Mobile Dev Memo Podcast – Season 7, Episode 9: RecSys and Internet Commerce (with Michael Komasinski)

Date: March 11, 2026
Host: Eric Suefert
Guest: Michael Komasinski (CEO, Critio)

Episode Overview

In this episode, Eric Suefert interviews Michael Komasinski, CEO of Critio, about the evolving landscape of recommendation systems (RecSys), agentic commerce, and the integration of AI-driven shopping assistants in internet commerce. They discuss Critio's new product launches, how RecSys bridges ads and commerce recommendations, the difference between agentic and autonomous commerce, Critio's partnership with OpenAI, and industry trends affecting advertisers, platforms, and consumers.

Key Discussion Points & Insights

Guest Introduction and Career Background

• Michael Komasinski brings extensive experience from executive roles at Dentsu, Merkle, and Nielsen, offering a well-rounded view of the digital advertising landscape.
  • “Nielsen is kind of where I grew up as an executive, just a very rigorous company, really great on product development and just the way that they run the place year to year.” (02:23)

Agentic Commerce and Critio’s Role

• Agentic Commerce RecSys for AI Shopping Assistants

  • Critio launched a commerce intelligence layer for AI assistants, powering product recommendations inside conversational environments driven by real-time purchase data and advanced AI models.
  • Offline tests show a “60% uplift in product relevance and accuracy” compared to LLM-only recommendations. (03:59, Komasinski)
  • Focus now is on partnership onboarding and outcome validation—monetization to follow.

• Agentic vs. Autonomous Commerce

  • Critio favors the "augmented/assisted" agentic model over fully autonomous purchasing.
  • Anecdote: People's willingness for autonomy ends at reordering basics like toilet paper; they still want control over most purchases.
    • “Everybody automatically defaults to the toilet paper reordering question... and then they immediately go to augmented and assisted [shopping] as long as they get to approve the final purchase.” (08:11, Komasinski)
  • Forecast that customer journeys will remain multi-touch: search, research, branded environments, ending in informed purchase decisions.

• Compatibility and “Friends with Everyone” Model

  • Critio's commerce graph complements Amazon’s data. Critio can operate alongside any retailer/platform.
    • “Our graph has intelligence about people's interactions with products in different environments… those two data sets are the two most powerful commerce data sets in the industry.” (12:14, Komasinski)

RecSys, Ads Targeting, and the “Matchmaker Role”

• Ad Targeting Is Applied RecSys

  • “Ads is applied RecSys… everyone is pointing their research bazooka at this because this is where all of the frontier research is yielding these incredible gains.” (15:20, Suefert)
  • Ad platforms and RecSys share 80% technical overlap—especially for Critio, which originated as a DVD recommendation engine before pivoting to retargeting. (16:55, Komasinski)

• Difference Between Generic Ad Targeting and Agentic Recommendations

  • "Traditional ad targeting... would be like audience based, agentic recommendations are more like product level decisioning inside of a live interaction.” (17:55, Komasinski)
  • Critio’s models optimize for sale/conversion, not just ad delivery.

Use Cases and Expansion Beyond Commerce

• Use Cases

  • Applicable anywhere structured decisioning improves recommendations: travel (flights, hotels surfaced based on true demand), restaurants (rankings based on likelihood to book), financial services (credit card offers matched by profile and intent). (20:14, Komasinski)
  • Currently focused on e-comm and fast-moving consumer goods but architecturally capable of expanding to automotive, vertical-specific conversational assistants, and beyond.

• Live Testing and Confidential Partners

  • Two large, but unnamed platforms are currently testing Critio’s RecSys for conversational shopping and product recommendations. (21:36, Komasinski)

Modes of Interaction and Adoption

• Conversational interfaces will become standard for shopping and product discovery—moving away from query-based to chat-based experiences.
  • “I think that has become kind of a new functional norm in terms of doing research… your first stop is a [chatbot].” (24:01, Suefert)

Critio Product Updates and Roadmap

Commerce Go – Self-Service Platform for SMBs

• Overview: Simplifies campaign launch for small businesses—self-registration, auto-tagging, cross-channel (open web, social) in "five clicks to campaign."

