Large Language Models have captured the imagination of techies, businesses, and the general public alike. Their potential to automate tasks, understand complex context, and generate creative content is unparalleled. Yet, as more organizations move from shiny demos to real-world deployments, a harsh truth emerges: Shipping a reliable LLM application is fundamentally different from launching a cool prototype — defying its purpose. At the forefront of this transformation, Monta AI empowers organizations to elevate their business with AI, delivering reliable and continually improving solutions, particularly in high-stakes environments.
There’s a massive gulf between a cherry-picked LLM demo and a reliable deployment in production. Imagine testing a rally car on urban roads and expecting optimal performance on unpaved terrain in a race. Similarly, AI applications need to be developed and tested with real-world settings in mind. Their non-deterministic nature makes controlling what customers experience a significant challenge. LLM applications exacerbate that quandary as customers use natural language to interact with applications in astonishingly unanticipated ways. Imagine buyers of a rally car using it to cross rivers and expecting it to be amphibious!
Demos give a false sense of control. They work as designed. They walk potential buyers through a happy path. AI applications in the real, rugged world suffer tremendously from chaos. AI models — by design — are nondeterministic. They model mappings between inputs and outputs in a far more compressed fashion (compared to rote learning or storing explicit mappings in a queryable format). The lack of control in AI applications stems from putting a nondeterministic solution in the hands of customers, who expect it to perform accurately, free from bias and noise. The harsh reality is that bias and noise are inevitable; we merely seek to minimize their effects. We strive to control as much as possible in applications that run amok once outside demo sandboxes.
To start, Monta AI works closely with businesses to define what targets their AI applications shall seek. AI objectives must align with business objectives to add value. These typically include optimizations for metrics such as profit, quality of service, and customer trust. Too often, many software vendors lose sight of such alignment between AI applications and business objectives. Many rely on community benchmarks and leaderboards to make critical decisions such as which LLM to use. In a demo, reusing canned examples from such benchmarks is commonplace. In a real-world application, the benchmark better be real-world examples; otherwise, evaluation suffers from the streetlight effect: looking for answers where it’s easiest to look instead of where they probably are. At Monta AI, we bring along floodlights to find business value — no matter how elusive. We transform business objectives and constraints into technical reality, applying proven best practices in high-stakes environments, as demonstrated by successful deployments for public and private sector clients. Our approach ensures that AI applications deliver measurable business value with maximal control, not just clever outputs in demos.
Part of our approach to increase reliability is deep analysis and understanding of business needs and critical challenges your application will face in production, for example:
The list above is by no means comprehensive. It’s merely a glimpse into what it takes to build reliable LLM applications in production. It takes deep integration and alignment of engineering, data, modeling, and business efforts. As LLMs enter high-stakes domains — such as government, healthcare, and finance — the need for reliability, auditability, and control keeps rising. In the next series of posts, we will walk through how Monta AI deployed LLM systems for high-stakes use cases with further details and insights into real-world compliance, resilience, and scale.
In the meantime, if you’d like to see examples of what we’re delivering for customers today:
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Want to bring production-grade LLM reliability to your next project?
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