Brian Benedict

Arcee.ai

Arcee AI: The Small Language Model Company

Arcee AI is revolutionizing the AI landscape with Small Language Models (SLMs) that power cutting-edge applications like Agentic AI Workflows (Orchestra) and Model Routing (Conductor). As the first end-to-end AI model and application solution built within a single system, Arcee delivers unmatched efficiency, security, and flexibility.

Our technology enables cost-effective AI deployment, reducing compute overhead while maximizing performance. With seamless integration, businesses benefit from a fully connected AI ecosystem. Security is a top priority—Arcee can be deployed in VPCs, on-premises, or in the cloud, ensuring compliance and data protection. Designed for both technical and non-technical users, our solution makes advanced AI accessible to all.

At Arcee, we’re redefining the future of AI by making powerful, scalable, and secure small-model technology available to everyone.

Highlights so far
✅ Built & Led High-Performance GTM & CS Teams – Scaled a team of 10+ across AEs, BDRs, SEs, CS, and Marketing to drive exponential growth.

Revenue Growth & Market Impact
✅ $0 to $3M+ Organic Revenue in Under 1.5 Years – Spearheaded a hyper-growth strategy that delivered rapid revenue expansion.
✅ 290% QoQ Revenue Growth – Transitioned from founder-led to sales-led motion, achieving nearly 3x revenue growth quarter-over-quarter.

Fundraising & Strategic Partnerships
✅ Secured Nearly $50M in VC Funding – Partnered with top-tier investors across multiple funding rounds (Pre-Seed, Seed, Series A, and Series A+).
✅ Built a Scalable Sales Engine – Designed and executed a repeatable, scalable GTM playbook for sustained growth.

Co-Founder

Various Startups

Working as an advisor to numerous start-ups across technology, software, with a specific focus on sales execution, sales management, sales scale-up and team building. 

Highlights include 
Picked by Ben Horowitz of A16Z to Lead Sisu out of Pre-Revenue to $1m+ ARR in under 1 year and help secure 55m B Round led by NEA, A16Z.  Built from scratch ICP, sales collateral, pricing, strategy, team build. 

Brought Narrator from 200k to 1.2m in under 9 months. Building all sales strategy, decks, execution, POC plans and pricing models. 

Companies include: 
Sisu, Clearfind, Maya.ai, VinSocial, CitiBeats, Activeloop, Anecdote, Kawa, Narrator, Luca, Agency, Stanza, Typedef and others

Sales Advisor / Investor

Hugging Face

Set up Hugging Faces repeatable sales motion, hired and trained AEs, Bdr, SEs and grew revenue 215% QoQ.  Helped Hugging Face with landing a 2B valuation on a 100m C round.

Head of Global Sales (First Sales Leader)

Tecton

More than doubled the customer count in 1st year and increased revenue 245% YoY. 
Established a PLG strategy that decreased time to conversion from 90+ days to under 45 days. 
Implemented a selling process that was unique with no sales rep 1st touch demonstrations and light touch 1 week POCs that moved conversion rates from >10% to 65%+
Grew and trained sales reps, SDRs, and executed on plan to scale sales including all sales training materials, role-playing, decks, sales hiring scorecards, and sales methodology
Brought in first multi-million dollar agreement and helped to land first deals in multiple categories
Trained and hired a team of 3 reps, 2 SEs, 1 SDR.

Pentaho from Hitachi Vantara

In 2016 as an IC - Grew a dormant territory in the Northeast from under $500K to over $5mm in 9 months on a full annualized quota of over $2mm highlighted by vertical work in Banking and Media & Entertainment.
Expanded presence at major banks and brought in new banking client logos in less then 6 months. 
For 2016 Ranked #1 in US Markets over 40 reps. (Percentage to Goal 205%). #3 Globally.

In 2017 ranked #1 in the US Markets over 40 reps (percentage to goal 212%) #1 Globally.

Presidents Club qualifier 2016, 2017, 2018, 2019

2018 Promoted to Regional Director in charge of all Financial Services Globally. Head count of 20+ including Global Account Managers, Solution Engineers, SDRs.

Implemented first alternative investment strategy for banks looking to capture a self service analytics use case with structured and unstructured data for robo-advisory. 

Grew FS Pentaho Revenue from 115m to over 250m in three year period. Partnered and worked with brands including Citi, BNY Mellon, Comcast, Blackrock, JPMC, Bank of America, Credit Suisse, State Street.

