top of page
Wooden Architecture

Altum Black

The smartest revenue management tool  

Altum Black delivered through the integration of retail, supply chain, marketing, and pricing, with our proprietary analytical layer effectuating actionable insights to business users.   

Our Story

In 2018, Altum Group Advisors started as a consulting firm providing revenue management, supply chain optimization and pricing analytics for B2B and B2C market participants. 


After running the company successfully for several years, one theme became resoundingly clear: spreading data across multiple silos makes it incredibly difficult for decision makers to get a holistic view of the enterprise. Realizing that most information in enterprise applications goes untapped, Altum Black spun out as a commerce SaaS solution to address this pain point.

Altum Black connects the dots by extracting and integrating valuable data from retail, pricing, marketing and supply chain departments into a repository warehouse. Our commerce intelligence is cutting edge because it enables stakeholders to make better decisions, coordinate their activities, collaborate more effectively and minimize avoidable errors caused by incomplete data sources.

Our Vision

Delivering succinct, actionable decisions for D2C and B2B market participants.  By creating an application environment in which retail, pricing, marketing, and supply chain KPIs are connected, otherwise opaque elements of commerce can be connected.   By simplifying a significant level of quantitative complexity, we can deliver beyond KPIs by providing insights that are generated by our platform, thus enhancing the tools at the disposal of decision makers.

  • All existing products optimize a vertical (e.g., supply chain optimization or pricing optimization) separately do not consider the interrelated nature of outcomes, and the corresponding dangers of “silo/local” optimizations.  For example, if marketing, pricing, and supply chain elements are not integrated, it is virtually certain that significant profitability leakage will occur.  In an environment of single channel operations or few SKUs, manual efforts may be possible to achieve desired outcomes, though in any scaled environment, manual efforts are not feasible. 

  • Example of data Integration (1): Current methods of marketing “optimization” are based on simplistic and often misleading metrics such as ROAS or Conversion Rate %, further, these metrics are most frequently calculated based on sales and not on underlying profitability.  Further, the data supporting decisions is typically “static” and does not comprehend such critical elements as demonstrated volume/price elasticity.  A decision framework without visibility and consideration of underlying variables will lead to false positives, negatives, and overall sub-optimization of results. 

  • Example of data Integration (2): Pricing and marketing optimization cannot happen without input elements of en-route supply. If the inbound is delayed, for instance, then marketing should be cut, and prices should increase in order to slow down the sales and maximize the VCM until the inventory arrives at the warehouse. No company wants to clear their inventory with low VCM and stay out of stock for months. Despite such a universally prevalent business problem, no widely available product exists to solve it (without significant customization and risk of near term obsolesces). 

Our Positioning

The product will be positioned between:  

BI: Business Intelligence products (e.g., Power BI, Qlik, Tableau) which are entirely dependent on the sophistication of the end user to create robust, dynamic, connected underlying data models.  


AI: Artificial Intelligence solutions which are segregated/isolated and vertically focused without reaching across the spectrum of variables required to deliver both globally optimized solutions, and also provide sufficient transparency to end users so they do not feel that a “black box” is governing their business decisions. 

Who are we
Wood Transparent



Arash Beheshtian, Ph.D.

Arash Beheshtian has an undergraduate degree in Civil Eng. and master's degrees in Planning, Transportation Eng., and Systems Eng. He earned his doctoral degree in Planning from Cornell University. He has been a post-doctoral fellow at Cornell University and a research fellow at Massachusetts Institute of Technology.  

​He started consulting right after his undergraduate studies and since then, he has accomplished over a dozen projects in a large variety of industries such as transportation and logistics, energy, and retail. He is an adjunct Professor at Columbia University.  

Arash is a Co-Founder at Altum Black. 

IMG_07wqw24 (1)_edited.jpg

Shirin Hejazi, Esq.

Shirin Hejazi is a graduate of University of California Berkeley where she received a Bachelor of Arts degree in Legal Studies. She received her law degree from USC Gould School of Law and is admitted to practice before all state courts in California.


Shirin has consulted on a wide range of projects including retail and supply chain. 


Shirin is a Co-Founder and General Counsel at Altum Black. 


Rick Geddes, Ph.D.

Rick Geddes has an undergraduate degree in Economics and Finance. He holds both masters and doctoral degrees in Economics from the University of Chicago. He is a Professor in the Department of Policy Analysis & Management and in the Department of Economics at Cornell University. 


Rick has consulted on a wide range of projects in infrastructure space. He has completed projects for United Parcel Service, the Federal Trade Commission, the Conference Board, and the Australian Price Surveillance Authority, among many others.  He has taught classes in Economics and Infrastructure at the University of Chicago, Cornell University, and Yale.

Rick is a Senior Advisor at Altum Black. 

How it works

Altum Black is a permanent, yet scalable, ERP solution to problems otherwise unsolvable!


Integrated solution for retail! 


Flexible objective function with respect dynamic business plans!

Resource sharing oportunities in all streams of business including supply chain, marketing and pricing. 





Start by setting ambitious business objectives, targeting large value pools, and identifying capability gaps. 


Select high-value use cases, launch focused pilots, then build, test, and iterate. Using an agile, sprint-based approach, cross-functional teams drive change in an integrated way.


It is crucial to build the digital and human capabilities needed to sustain and scale their artificial intelligence strategy. We should be prepared to develop new ways of working, create opportunities for reskilling and upskilling, reimagine processes to facilitate true human–machine collaboration, and deploy a robust AI architecture.

Request demo

Unleashing the power of signals hidden deep within large and complex data sets!

260 Fifth Ave., Unit 12S, New York NY, 10001

bottom of page