IntroHub

IntroHub is an AI Agent that brokers introductions to your target contacts through other members' contacts. Here's how it works:

• Users sign-up with a Google Apps account, granting IntroHub access to email metadata and send permissions.

• Users identify their target prospects from available filters, such as job titles, industries, regions, etc. 

• IntroHub automatically identifies and requests intros on your behalf to relevant contacts via another member. 

• As a member, you don't need to review or approve introduction requests. Instead, IntroHub's AI Agent emails the target contact, seeking their consent when someone wants an introduction. This ensures all connections are mutually agreed upon without overloading you with requests.

• IntroHub keeps your network spam-free. Approval requests are limited to once every 7 days per contact, and you can customize this setting to better suit your preferences.

Co-founder & CEO

Arrowhead Center: New Mexico State University

I coach and advise NMSU and UTEP professors, post-docs, and students on product, marketing, sales, and fundraising.

Entrepreneur In Residence

BrandWeaver.ai

BrandWeaver is a GenAI-powered platform that helps publishers unlock new ad revenue by weaving personalized product placements directly into their editorial content. Here's how it works:

• BrandWeaver analyzes a publisher’s existing content and generates brand-aligned ad copy that matches the article’s tone, topic, and audience. 

• Using a lightweight JavaScript tag, BrandWeaver dynamically inserts the AI-generated content blocks into the page—creating seamless, non-intrusive ad placements.

• Each placement is contextually relevant and personalized, increasing reader engagement while preserving the editorial experience.

• Publishers maintain full control: BrandWeaver offers content review tools, styling options, and A/B testing capabilities to fine-tune performance.

• All campaigns are tracked with impression logs, engagement data, and revenue reports. Our prompt evaluation system ensures outputs improve over time based on performance data and publisher feedback.

BrandWeaver turns GenAI into a monetization engine, helping publishers scale inventory without interrupting their readers.

Integrate Inc.

• Led ABM product division, which grew revenue 300% during my tenure.
• Set strategy and vision for ABM products, coordinating resources across teams and departments.
• Interviewed enterprise customers to identify their pains and evaluated features against viability, feasibility, desirability criteria.
• Led pricing strategy to convert product from SMB/Mid-Market to Enterprise positioning.
• Pitched enterprises as part of a 5-person team and closed 7-figure, multi-year contracts.
• Developed enterprise sales decks, product roadmaps, and QBR materials with Product Marketing dept.

VP of Product, ABM Division

ListenLoop

• Co-founded a B2B SaaS company, leading it from initial product development to acquisition.
• Interviewed prospects to identify their "jobs to be done", pains, and gains 
• Mocked-up solutions and tested viability using Lean Startup methodology
• Created product roadmap, optimizing for iterative delivery with engineering team
• While v1 software was engineered, performed outbound sales using Predictable Revenue method (eg, prospected, emailed, and called to book meetings; then discovered, qualified, and sold software in meetings)
• Delivered multiple product iterations, using KPIs -- such as user engagement, revenue, and retention -- to measure success
• Refined product roadmap based on user interviews, Mixpanel data, and market opportunities
• Used outbound sales learnings to create full-funnel marketing engine (eg, authored sales decks, eBooks, white papers, case studies, email nurture tracks, automation programs, proposals, website HTML/CSS, etc.)
• Set up repeatable revenue process using CRM and marketing automation softwares, which I delegated to sales and marketing managers that I trained
• Created "thought leadership" content via blog, videos, and speaking engagements to support brand and demand initiatives
• Handled customer success and support at first, then trained in-house and offshore teams to handle success calls and support tickets
• Managed company finances, such as P&L, budgeting, accounting, and raising $2M in venture capital
• Led human resources through growth and pivot/downsizing phases
• Handled legal affairs, working with hired counsel on H1B visas, patent applications, and M&A process

RC Labs, Inc.

RC Labs, Inc. was the parent company of callTrackingFox, a SaaS company that linked eCommerce phone orders to online keyword sources.  After invalidating that product, RC Labs founded a subscription commerce startup that delivers romantic experiences for couples every month. 