Global Sales Director - Financial Services 

Quantifind

As the second individual contributor sold individually $7.5m on a $2.5m quota Logos included Comcast, Taco Bell, Citi, Moet-Hennessy, Pernod-Riccard,  Ciroc, Wells Fargo, Dunkin' Brands, Tim Horton's, L'Oreal, Verizon and others.

Vice President  - East

NetBase Quid

Led the sales team in brand sales two years in a row generating over 2mm/year in new revenue and promoted to manage the East team of five outside reps. 

Logos include Credit Suisse, Lowe's, Viacom, AMC Networks, A&E Networks, Aereo, ABC, Disney, Target, Kenneth Cole, Time, Inc., Ralph Lauren, IFF, Tory Burch, and others

Vice President - East

inmoment

Senior Sales Director

Forrester

Senior Account Executive

Provident Corporate Finance

Associate

Experienced and highly accomplished sales professional working in early stage start-ups to help grow revenue and build a repeatable sales process. My passion is selling disruptive solutions that bring real value to organizations.  My abilities span building sales and constructing the right go to market approach, collaborating and leading positive culture growth, and generating repeatable revenue quickly. My background blends a deep knowledge of selling to both business, analytics, data science, and IT professionals coupled with solid analytical and team management skills. Proven track record of over-achievement, generating new business organically through thoughtful interactions while cultivating new relationships with key decision-makers.

Arcee's mission is to help enterprises build SLLMs (Specialized Large Language Models) that are more accurate with your own data, deployed in your own environment and with a domain adaption system that allows for control and security over the entire end to end process. Arcee is building context adaptive LLM's through our domain adapted language model system (DALM) first ever RAG solution training the retriever and fine tuning the generator.  

DALM encompasses in-domain pretrained LLM's, fine tuned retriever models and tuned generator LLMs - all in one system. DALM allows you to get LLM's into production fast while creating a trustworthy and factual system. Specifically DALM mitigates hallucinations, inaccuracies, the constant need to retrain your model, while enabling dynamic data sources, having a clear indication of the source, and reduces infra and latency challenges.

To learn more ---> www.arcee.ai

A big thank you to NYSE for interviewing me for the Mixture of Experts AI Series.  I always enjoy my conversations with Gemma Allen about all things AI and how upskilling the employees of today needs to happen and what is in store for the future of Eliza

NYSE, theCUBE SiliconANGLE Media, #NYSE Wired, Brian J. Baumann, John Furrier, David Vellante, and SiliconANGLE & theCUBE

https://lnkd.in/eZMPf6xD

AI Transformation | Founder & Builder | Practical AI And Training I X-Hugging Face I 2X Founder

Open video: Brian Benedict, Eliza | theCUBE + NYSE Wired: Mixture of Experts on youtube.com

Brian Benedict, Eliza | theCUBE + NYSE Wired: Mixture of Experts

Roughly 1 in 4 college students drop out after their first year. 

Not because the courses are too hard, but because the transition is. Most students aren’t given the tools to succeed in a new environment — to adapt, learn, and compete at a higher level. 

The Dropout Problem Isn’t About College...It’s About Transition

Sound familiar? It should. The same thing is happening right now with AI adoption inside organizations.

We’re watching companies quietly decide who they think can’t be upskilled — and individuals opting out because they don’t feel equipped to keep up. The problem isn’t capability, it’s support. Transitioning is hard. And most companies aren’t helping their people make it with confidence.

That’s exactly why we started Eliza...to help organizations and employees re-skill and up-level their AI capabilities so they can compete and thrive in this new world.

As Einstein said, “Once you stop learning, you start dying.”

p.s. Shoutout to Merrimack College and my most recent visit to my alma matter

Start Ups - Stop selling Enterprise AI solutions like they are SaaS. 

AI is not SaaS, the solutions need customizing, tailoring, and there is no one size fits all.

Forward Deployed Engineers are being hired in mass to bridge this gap.  But still some AI vendors don't get it, they want to applaud their initial usage and sign-up numbers but are spending little on tailoring solutions/enablement/training which is the foundation for renewals and scale. 

My prediciton: NRR will be king again and it will be driven by FDEs.

I’m happy to share that I’m starting a new position as Co-Founder at Eliza!

Consulting as we know is changing...

There is a perception whether its true or not most people are inherently skeptical about consultants.Old school consultants charge hourly. Come up with PowerPoints and write essays on strategy. Leaving a ton more questions...

When Stephen Garden approached me on starting Eliza we wanted to package ourselves in an entirely different direction as a firm.

