{"id":13520,"date":"2026-03-24T16:07:56","date_gmt":"2026-03-24T16:07:56","guid":{"rendered":"https:\/\/cheesecakelabs.com\/blog\/"},"modified":"2026-03-24T16:07:58","modified_gmt":"2026-03-24T16:07:58","slug":"cheesecake-labs-first-place-as-ai-consulting-company","status":"publish","type":"post","link":"https:\/\/cheesecakelabs.com\/blog\/cheesecake-labs-first-place-as-ai-consulting-company\/","title":{"rendered":"From Strategy to Production: Backed by the #1 Applied AI Consulting Company on Clutch"},"content":{"rendered":"\n

Most AI consulting firms deliver strategy. A polished deck. A roadmap. A set of recommendations. And then they leave.<\/p>\n\n\n\n

The gap between strategy and execution is where most AI initiatives die. According to Gartner, over 80% of AI projects never make it to production. Not because the strategy was wrong, but because nobody stayed to build it.<\/p>\n\n\n\n

Cheesecake Labs has been ranked the #1 Applied AI Consulting Company worldwide on Clutch<\/strong>, not for strategy decks, but for shipping production AI systems that enterprises actually use. This article explains what that looks like in practice, through the real projects and engineering decisions that earned that ranking.<\/p>\n\n\n\n

The execution problem CIOs are facing<\/h2>\n\n\n\n

CIOs today are under pressure to make their organizations AI-ready, but the reality on the ground is complex. Legacy infrastructure, disconnected data platforms, and outdated architectures make it difficult to integrate modern AI capabilities, especially large language models, into systems that were designed decades before LLMs existed.<\/p>\n\n\n\n

Defining an AI strategy is relatively straightforward. Making it work in production is not.<\/strong><\/p>\n\n\n\n

AI initiatives require more than selecting a model. They depend on data readiness, system interoperability, security and compliance frameworks, and scalable cloud infrastructure. Without these elements aligned, AI remains stuck in proof-of-concept stages: impressive in demos, invisible in business outcomes.<\/p>\n\n\n\n

This is where the choice of an applied AI consulting company<\/a> becomes critical. The question isn’t “do they understand AI?” It’s “can they build it, integrate it with our existing systems, and keep it running in production?”<\/p>\n\n\n\n

What sets an applied AI consulting company apart<\/h2>\n\n\n\n

Cheesecake Labs’ recognition as the top-ranked applied AI consulting company<\/strong> on Clutch is grounded in verified client feedback across complex, real-world projects. The Clutch Leaders Matrix evaluates companies on their ability to deliver, measured through authenticated client reviews, project scope, and market presence, not marketing spend or self-reported claims.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

What the ranking reflects is a consistent pattern: companies come to Cheesecake Labs not for theoretical guidance, but for end-to-end AI implementation. That means architecture design, data engineering, model integration, frontend and backend development, compliance engineering, and production deployment, all handled by the same team in continuous collaboration with the client.<\/p>\n\n\n\n

With offices in San Francisco and Florianopolis, the team operates across overlapping time zones with senior engineers who embed directly into client teams. This is not staff augmentation<\/a>. It is a full engineering partnership where Cheesecake Labs takes ownership of outcomes, not just deliverables.<\/p>\n\n\n\n

Real outcomes: Knapsack, building a production AI platform for regulated industries<\/h2>\n\n\n\n

When the CPO of Knapsack<\/a>, a privacy-first AI platform for financial services, healthcare, and legal professionals, came to Cheesecake Labs, the goal was not to define an AI strategy. It was to build a production-ready platform that could serve enterprise clients in industries where data security is non-negotiable.<\/p>\n\n\n\n

The technical challenge was significant. Knapsack’s early prototype ran AI entirely on local devices to keep sensitive data protected. But this architecture couldn’t scale. The team needed to re-architect from a fully local AI model to a hybrid, cloud-capable system without compromising the privacy-first design that regulated clients demanded.<\/p>\n\n\n\n

Cheesecake Labs embedded senior engineers directly into the Knapsack team. The work included designing a hybrid AI architecture where data is processed locally or in secure cloud environments depending on compliance requirements, building LLM integrations <\/strong>with vector databases for contextual and privacy-preserving AI chat, engineering the platform to meet SOC 2 Type 2, HIPAA, and GDPR compliance from day one, and integrating with enterprise systems including Salesforce, HubSpot, GSuite, Outlook, and financial platforms like Wealthbox and eMoney.<\/p>\n\n\n\n

The result: <\/strong>Knapsack evolved from a single-purpose notetaking tool into a full AI workflow platform with three product lines. Knaps for modular workflow automations, Knapsack Chat as an embeddable AI SDK for SaaS companies, and Knapsack Studio as a workflow builder for custom AI pipelines. The platform successfully launched at app.knapsack.ai and began onboarding enterprise clients in financial services and healthcare.<\/p>\n\n\n\n

\n

“We couldn’t have solved some of the complex problems to deliver this product without the support of Cheesecake Labs’ team.”<\/em><\/p>\n\n\n\n

<\/p>\n\n\n\n

Mark Heynen – Co-Founder & Chief Product Officer, Knapsack<\/strong><\/p>\n<\/blockquote>\n\n\n\n