I focused on online marketing, product development, graphic design, front-end development, and business development.

KIUBOshop.com

• Conceived and launched KIUBOshop.com, an e-commerce business that sells nostalgic Mexican products to the growing Hispanic population in the U.S.
• Managed website design, prepared online ad campaigns, and marketed site via social media
• Sourced products from distributors and negotiated favorable concessions regarding minimum orders to reduce inventory

Founder

Fish & Richardson P.C.

Practiced intellectual property law, where I:

• Defended e-commerce, chemical, and financial services companies in patent infringement lawsuits in federal trial courts in teams of 3-10 attorneys. 
• Protected clients’ intellectual property by filing patent application and drafting employee contracts.
• Managed team of 3-4 attorneys, experts, and paralegals to sue company, review thousands of documents for trial, and analyze settlement options.
• Drafted pitch letters and presentations with 3 attorneys to secure 2% of new clients in a year.
• Prepared scores of motion papers regarding jurisdiction, dismissal, discovery, judgement as a matter of law, motions in limine, and bench memoranda.

Associate

• Drafted various motion papers, including a motion to dismiss an appeal to the Federal Circuit (granted) and a motion to recover costs and attorneys' fees (granted).
• Researched and prepared memoranda on: standing, joinder, expert witnesses, trademarks. 
• Drafted office actions from the U.S. Patent and Trademark Office. 
• Prepared online pitch book for a new client.

Summer Associate

IBM, T.J. Watson Research Center

Summer 2005: Implemented a prototype hardware video processor to conserve bandwidth in high-resolution video applications using programmable digital logic.

Summer 2004: Designed and implemented a real-time hardware median filter for high-resolution video.

Pre-professional Software/Hardware Engineer

R&D lab for publishers experimenting with generative AI

Authentic and enthusiastic product leader with a proven track record of taking products from idea stage to millions in sales.

Currently in discovery mode, looking for my next adventure in ML/AI. Feeling blessed to be an Entrepreneur-in-Residence at my hometown university, where I can give back to the local startup community.

Previously co-founded a B2B SaaS company, where I envisioned and built a marketing technology product from mock-up to its first million in revenue in less than 12 months. I grew that startup until it was acquired by a strategic buyer, where I led product development for its ABM division.

My success stems from these principles:
* Persistently focus on ambitious goals. 
* Be a doer and inspire others into action by leading from the front. 
* Deplore and trim inefficiency in all things. 
* Communicate persuasively. 
* Prepare for interactions, never "wing it."
* Be calm in turbulent environments.

Rodrigo Fuentes

This Veterans Day, I honor my dad, Vic Simmons. After 21 years with the Army, he gave the rest of his career to civil service!

I push hard for my daughters to remember him. He had a huge impact on my life and outlook. 🥰 

He battled FIERCELY against the cancer that took him. I’ve never met a stronger person.

Generative AI Product Leader | GTM Strategist | Driving Products from Idea to Market

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Take less per account, make more overall. Counterintuitive, but the math holds.

When we ran a four-year-old back book through our AI agent, it didn’t “spike” at first. But it ramped methodically, and, by month-4, the cumulative liquidation curve beat two incumbent agencies on the same placements.

Why? Incentives and constraints.

Human teams sprint the top accounts early. Then staffing (e.g., labor cost, morale, quota incentives, etc.) slow the long tail. The AI doesn’t tire, doesn’t reshuffle priorities, and doesn’t depend on floor energy. It just keeps executing.

Here’s the hidden insight: accepting a slightly lower per-account recovery can increase total liquidation.

If you’re willing to settle at, say, 40% consistently, you may take less per consumer, but you collect from more consumers, and the aggregate goes up. 

Humans resist this because of FTE costs and quota realities. AI doesn’t carry that overhead...

The net result is a win-win-win.  The agency wins more business, the lender recovers more debt, and the consumer has a better negotiated outcome.

AI agents in regulated environments change the unit of control from people to policy.

Most firms have solid UDAAP policies. But things break at the edge... stressed reps, misaligned incentives, and more. That’s why “more training” hits diminishing returns.