  • “A small medium advertiser can be up and running literally within 15 minutes.” (26:39, Komasinski)
  • 20% higher ROAS, lower churn, more spend among 3000–4000 early clients.

• Differentiation

  • Only cross-channel self-service product vs. single-platform tools like PMax or Advantage+.
    • “What's unique about GO is the cross channel setup and the ability to hold constant performance across.” (30:01, Komasinski)

• Measurement

  • Integrates directly with common analytics/tagging tools (e.g., Google Analytics), automates technical integration for non-technical users. (29:06, Komasinski)

2026 Roadmap Highlights

• Agentic:
  • Scale OpenAI integration (announced this week), add use cases for recommendation services, conversational shopping, conversational ads.
  • Deprecate legacy UIs in favor of MCP (multi-channel platform) front ends for scalability.
• Performance Media:
  • Move “up funnel” via a new Discovery Ad product, expanding supply (CTV etc.).
• Retail Media:
  • Scale auction-based display, shoppable video, conquesting, and demand-side tool Commerce Max.
  • Improved cross-retailer support, measurement, and new insights packages.

Critio x OpenAI Partnership

Announced: The week of this episode (Monday)
Scope: Critio becomes OpenAI's first ad tech partner; integration allows brands to access a contextual ad unit in ChatGPT (free and Go versions) via Critio.

• Notable Quotes:
  • “The inbound from clients this week has been incredible, like just level of interest, like nothing I've ever seen.” (34:11, Komasinski)
  • “Our studies show that the traffic that comes from LLM platforms like ChatGPT, like they convert at one and a half times the rate of other referral channels.” (34:11, Komasinski)
  • “Because of how OpenAI is approaching this, companies like Critio now have access to that TAM that we didn't have before. So it's purely incremental to play in the discovery layer in a way that we couldn't, you know, even two weeks ago.” (35:53, Komasinski)

Memorable Moments & Notable Quotes

• On the semantics of “agentic” vs. “autonomous”:

  • “We definitely subscribe to the latter [augmented/assisted] definition. So like you, we are skeptical of the autonomy use cases.” (08:11, Komasinski)

• On RecSys being everywhere:

  • “If there's a decision and structured data behind it, like this model can apply to bring relevancy and personalization to any user experience.” (20:14, Komasinski)

• On future of shopping:

  • “The way I do shopping discovery will be conversational going forward essentially across any category.” (25:20, Suefert)

• On scaling with MCP:

  • “Managed service just doesn't allow you to scale a platform as quickly... automating those things or just making them easier to access and not getting hung up in a lot of front end UI and workflow.” (32:57, Komasinski)

Notable Timestamps

• Introduction and Background: 00:42 – 03:36
• Agentic Commerce RecSys Launch & Use Cases: 03:59 – 06:17
• Agentic vs. Autonomous; The Toilet Paper Test: 08:11 – 11:19
• RecSys & Ad Targeting Overlap: 15:20 – 17:55
• Recommendation Use Cases Beyond E-comm: 20:14 – 21:36
• Commerce Go Platform Overview: 26:34 – 30:01
• 2026 Roadmap & MCP Strategy: 30:56 – 32:57
• OpenAI Partnership Details: 34:11 – 36:33

Tone & Style

• The discussion is candid, technical, and insight-driven.
• Both Eric and Michael favor clear, direct explanations and thoughtful skepticism, especially around buzzwords and industry hype.
• The mood is energetic yet measured, reflecting optimism about innovation with realism about user agency and scaling challenges.

This summary delivers a comprehensive walkthrough for anyone wishing to understand Critio’s approach to next-generation commerce recommendations, the true meaning of agentic commerce, and the evolving intersections of AI, advertising, and discovery.