🤼 The team - doers/practitioners/builders - that’s our ethos, we hire doers who have built ai systems and understand the nuance of going from prototype to production not vibe coders or old school software engineers who never worked on Ai 

🫰 Outcome based - be outcome driven with defined time lines that hold us accountable and take on work that only has an outcome based structure 

🚃 Train - the people at the company are often forgotten, We. Train. them. It’s part of what we do, it’s not negotiable. You can’t build a system for AI and leave no one to man the ship. 

We are building a vastly different company I like to call Eliza an AI Execution Firm but my marketing team thinks that wording won’t fly.  

I guess what would you call it?

My favorite day at school was Friday's growing up and not because the weekend was almost here.  Friday's was "show and tell" day.  Each kid had to bring something in that they were passionate about one kid brought a receipe book, one kid brought her pet turtle (yes she got in trouble for this), I remember bringing Marshmellow Fluff (yup that was my jam) and we had to explain it to the class.  

Looking back it was literally the kids version of "demo day" and it must be why I love showing up with clients and prospects when I have something exciting to show/discuss.

At Eliza we show up on every call with being thoughtful.  We bring use cases, specific to the business we are speaking to, that we know will work.  

We have built some of the most comprehensive agents, solutions, apps, and workflows to allow us into some critical paths in a business to understand what will work quickly and what might take time.

The feedback is usually suprise, they can't believe the accuracy of what we develop before they ever give us a dollar. How we net out paths for them upfront and how we lean in prior to them even knowing us. 

We do this because our mantra is practical AI.  We are not here to wow you with presentations but build and deliver.  

The big question you need to ask yourself, are your partners coming in to ask you more questions or showing up with answers...

McKinsey & Company just admitted the quiet part out loud: the traditional consulting business model is collapsing with AI as the accelerant. And rivals are scrambling to keep pace.

Business Insider reports that 25% of McKinsey & Company’s global fees now come from outcomes-based pricing, not billable hours. That’s a direct challenge to the old “scope + time + team” playbook that Deloitte, Boston Consulting Group (BCG), and EY still rely on.

What’s changing:
→  Strategy work is now <20% of McKinsey’s revenue. The rest is deep, multi-year AI and transformation execution.

→  Clients no longer ask for a project; they ask for a result with McKinsey paid only if it delivers.

→  EY is openly considering a service-as-a-software model powered by AI agents.

→  Multi-year, cross-functional transformations are pushing firms to behave more like long-term operating partners than advisory vendors.

The stakes? Tens of billions in consulting revenue are tied to models that AI has just made obsolete. Whoever adapts fastest wins the C-suite wallet.

This shift already determines which firms win multi-year AI modernization deals, and which are sidelined. 
But here’s the unanswered question: if outcomes drive fees, who owns the risk when AI underperforms or fails to scale?

Is this the beginning of the end for time-and-materials consulting. Or will the incumbents find a way to protect the old model?

 #AI #Consulting #DigitalTransformation #AI #Strategy

MER vs HER 

No this is not a pronoun post. This is a metric one.  We are seeing extraordinary high bars from clients looking to implement AI.  One clear indicator of a first timer putting something in production is Machine Error Rate vs Human Error Rate. If they never heard of it - its time they learn. 

This is a metric that guides our builds Eliza.  In short. if you are looking for 0% machine error rates - meaning the workflow/agent/model you built will perform falwlessly everytime then you don't understand why AI is so popular. 

The benchmark is human error rate - if your AI can consistently beat the HER   then the cost, long term gains and ability to outperform your current rate of return 24/7/365 then that is a no brainer. 

Take the win - stop striving for perfection. We are not ready for 0% Machine Error Rate....

"The left side of my brain wants the quick win but the right side wants to be thoughtful." This is what a senior executive said to me when describing AI for 2026.

Executives are still debating it. The data isn’t.

Quick wins deliver speed
 • 60 percent of enterprises already see or expect AI ROI within twelve months
 • Teams using AI dev tools cut cycle times by more than thirty percent
 • Eighty eight percent of companies now apply AI in at least one function

The upside is momentum and political capital. The downside is sprawl. Only thirty nine percent translate these wins into enterprise level impact because the foundations are weak.

AI Infrastructure delivers scale
 • Nearly sixty percent of enterprises report under fifty percent ROI when data and governance are fragmented
 • By 2026 most G2000 CIOs will tie AI budgets to infrastructure readiness not pilots
 • Organizations that consistently operationalize AI share one trait reusable platforms and shared pipelines
The upside is compounding value. The downside is slower proof and higher early cost.

Where the market has already moved
 The leaders run a dual track...Quick wins for visible progress...Infrastructure for durable advantage

Who is doing both? Who is fixed on just one? Curious to get others take...

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