We think of AI agents as "compliance-as-code." Our team spent 2000 man-months mapping regulatory requirements so that these rules are machine-executable (consent, mini-Miranda equivalents, prohibited phrases, hardship paths).

That enables "constrained generation." E.g., a known (approved, configurable) set of responses, not freeform LLMs that are prone to hallucinations. 

Then we layer on real-time guardrails. Interruptible turns, refusal modes, and safe escalation to a human when policy confidence drops.

But we don't stop there. Our defense-in-depth approach captures evidence trails. Structured logs (who/what/why) + audio/text transcripts tied to each decision. 

Result: consistent adherence to policy while maintaining a dynamic, empathetic consumer experience.

Most “AI rollouts” are theater. Please^3 create a solid plan to QA and test UDAAP edge cases. Here's how.

You don’t need moonshots to de-risk AI for UDAAP (the laws prohibit unfair, deceptive, or abusive acts or practices by collectors). You need clear pass/fail gates for your AI plan. 

Follow this framework:

+ Days 0–30 (Map & Contain)

1. Select 1–2 use cases (e.g., right-party verification, hardship routing).

2. Convert policies to policy-as-code; define prohibited intents & mandatory disclosures.

3. Spin up sandbox datasets + red-team prompts to try to break the agent.

+ Days 31–60 (Run & Measure)

1. A/B vs. top-quartile human reps on: accuracy of disclosures, adherence to call script, complaint rate, first-contact resolution, average handle time.

2. Live-time guardrails: confidence thresholds force a handoff to humans.

3. Daily QA via structured logs + transcripts; sample failures, fix policy or responses, re-deploy.

+ Days 61–90 (Decide & Expand)

1. Go/No-Go by hard criteria: zero critical UDAAP defects, SLA hit rate, complaint delta ≤ target

2. If Go: expand to adjacent intents and add automated QC for self-audit.

Confused by the path ahead?  Shoot me a DM. I'm happy to educate (not sell) on how to get leadership aligned, compliance comfortable, and ops focused on deployable AI. 

#DebtCollection #AccountsReceivable #ARMindustry

UDAAP isn’t one problem. It's two, and the gap lives between “what leadership intends” and “what agents actually do.” AI helps close the gap. Watch Nguyen Nguyen break it down in this short clip. 

UDAAP risk operates on two layers:
1. Organizational intent: policies, tech, supervision.
2. Agent behavior: incentives, emotion, fatigue.

The ARM industry has done well to mitigate risks in layer 1. But layer 2 risks remain. Fortunately, AI agents will significantly help with that second layer. Here's why:

- No bonus-chasing: policy logic executes the same way at 8am or 8pm.

- Real-time guardrails: contact frequency, consent capture, and immutable logs make “safer-than-human” provable.

- Relentless empathy: AI agents continue to treat the consumer with compassion and care despite challenging scenarios (e.g., cursing, combative, distracting scenarios)

Jeffrey Simendinger - some food for thought for your upcoming webinar with Mike Gibb on AccountsRecovery.net on Nov. 28th!

INFUSE

Vice President of Display Advertising 

Integrate

Integrate Pipeline360 all-in-one solution that brings together content syndication, digital advertising, and account intelligence to build predictable pipeline quickly and efficiently.

Vice President of Digital and SaaS Sales

DraftKings Inc.

DraftKings is a Boston based startup in Daily Fantasy Sports (DFS), Sport Betting and iGaming that went public April 2020.

Director of Advertising Sales

Hearst Television

Digital Adveristing Sales Manager

LocalVox - A Vivial Company

LocalVox was NYC based startup providing local search marketing services to multi-locations businesses acquired by Vivial.

National SaaS Sales Manager

Group Commerce

Group Commerce was a NYC startup providing e-commerce daily deal software as a services (SAAS) platform to publishers was acquired by Nimble Commerce in May 2013

Vice President of SaaS Sales

Boston.com

A Top 10 Regional Newspaper websites in the country with over 200M Page Views and 7M unique visitors a month

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