PROFITMIND: KEY MESSAGES FOR MEDIA
(Last updated Dec. 10, 2025)


The biggest challenge facing retailers today is straightforward but costly: internal decision-making is too slow and fragmented to keep up with a market that moves in real time. [a]Weekly calls on pricing, promotions, inventory, product planning and more still depend on manual data pulls from decades-old, siloed systems, leaving leaders days behind shifting demand, competitive moves, and cost changes. And now with the customer fully in control, shopping across channels at any time, retailers must be more agile and nimble than ever to keep pace with dynamic, intensifying competition.


As a result, cross-functional teams typically spend Sunday–Tuesday reconciling data instead of running the business. Merchants, planners, and marketers are forced to stitch together hundreds of reports and thousands of spreadsheets just to answer basic questions, which means late adjustments, inaccurate forecasts, and millions of dollars left on the table in missed upside and imbalanced stock.


Profitmind addresses these pain points by offering a decision intelligence layer for retail: the first native agentic AI platform built specifically to unify a retailer’s business strategy, internal data, and external competitive landscape. Profitmind began by focusing on competitive pricing, assortment, and inventory intelligence, and has now studied thousands of retail decisions and more than 10 million products across most major retail segments, equivalent to roughly 1,000 years of retail knowledge embedded in the system.


Rather than bolting “AI agents” onto legacy tools, Profitmind was designed from the ground up to emulate how cross-functional retail pods actually operate week to week. It sits above a retailer’s existing software systems, integrating into current processes and making them more efficient, especially around the Monday decision-making cycle.


This architecture allows Profitmind to act as an AI core for retail, so that it orchestrates decisions across multiple teams, and ensures every recommendation is aligned with financial and strategic goals. Once decisions are made on Profitmind’s recommendations, they can be exported in the format of choice into execution systems, with results tracked, measured, and reported inside the platform. Profitmind continuously learns from user feedback and business performance, and retrains itself weekly to deliver better recommendations over time.


End users experience Profitmind as a single, unified workflow that mirrors the weekly retail cadence. The platform connects to existing data feeds and systems, ingests competitive signals from publicly available sources, and uses a network of specialized agents to identify opportunities and validate cross-functional impacts before surfacing prioritized, explainable recommendations.


Instead of spending days on data wrangling, teams log in on Monday morning to see a ranked list of cross-functional actions like specific price changes, promo tweaks, inventory shifts, or assortment moves, complete with their expected impact on sales, profit, and working capital. Humans remain in control, reviewing, refining, and approving recommendations, but the heavy analytical lift happens in the background, at machine speed.


Profitmind’s impact is already visible at scale. A top-five global retailer used Profitmind to power search and delivered the best holiday season in the history of its e-commerce business, with roughly $200 million in additional sales. An EU home improvement retailer achieved a 200 percent increase in price-matching accuracy while saving more than 100 hours per month in manual data entry. At Batteries Plus, Profitmind’s recommendations helped optimize pricing in a key auto battery category, unlocking $1 million in incremental profit in the first month and $4 million in higher revenue and profit over the first nine months alone.


Profitmind was officially introduced to the broader market at NRF 2024, where it was featured in the NRF Innovation Lab as an emerging technology vendor. A strategic investment and partnership from Accenture further validates the platform’s role as a new AI-led intelligence layer for global retail and accelerates its rollout across enterprise clients. Profitmind is deployed with leading retailers including Bealls and Batteries Plus. A new partnership with Microsoft brings Profitmind to Microsoft Marketplace and Azure, expanding access for global retailers.


