Intelli Deal
Designed and developed an internal app enabling Pricing Specialists to make data-driven decisions and increase deal profitability using AI.
(YEAR)
2024
(TIMELINE)
18 Months
(ROLE)
End-to-End Product Discovery, UI & Design Governance
(PROJECT TYPE)
Strategy
We analyzed leading CPQ platforms to understand how pricing workflows operate today, uncovering inefficiencies in fragmented systems, manual processes, and limited AI adoption. These insights revealed opportunities to reimagine pricing as a unified, intelligent, and proactive decision-making experience driven by AI and real-time data.
“
It is very difficult to browse through multiple meta data from multiple different applications. With complex pricing the client retention is very important and being efficient is of high importance.
Kunal Verma
Special Pricer
We mapped the end-to-end pricing journey to uncover how deals are analyzed, validated, and finalized, identifying key friction points and opportunities where AI can accelerate decision-making and improve win outcomes.
Special Pricers across APJC and EMEA relied on fragmented Salesforce data and manual tracking to price deals, a 12-step workflow shaped by complex requirements from multiple stakeholder groups. I simplified this down to 7 steps, navigating competing priorities from PMs, stakeholders, and pricers to cut friction without losing decision nuance. The result: pricers submitted 43% more accurate deals per quarter.
The Intelligent Deal Engine combined AI-powered referencing, pricing simulation, and a custom rule engine into one product, but the complexity needed structure. After a prioritization exercise with PMs and stakeholders, I organized the product around three distinct workspaces: a Dashboard for deal management and historical context, a Playground for building and testing pricing rules, and a Simulation space for running predictive deal scenarios. This separation gave each pricer workflow its own depth without overwhelming the experience, creating a clear mental model that mapped to how pricers actually think: review deals, refine rules, test outcomes.
This shows how different deal inputs come together and are processed by a smart system to guide decisions. By combining past data, rules, and AI, it helps users quickly understand outcomes like deal quality, confidence, and risk, making pricing decisions clearer and more reliable.
Each solution targets a specific pricer pain point, from rule creation guidance to transparent scoring using AI capabilities designed to make every pricing decision faster, more informed, and defensible.
Pricers were creating rules that underperformed or abandoning the flow entirely because they lacked clarity on what makes a rule effective. I designed an AI-powered guidance layer that surfaces contextual, task-specific suggestions during rule creation, helping users refine patterns in-flow rather than through trial and error.
Pricers previously relied on gut feel and manual Salesforce lookups to set pricing. I designed a prediction system that draws from historical deals, SFDC market data, client history, and product configurations to suggest metrics with built-in explainability so users can see exactly how a recommendation was derived before accepting or overriding it.
Without visibility into how deals were evaluated, pricers couldn't trust or challenge the system's output. I designed a transparency layer that exposes the exact metrics and inputs the model uses giving users clear line-of-sight into how scores from Grade A to Grade F are determined, so every decision is auditable and defensible.
Before diving into a deal, pricers needed a quick read on where it stands. I designed a benchmarking view that surfaces revenue, margin, discount, and win probability upfront, paired with an interactive area graph that compares the current deal against comparable, top-revenue, and won deals using AI-curated comparisons to remove subjective bias from evaluation.
Improved pricing accuracy, streamlined workflows, and stronger user trust led to smarter, faster, and more consistent decisions.
“
Somnath was a dependable and thoughtful contributor to the project. He was proactive, collaborative, and took full ownership of his tasks. I saw steady growth in his work throughout the time we worked together.
Shalaka Haldankar
Senior AI Product Designer @ Dell Technologies
