Velocity AI vs. Prolego: Full-Stack Enterprise AI vs. LLM Architecture Expertise

Velocity AI vs. Prolego is not a conventional competitor comparison. Prolego is a three-person firm led by one of the most credentialed AI practitioners in the boutique consulting space — Kevin Dewalt, who built his first neural network at Stanford in 1995, has 30 years of AI experience, and has published one of the most respected open-source LLM engineering frameworks available. The question is not which firm is "better" but what each is built to do.

This post is written by Velocity AI. We have an obvious interest in how it reads. We have written it honestly — including a frank account of Prolego's Fortune 1000 client associations and why Kevin Dewalt's technical credibility is a genuine differentiator for specific types of AI problems.

At a Glance

What Prolego Is Actually Built to Do

Prolego's positioning is specific and honest about it: they help Fortune 1000 companies get their LLM and GenAI projects unstuck. This is a real problem in the enterprise market. Many organizations have launched AI initiatives — built early prototypes, invested in proof-of-concepts, or acquired AI tooling — and found themselves stalled. The models hallucinate in production. The architecture doesn't scale. The evaluation framework can't measure whether the system is actually working.

Prolego's Performance-Driven Development methodology is the most directly applicable publicly available framework for solving exactly these problems. PDD provides a systematic approach to building reliable LLM systems — not just getting a model to work in a demo, but making it production-hardened, evaluatable, and maintainable. The fact that Prolego published PDD openly on GitHub rather than treating it as proprietary IP reflects a thought leadership posture that technical buyers recognize and respect.

Kevin Dewalt's 30-year AI background means he has built and diagnosed AI systems through every major technology cycle. He is not a generative AI consultant who pivoted from another field in 2022. He is an AI practitioner who built neural networks in the 1990s, invested in AI companies as a fund manager, and has decades of accumulated pattern recognition about why AI projects fail and how to fix them.

For a narrow, specific problem — LLM architecture design, GenAI project diagnosis, building a reliable evaluation framework — Prolego's expertise is difficult to match.

The Capacity Constraint Is Material

Prolego's three-person team is not a temporary state — it appears to reflect a deliberate choice to remain a principal-led, high-expertise boutique. This works well for the type of engagements Prolego pursues: advisory, architecture, and diagnostic work that scales with expert judgment rather than team headcount.

It does not work well for large-scale enterprise AI production programs. A Fortune 500 company running a multi-workstream AI initiative — concurrent data infrastructure work, model development, system integration, governance documentation, and production deployment — cannot be delivered by three people on a reasonable timeline. The capacity simply is not there.

Velocity AI's 100+ practitioner network is structured for exactly this scale. We can staff programs appropriately — not by stretching a three-person team across multiple simultaneous clients, but by allocating the right specialists to your program and maintaining that staffing through production go-live.

Prolego's Fortune 1000 Client Roster

The honest account of Prolego's differentiation includes their client associations — and they are notable for a three-person firm. Prolego's public profile is associated with organizations including Morgan Stanley, Citibank, FINRA, Transamerica, Cox Enterprises, Raytheon, Bosch, and Lockheed Martin.

This is an exceptional list. Defense contractors and major financial institutions are not casual clients. They reflect the trust that Kevin Dewalt's deep technical credibility and 30-year track record generates with enterprise buyers who prioritize expert judgment over delivery infrastructure.

The constraint is what each of these relationships likely involved. At three people, Prolego's engagements with these organizations are almost certainly advisory, architecture, and diagnostic — not large-scale production deployment programs. A Raytheon AI architecture review conducted by Kevin Dewalt is a very different engagement from a multi-month production ML deployment. Both are legitimate. They are not the same type of work.

Where Prolego Genuinely Excels

We believe in honest advice. Prolego is the right choice in specific scenarios:

LLM architecture design and project rescue. If you have a GenAI project that is stuck — hallucinating in production, failing evaluation, or architecturally unsound — Kevin Dewalt and Prolego's PDD methodology are among the most credible available resources for diagnosing and fixing the problem. For this specific use case, Prolego's expertise is difficult to match.

Highly technical LLM advisory for Fortune 1000 buyers. If your buying committee includes senior AI engineers who evaluate consulting partners on technical depth rather than delivery process, Prolego's published methodology and Kevin Dewalt's track record create a peer-level credibility that institutional delivery firms do not always match.

Expert advisory without full delivery scope. If you need expert LLM architecture input but have an internal team to execute the build, Prolego's advisory model allows you to access Kevin Dewalt's expertise without paying for a full delivery team you do not need.

How to Decide

The decision between Prolego and Velocity AI is fundamentally a question of what type of problem you are solving.

If your problem is "we have a stalled or broken LLM project and need expert diagnosis and architecture re-design," Prolego is among the best available resources. Kevin Dewalt's expertise and the PDD methodology are precisely calibrated to that problem.

If your problem is "we need production AI built and deployed on a specific use case, starting from data infrastructure and ending with a live system," Velocity AI is built for that. Our delivery model, team size, and production focus are structured for programs that require more than three people and more than architecture advice.

If you are uncertain which applies, the question to ask is: do you need an expert to fix or design something, or do you need a team to build and ship it? The answer to that question points clearly to the right firm.

We are willing to give you an honest assessment of whether your initiative matches our strengths. If it doesn't, we will say so.