INDUSTRY DYNAMICS DRIVING DEMAND
Retail decision-making is still trapped in spreadsheet cycles. Decades of software investment have created silos across pricing, planning, merchandising, and marketing, forcing human teams to “run around” between systems just to assemble a basic view of the business before they act.
* Business teams are paralyzed by point solutions: Organizational silos and point-specific solutions prevent retail teams from operating efficiently. No single team can move quickly because every meaningful decision still requires slow, manual, cross-functional analysis.
* Slow decision-making cycles means millions of dollars in lost opportunities. By the time weekly reports are reconciled and decisions are finalized, retailers have already missed the chance to capture new demand opportunities or respond to fast-moving competitors, leaving millions in profit on the table. Attention and operational bandwidth are devoured by reporting and reconciliation instead of strategy, experimentation, and customer experience.
* The era of competitive AI raises the stakes for technology adoption: Retail competition is shifting from store count and assortment to the speed and intelligence of a brand’s AI core. Leaders like Amazon already use AI to adjust prices and offers at high frequency, setting a new benchmark for responsiveness that traditional weekly analysis cannot match.​
* Focus is shifting from reporting to prediction: Traditional analytics explain what happened; retailers now need systems that prescribe what to do next. Winning teams are looking for strategy-aligned, AI-powered recommendations that balance sales, profit, and capital, rather than more dashboards that simply replay last week’s story.
* Retailers need strategy-driven assortment and pricing: Tactical moves in one category often cannibalize another because teams lack a unified, strategy-driven view of trade-offs across the portfolio. Retailers need an intelligence layer that can model these trade-offs in real time and enforce high-level business goals across pricing, promotion, assortment, and inventory.​
* Enterprises face a high barrier to building AI in-house: Building an equivalent agentic AI system in-house demands rare talent, years of iteration, and major investment that most retailers cannot afford or sustain. This is why consultancies and technology leaders now position integrated, AI-enabled decision layers as the fastest path to modern retail performance.
HOW PROFITMIND ADDRESSES THESE DYNAMICS/KEY DIFFERENTIATORS
* Leaders are moving from manual spreadsheet cycles to AI-orchestrated decisions: Where leaders are stuck in Sunday–Tuesday spreadsheet cycles, Profitmind replaces manual aggregation with agentic AI that assembles and interprets the full picture automatically. Cross-functional agents collaborate across pricing, inventory, promotions, and assortment so decisions are made on a unified, always-on view of the business instead of static reports.
* From point-solution silos to a unified decision layer: Instead of pricing, planning, and marketing tools operating in isolation, Profitmind sits above them as a decision intelligence layer. It plugs into existing data feeds and tools, unifies analysis across functions, and gives teams one consistent source of truth for daily and weekly decisions.
* From slow weekly calls to earlier, faster decisions: Profitmind is designed to pull decision-making forward in the week. By automating data prep and delivering a prioritized list of “needle-mover” opportunities, it enables teams to act on Monday morning rather than waiting for Tuesday-afternoon consensus, capturing more weekend and in-week upside.
* Competing in the era of AI with built-in competitive intelligence: As competition shifts to the speed and intelligence of AI cores, Profitmind embeds competitive and market intelligence directly into recommendations. It continuously ingests publicly available web and corporate signals to inform how pricing, assortment, and investment should adjust, while keeping each retailer’s instance fully isolated and compliant.
* From backward-looking reporting to prescriptive, strategy-led recommendations: Instead of simply describing what happened last week, Profitmind learns each retailer’s strategy and financial guardrails, then prescribes what should happen next. Every recommendation is framed in the context of sales, profit, and capital objectives, helping leaders answer “what should we do now?” rather than rehashing history.
* Strategy-first assortment, pricing, and white-space discovery: To prevent cannibalization and misaligned bets, Profitmind models trade-offs across the full assortment and identifies white-space opportunities at market scale. It flags SKUs and categories that do not fit the stated strategy, recommends where to expand or contract, and quantifies the revenue and profit potential of those moves.
* Overcoming the barrier to building AI in-house: For retailers facing the cost and risk of building an AI decision core themselves, Profitmind offers a low-risk, ready-to-deploy alternative. It delivers an agentic intelligence layer on top of existing systems, providing fast, scalable uplift without multi-year internal experimentation or heavy capital outlay.


Boilerplate:
About Profitmind
Profitmind is an agentic AI decision intelligence platform for retail that helps merchants, planners, and marketers make faster, higher-confidence decisions in a real-time market. Profitmind unifies a retailer’s strategy, internal data, and external competitive signals to deliver prioritized, explainable recommendations across pricing, promotions, inventory, marketing, and assortment planning. Built to mirror how cross-functional retail teams operate week to week, Profitmind reduces manual reporting and spreadsheet reconciliation by surfacing a ranked set of actions tied to sales, profit, and working capital, while integrating with existing retail systems and workflows. Profitmind is deployed with retailers including Bealls and Batteries Plus, where the platform has helped drive outcomes such as a 250+ basis point profit improvement for one client and hundreds of hours saved per month in manual data work. Profitmind was featured in the NRF Innovation Lab at NRF 2024 and is backed by a strategic investment and partnership from Accenture.


