AI Executive · Founder · Los Angeles
Your expert partner for enterprise-grade AI solutions and agentic applications — from first roadmap to running system.
"If you can dream it,
I can build it."
Mackenzie's story
Mackenzie Rae Knapp is the Founder & CEO of LifePrint — private health intelligence for the GLP-1 and longevity generation — and one of 35 Partners across Credera, a 3,000-person global management consulting firm.
Credera is an Omnicom company — NYSE: OMC, the world's largest marketing and sales group at more than $25B in annual revenue — where Mackenzie sits in the US Office of the CTO and leads AI go-to-market globally, accountable for an eight-figure portfolio of tracked AI revenue across the US, UK, and APAC.
Building AI since before the generative-AI wave, she has repeatedly beaten BCG, Accenture, and McKinsey to win and ship Fortune 50 agentic-AI programs — from Eli Lilly's Innovation Engine to the AT&T work its leadership called "the most important at AT&T this year." Earlier she led data products at Cart.com through its ~$400M cumulative-funding era, and ran BI & analytics at DuMont Project through its acquisition. She architects the multi-agent orchestration on AWS Bedrock behind Omnicom's Agentic Marketing Operating Model.
RAE Analytics is her independent practice: the same enterprise rigor, applied directly. Off the clock she grew Women in Tech into a firm-wide initiative, co-created the Rapid Prototyping Playbook, advises health-tech startups on AI, and champions LGBTQ+ initiatives. Finance at BYU, data science at UCLA, fluent Spanish from years in Chile — snowboarder, surfer, and boater, based in Los Angeles.
"I show rather than tell — I prototype live what others only pitch, with humans always in the lead."
Speaking
Opening keynote at Iowa State University's Ivy College of Business: a tested playbook for AI that adapts to its users, with the guardrails to prove it. The failure patterns worth learning from before betting on production — and the AI Operating System they produced, governed tightly enough that an enterprise can actually run it.
From experimentation to enterprise scale: what it takes to operationalize AI — workflows, data, creative process, and org design — with a client-zero approach, without losing creativity.
Data foundations for real-time personalisation: cutting through the AI hype to show what an agent actually is, and how to beat data silos and privacy barriers with confidence.
How AI and machine learning drive growth and decision-making in retail — the highest-viewed session of the series, on the unified-analytics product that led to an acquisition.
Connected data plus machine learning: actionable insight that lifts margins, ROAS, and inventory efficiency — delivered mainstage at Adweek's Brandweek.
Guest speaker on the new digital landscape in a privacy-first ecosystem, and rethinking first-party data capture strategies.
Every business can — and should — A/B test, but testing badly is expensive. With optimization specialist Seth Hutchings: the seven most common testing mistakes and how to avoid paying for them.
Inventions
First-of-their-kind products and platforms — invented, shipped, and in some cases sold as subscriptions.
Credera's first multi-agent orchestration platform, productized and marketed as the AI Operating Model: the system that houses all agentic activity.
The first agentic application ever built inside Omnicom — a market first, shipped for Eli Lilly.
Credera's first chatbot and retrieval system with a SQL agent — plus the training program that turned 76+ developers into AI experts shipping 1.5 new features every sprint.
Multi-touch attribution AI products — part of the data & AI portfolio behind ~$400M in cumulative funding.
A novel approach to unified analytics, built on a deep Google Cloud partnership — the product that led to the firm's acquisition.
A model detected a competitor scraping our client's pricing pages — so I built the countermeasure: serve intentionally higher prices to the competitor's IPs, and let them price themselves under our client.
Turned my validation methodology into a product ObservePoint sells — a $3,000-a-month subscription that audits live analytics implementations against the solution design reference.
Companies I've consulted for or worked with
Hover any logo to see the work behind it — analytics implementations, predictive models, and production AI across a decade of enterprise engagements.
Engagements by industry
26 brands across the portfolio — hover a bar for the roster
Track record
Revenue owned, competitors displaced, organizations led, categories created — the short version.
Accountable for the firm's global AI go-to-market across the US, UK & APAC — offerings, pursuit engine, and the AWS AI partnership.
Consent-first private health intelligence for the GLP-1 and longevity generation — people own their telemetry, and choose how it contributes to research.
Won the CMO's AI workshop; overnight prototypes moved 50 executives — "the most important work at AT&T this year."
Two validated product concepts in three weeks — vs. a top-tier firm's three months. Demonstrated to Omnicom's CEO; featured at AWS re:Invent.
Pioneered Unified Analytics, the flagship data product central to Cart.com's growth through ~$400M in cumulative funding — directing data products across 65+ professionals.
Multi-agent orchestration on AWS Bedrock AgentCore — an active engagement.
Ran BI & analytics behind $80M+ in annual media spend; a 1.2B-row governance overhaul cut data errors from 25% to 0.5% — through the firm's acquisition.
Presented a 30M-customer, 70M-transaction analysis to the CEO of a leading international luxury brand — reshaping cross-brand discounting strategy.
What I offer
Twelve ways to engage — most clients start with strategy or a focused build, then grow from there. Looking for training and enablement? That's below, with rates.
Roadmaps that prioritize use cases, establish governance, and sequence delivery — so AI adoption compounds advantage while minimizing risk.
From problem discovery to deployment: co-created, enterprise-grade AI products built for measurable ROI.
Self-governing agent networks that handle complex workflows with minimal supervision — and learn with each use.
Retrieval-augmented generation grounded in your own document libraries — accurate, contextual, and sourced.
Assistants that answer, qualify leads, and support customers 24/7 — matched to your brand voice and integrated with your systems.
Rule-based automation for repetitive work — fewer errors, faster cycles, and time back for the team.
Tag management and implementation that tie every effort together — know who visits, how they land, and what works.
Is your content effective? Let your data answer that — and the harder questions about customer behavior behind it.
Create, run, and monitor campaigns targeted on real user behavior — not guesswork.
SEO & SEM that make sure customers find you at the moment they're looking.
A social strategy designed to connect with the customers you actually want — then prove it in the numbers.
A custom site tailored to your industry and designed for your customers — like this one.
Training & enablement
I get asked to teach AI more than anything else on this page — by people who've never opened a model, and by people who use one every day and can feel a ceiling. Two different rooms, so two tracks. Same formats, same rate for either one.
Zero to shipping something. For people who haven't made AI part of the job yet — curious, a little skeptical, unsure where it fits.
Getting the most out of Claude
CLAUDE.md file — why giving it context is most of the jobThen building something real
For daily users who've hit the ceiling of the chat box and want the compounding version — systems, not sessions.
Systems instead of sessions
Knowing it's actually working
Where the number comes from
Every figure above is the same rate — $500 for an hour of real work, which is roughly what partner-level advisory carries at a major consultancy. A half day in the room is about twelve hours once you count building the material around your tools; a full day is about twenty-four. That's the whole rate card, and it's why training is quoted per session rather than per hour in the room. Priced by the hour in the room, the preparation disappears — and the preparation is what makes the session worth attending.
General market reference points for comparable AI enablement work in the US in 2026, not a quotation. Travel, audience size, and how custom the material needs to be all move the number.
Thinking about a session? Tell me who's in the room and what they should be able to do when they leave.
Scope a sessionContact
Reach out for a consultation. Bring your hardest questions — challenge me.