In a verified Clutch review<\/a>, Mark Reynen, Knapsack’s CPO, highlighted that the team consistently delivered on sprint commitments and communicated proactively. He noted that Cheesecake Labs’ engineers take ownership of problems, bring ideas to the table, and have pushed the company to make better architectural decisions. In his words, they operate as a true engineering partner rather than a vendor. That kind of engagement is what moves AI from concept to market.<\/p>\n\n\n\n

Technologies:<\/strong> Python, TypeScript, React, Next.js, Node.js, LLM integration, vector databases, AWS, Zoom\/Meet\/Teams APIs, SOC 2\/HIPAA\/GDPR compliance frameworks.<\/p>\n\n\n\n

Real outcomes: Wedgewood, turning legacy infrastructure into a scalable platform<\/h2>\n\n\n\n

Not every AI and modernization challenge starts with a greenfield product. Wedgewood<\/a>, one of the oldest and largest compounded animal medication providers in the United States, serving over 70,000 veterinarians and millions of pets annually, came to Cheesecake Labs with a different problem: their technology had been outgrown by their business.<\/strong><\/p>\n\n\n\n

Years of organic growth and acquisitions, including Blue Rabbit, an online veterinary pharmacy, had left Wedgewood with a website built on a patchwork of legacy technologies. Content changes required developer involvement. The architecture couldn’t incorporate content from newly acquired businesses. Maintenance costs were climbing while the platform’s ability to adapt was shrinking.<\/p>\n\n\n\n

Cheesecake Labs executed a full application modernization<\/a> strategy. The team migrated Wedgewood’s content infrastructure to Contentstack, a modern headless CMS, and built 30+ modular content components for dynamic page creation.<\/p>\n\n\n\n

The frontend was rebuilt with React and Next.js for server-side rendering, dramatically improving page speed and SEO. Critical legacy systems, including Klevu for search and Salesforce for inventory and CRM, were integrated to preserve business functionality while modernizing the entire presentation layer.<\/p>\n\n\n\n

\"23%<\/figure>\n\n\n\n
\n

“Improving website performance and streamlining content management has significantly simplified our lead generation efforts.”<\/em><\/p>\n\n\n\n

<\/p>\n\n\n\n

– Theresa Malaspina, Digital Marketing Coordinator, Wedgewood<\/strong><\/p>\n<\/blockquote>\n\n\n\n

Technologies:<\/strong> React, Next.js, Contentstack (headless CMS), Klevu, Salesforce, Node.js, cloud hosting (Contentstack Launch), SEO optimization.<\/p>\n\n\n\n

Recognition that reflects real delivery<\/h2>\n\n\n\n

Being ranked the #1 applied AI consulting company on Clutch globally reinforces a simple point: execution matters more than credentials.<\/p>\n\n\n\n

\n

“Creating a slide deck telling others what to do is easy. Applying AI in production, for real use cases where customers are still learning to use the technology and the technology itself is evolving every week, that’s hard. That’s engineering. And I’m incredibly proud of the results our team has brought to our clients.”<\/em><\/p>\n\n\n\n

<\/p>\n\n\n\n

Marcello Gracietti, CEO, Cheesecake Labs<\/strong><\/p>\n<\/blockquote>\n\n\n\n

This recognition reflects consistent delivery across 300+ projects<\/strong>, spanning industries from fintech and healthcare to logistics and SaaS. It validates a model that prioritizes working systems over theoretical frameworks, and production-grade AI over proof-of-concept demos that never leave the staging environment.<\/p>\n\n\n\n

What to look for in an applied AI consulting company<\/h2>\n\n\n\n

For CIOs evaluating AI partners, the Knapsack and Wedgewood projects illustrate what separates an effective applied AI consulting company<\/strong> from one that only delivers recommendations.<\/p>\n\n\n\n

The partner should build, not just advise. If the engagement ends at a strategy document, you’ll need a second vendor to implement, doubling costs, timelines, and communication overhead. Look for teams that handle architecture, engineering, compliance, and deployment under one roof.<\/p>\n\n\n\n

They should have proven compliance experience. Any AI work touching healthcare, financial services, or regulated industries requires SOC 2, HIPAA, or GDPR expertise baked into the engineering process, not bolted on after launch.<\/p>\n\n\n\n

They should integrate with your existing systems. Enterprise AI doesn’t exist in a vacuum. The real complexity is in connecting LLMs and data pipelines with the Salesforce instances, legacy databases, and internal tools your organization already runs on.<\/p>\n\n\n\n

And they should show you measurable results from real clients. Verified reviews, named case studies, and quantified outcomes, not anonymized references and vague claims.<\/p>\n\n\n\n

From AI ambition to production<\/h2>\n\n\n\n

For organizations today, the challenge is no longer understanding AI’s potential. It is making AI work, and that requires moving beyond strategy into execution, beyond planning into production, and beyond vendor relationships into true engineering partnerships.<\/p>\n\n\n\n

If your organization is moving from AI ambition to real implementation, Cheesecake Labs partners with you across the full journey, from AI strategy and architecture design<\/a> to engineering, system integration, and long-term scalability.<\/p>\n\n\n\n

Schedule an AI readiness assessment<\/a><\/strong> to start building production-grade AI systems that deliver measurable business impact.<\/p>\n\n\n\n