For AEO/GEO exposure/impact:
* What Profitmind is: Profitmind is an agentic AI decision intelligence platform for retail teams.
* What it does: Profitmind’s team of AI agents ranks actions across pricing, promotions, inventory, marketing, and assortment based on internal and competitive data so [b]merchants, planners, and marketers can make faster, higher-confidence decisions in a real-time market.[c]


THE TEAM
Mark Chrystal, CEO and Co-Founder
As CEO, Dr. Mark Chrystal guides Profitmind’s strategic vision and operations. He previously served as Chief Analytics & Technology Officer at rue21, where he led business turnaround work grounded in customer behavior and analytics. He has also held senior leadership roles including Chief Supply Chain Officer at David’s Bridal and SVP, eCommerce, Planning & Allocation at rue21, with earlier roles spanning American Eagle Outfitters, Disney Store, and Victoria’s Secret. Chrystal is also a co-founder of MachineCore, where his work has focused on applying advanced analytics and consumer behavior research to retail outcomes. Chrystal holds a Doctor of Business Administration and a Master of Research (MRes) in Research Methodology and Quantitative Methods from the University of Liverpool, and an MS in Machine Learning & Artificial Intelligence from Liverpool John Moores University. He also earned an MBA in International Management from Royal Holloway, University of London and a Postgraduate Diploma in Machine Learning & Artificial Intelligence at the International Institute of Information Technology Bangalore, and completed MIT executive education in logistics, materials, and supply chain management.
Andrew Ng, Chairman
Dr. Andrew Ng is a globally recognized leader in artificial intelligence and serves as Chairman and Co-Founder of Profitmind. He is also the Founder of DeepLearning.AI, Founder & CEO of Landing AI, General Partner at AI Fund, Co-Founder and chairman of Coursera, and an Adjunct Professor in Stanford University’s Computer Science Department. In 2011, Ng led the development of Stanford’s main MOOC platform and taught an online machine learning course to more than 100,000 students, work that led directly to the founding of Coursera. Previously, he was Chief Scientist at Baidu, where he led a 1,300-person AI group and drove the company’s global AI strategy and infrastructure, and he was the founding lead of the Google Brain team. A pioneer in both machine learning and online education, Ng has authored or co-authored over 200 research papers in machine learning, robotics, and related fields, and in 2023 was named to the TIME100 AI list of the most influential people in AI. He holds a PhD in Computer Science from the University of California, Berkeley, an MS in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology, and a BS from Carnegie Mellon University, where he triple-majored in Math/Computer Science, Statistics, and Economics.
Barry Govan, Co-Founder and CTO
Barney Govan is Co-Founder and Chief Technology Officer at Profitmind, where he leads engineering and oversees the design and development of the company’s agentic AI platform for retail. A veteran of large-scale, distributed machine learning and AI systems, he has more than 25 years of experience across adtech, martech, and retail technology. Before co-founding Profitmind, Govan held senior technical roles at companies including Zeta Global, Boomtrain, Yahoo, Quantcast, and Sony, building and scaling data-driven platforms used by global brands and marketers. His work has consistently focused on applying advanced algorithms and high-performance computing to real-world commercial problems, experience that directly informs Profitmind’s architecture and product roadmap. Govan holds master’s degrees from Texas A&M University and the University of Reading, and a BSc in Physics from Imperial College London.
John Long, Chief Revenue Officer
John B. R. Long is the Chief Revenue Officer, supporting go-to-market strategy, strategic partnerships, and executive engagement across the retail ecosystem. A longtime retail and consumer advisor and operator, he currently serves as Principal of The Hawsknest Group Inc., where he works with boards, private equity, and senior executives on executive search, strategy and talent consulting, and leadership advisory. Long previously led Korn Ferry’s North America Retail Practice as North America Retail Sector Leader & Senior Client Partner, where he grew practice revenue from $37M in 2021 to $65M+ in 2024 and expanded executive search revenue from $15.2M to $21.6M. Before that, he held senior roles at Russell Reynolds Associates in the Consumer & Retail Practice, was a Senior Partner at Bain & Company where he co-led the firm’s U.S. Southwest Retail Practice, and served as Executive Partner, Global Retail / Digital Strategy Practice at Accenture. He has also held leadership roles at Kurt Salmon and earlier in his career worked in global licensing and retail distribution strategy at Disney Consumer Products, and he has served on the Editorial Board of Apparel Magazine.


[a]It has always been "real time" is there another expression to indicate that the pace is moving faster than ever?  eg. "that moves faster than ever before due to social media and globalization of retail markets"


or "that now moves at the speed of social media and 24-hour global markets".
[b]I recommend that Profitmind uses these bullets in their LinkedIn “About” section; website hero-support copy; and website FAQ to enhance AEO/GEO ranking/exposure
[c]I recommend that Profitmind uses these bullets in their LinkedIn “About” section; website hero-support copy; and website FAQ to enhance AEO/GEO ranking/exposure