Case Study
DealIQ OS
An AI-powered deal assistant built for how modern sellers actually work. MEDDPICC scoring from call transcripts, deal coaching, and a full CRM — all in one place.
The Problem
Sellers lose deals they should have won.
The Economic Buyer was never confirmed. They thought they had a champion but that person couldn't actually sell internally. The Decision Process was assumed, not mapped. The Identified Pain was real — it just lived in one call note from month two and never surfaced again.
MEDDPICC exists as a framework because complex B2B sales are predictable if you know where to look. The problem is that most reps run qualification in their head, informally, across a 9 to 18 month cycle. By the time a deal goes quiet, the gaps are obvious in hindsight.
Applying MEDDPICC rigorously — on every deal, every week, at every stage — requires a discipline that competes with everything else: demos, follow-up, pipeline reviews, prospecting. It either gets done inconsistently or it doesn't get done.
The problem wasn't that reps didn't know the framework. The problem was the overhead of applying it consistently.
What It Does
MEDDPICC scoring. Automatically. From the data you already have.
DealIQ OS is a full CRM built around deal coaching, not data entry. It connects to how you already work and adds the AI layer that makes qualification automatic.
- →Upload a call transcript. Every MEDDPICC dimension is scored, evidence cited, gaps flagged. Takes under 30 seconds.
- →Pipeline view surfaces risk by deal. Which opportunities are qualification-complete. Which have blind spots. Which are at risk of going dark.
- →Deal coaching doesn't tell you what happened. It tells you what to do next: which gap to close on the next call, which question you haven't asked.
- →Deal timeline tracks qualification progression over the full cycle. Score at month one versus score at month six. You can see the deal getting healthier or getting riskier.
- →Full CRM underneath. Every deal, every note, every contact, every call — all in one place. Not a scoring overlay on top of Salesforce.
The Build
What was actually hard.
Getting the AI to surface gaps, not just confirm presence.
Most LLMs default to finding evidence of the positive. Feed them a transcript, they'll tell you what's there. The coaching value in MEDDPICC is in what's missing. The prompt engineering to reliably surface absent dimensions — without hallucinating evidence — required significant iteration. The model had to learn to reason about silence, not just signal.
Building a real CRM, not a scoring widget.
The temptation was to build a scoring layer that sits on top of an existing CRM. The problem with that approach: the context lives in the wrong place. Deals needed to live in DealIQ OS natively — notes, contacts, call history, stage — so the AI could reason across the full context of each deal, not just the most recent transcript.
Schema design for deal evolution.
A deal's MEDDPICC score changes over time. The data model needed to track scoring history, not just the current state. That meant thinking through the schema carefully upfront: deal records, score snapshots, coaching sessions, and call transcripts all interconnected in a single source of truth.
The Stack
Production-grade from day one.
The Impact
What actually changed.
Earlier signal.
Qualification gaps surface at the end of month two instead of the end of month eight. There's still time to close them.
Objective coaching.
Deal coaching moves from manager's intuition to scored evidence. The conversation shifts from “how do you feel about this deal” to “economic buyer is unconfirmed and decision process is unverified — here's what to do about it.”
Consistent application.
MEDDPICC stops being something reps apply when they remember and starts being something the system applies on every call. Consistency compounds.
The honest version: the AI scores what's in the transcript. If a rep doesn't ask the right questions, the gaps stay gaps. DealIQ surfaces the problem — it doesn't fix the seller.