In 2024, Accenture reported over $1 billion in Generative AI (GenAI) pilots and projects. This impressive figure, however, highlights a critical distinction: Pilots are a first step in the right direction. Still, they are a far cry from actual deployments or rollouts. Our takeaway from the report is that scaling AI continues to be a topic that many organizations are still grappling with.
In the pilot phase, the nuances of AI model choice – such as their token consumption or the specialized tooling they use to reduce query volume – seem less critical. The financial stakes simply aren’t high enough. The same logic applies to privacy at scale; if a project only involves basic company files, the private handling of vectors at scale is not yet a pressing issue. Why? Because test data is rarely as sensitive as your sensitive data you operate on daily.
This leads to a familiar picture: Time and again, we’ve seen organizations mistake pilot success for deployment readiness, only to be blindsided by the complexities of scaling generative AI.
Of course, AI at scale matters. Who wants a RAG solution that’s limited to HR policy documents? No one. The true power of AI lies in its ability to access and understand data across all enterprise systems, while respecting user identities and access rights. That's the essence of scalable AI.
But here’s the thing: Many organizations have initiated AI projects by tossing three engineers into a room for six months to build an internal RAG to “show the board we can do AI.” While they often succeed in impressing the board, the real test comes when their solutions are rolled out enterprise-wide, handling real data and adhering to stringent governance requirements. All too often, it leads to a dead end, as legal and finance departments, for example, demand tight control over access to sensitive information.
This is where the Al scaling problem becomes very real.
At Squirro, we’re not just talking about AI; we’ve already been deploying it at scale for years, partnering with leading organizations to achieve real-world results. Consider these achievements in AI scale:
Granted, with some resources, most engineers could build a RAG for 100 users across thousands of documents fairly easily. But, the real challenge – and where Squirro truly shines – lies in managing privacy, i.e. a company’s data governance rules, and optimizing costs at this scale.
Scaling laws become critical here. When you're dealing with thousands of users and millions of documents, complexity explodes, and managing LLM bills becomes a serious concern. This complexity is why there are so many enterprise AI tools available today. (Find out what sets us apart from many of these SaaS GenAI vendors.) Indexing large datasets is one thing; doing it within hardware constraints while ensuring privacy for large-scale operations is another.
Without access control lists (ACLs), performance might be acceptable. But when you introduce ACLs into the vectorized space, latency spikes. This is due to the exponentially more complex computing power required. Vectorized indices are often massive, and for fast retrieval, vectors are grouped by similarity. But, as we’ve discussed before, ACLs are orthogonal to this setup, making computation across different layers exponentially more complex. This is a core aspect of the AI scaling hypothesis and one of the key scaling laws in AI.
And this is precisely where Squirro excels. Our expertise is built on deep experience with key customers, including regulators and government entities. According to our latest research, we achieve 20% greater accuracy at half the price point of other leading solutions, a significant benefit for those managing privacy at scale for 10,000+ users.
Privacy at scale matters as organizations deploy generative AI across their operations. At Squirro, we have the expertise and proven track record to navigate this landscape. We’ve earned the trust of regulators, governments, Tier 1 banks, and some of the largest organizations worldwide.
For those looking to deliver AI scalability while adhering to governance layers, we are here to discuss the journey we’ve undertaken. We understand the intricacies of large language models and the importance of energy infrastructure to support data computing at this scale. Reach out to learn more, and let's scale your enterprise AI ambitions together.