
Needs-question · Qualification · Budget-checkObjection Handling
Was the objection recognized, isolated, and resolved, or ignored and bypassed?
Pain-first · Closing mindsetValue Argumentation
Feature or value? Scores whether the benefit was aligned to the customer's actual need.
Teach & Lead · ValueClosing Strength
Commitment over stalling: clear next steps, closing questions, concrete agreements.
Qualification grid · Closing mindsetConversation Management
Talk ratio, active listening, pace, structure. Who drives the conversation: rep or prospect?
all frameworksObjective Metrics
Complementing the AI score, we measure hard data from the transcript: free of any interpretation.
You choose the methodology. The scoring follows it.
6 established sales frameworks, switchable per session. The scoring aligns its expectations exactly to the chosen framework instead of treating every conversation the same way.
Needs-Question Method
needs-questionStructured questioning technique: Situation, Problem, Implication, Payoff. Rewards structured needs development over an early pitch.
Qualification Grid
qualificationFor complex enterprise deals: metrics, economic buyer, decision criteria, decision process, pain point, champion. Measures decision-making maturity.
Budget-&-Decision Check
budget-checkClassic qualification: budget, authority, need, timeline. Scores whether all four levers were properly addressed.
Pain-First Method
pain-firstPain · Budget · Authority. Rewards surfacing genuine pain early and avoiding unpaid consulting.
Teach & Lead
teach-leadTeach · Tailor · Take control. Scores whether the rep introduces a new perspective and steers the conversation with confidence.
Closing Mindset
closing-mindsetCommitment-focused framework: accountability and consistent objection handling. Tailored to the DACH market.
Default is needs-question + qualification for need decision-process coverage. Switch frameworks at any time, per session.
Objective scoring vs. LLM black box.
Many AI training tools let a language model freely guess a score. Run it a second time and you get a different number. That is exactly what makes a score worthless in enterprise contexts.
The goal is a shared language for conversation quality, and a score whose derivation can be traced. That is exactly what makes the process verifiable for auditors and works councils.
Built with sales teams, for accountable evaluation.
Scoring & methodology: clearly answered.
Start today with 2 free training sessions: 4 weeks to try it.
No credit card. Setup in 2 minutes. German data storage.