Beyond the booth: What is "100% accurate" enterprise AI?

At recent events you may have seen our booth messaging say ‘100% accurate enterprise AI.’ We chose that phrasing to spark exactly the conversation you’re now reading: what does accuracy even mean when we delegate decisions and actions to LLM agents?

In this post we dissect the claim, translate it into measurable engineering goals, and show the architecture that turns this provocative hyperbole into operational guarantees.

A 100% accuracy claim technically doesn't make sense for AI. Even humans can't achieve 100% accuracy, and in real-world scenarios sometimes whether something is accurate or not is itself is a grey area.

Here are 2 examples of real world challenges in accuracy that result in not being able to trust the AI system that does an analysis for us, or automates a repetitive task or workflow:

So if 100% accuracy is not possible, how do we define sufficient accuracy? What is necessary and sufficient to be able to delegate analyses or tasks to AI connected to our data and systems in enterprise?

There are 3 critical things that are realistic 100% north stars:

1) Predictability

• Should do the same thing always
• Should not fail in surprising ways
• Should learn new things in expected ways

2) Explainability

• Should be able to completely explain how it arrived at an answer

3) Steerability

• Humans should be able to fully control AI behaviour to steer it to an answer or a plan that is accurate in their eyes

These are in fact the things that you would want from any colleague working with you.

You want your colleagues to be predictably accurate, be able to explain their work with rigor and have an expectation that they'll do better once told how to do so.

And while 100% accuracy is not possible, it is possible to achieve a 100% trust in the accuracy/reliability level of the AI system's work, by targetting complete predictability, explainability and steerability.

So how does PromptQL achieve that? PromptQL's AI platform helps build a custom AI model that targets complete predictability, explainability and steerability.

Regardless of how advanced foundation models get, there are 2 fundamental problems that create a trust gap:

1. LLMs don't use and continuously learn your unique business context.
2. LLMs are not deterministic in doing what they say they are doing.

So for a domain called Acme, PromptQL provides a learning layer to build and deploy an AcmeQL model.
The AcmeQL model generates plans in a DSL "AcmeQL" which is then programmatically executed in PromptQL's runtime layer.

This enables the AI system to:

• Engage with data & systems the way a person on your staff would
• Plan, reason, act & explain in the "language" of your business
• Stay up to date with the continuously changing context of your business
• Guarantee that planned orchestration is exactly how it is executed

A note on trade-offs & limitations:
PromptQL's approach is designed for analysis and automation tasks in an enterprise. It won't improve accuracy or the perception of accuracy for purely generative tasks like, say, writing poetry or brainstorming creative campaigns. Here the accuracy will be comparable to what the underlying LLM offers.

In our work with a public healthcare technology company, accurate automations powered by PromptQL's AI platform will generate ~$100M impact, by helping patients schedule radiology appointments.

Patients call to schedule radiology appointments—ultrasounds, X-rays, mammograms. Simple, right? Each call takes 12-15 minutes. But hidden in those minutes is extraordinary complexity.

Operators juggle countless rules in their heads to help schedule correctly:

• Which procedure code matches this patient's insurance and medical history, not just their symptoms?
• Clinic A doesn’t do  X-rays after 3pm on Fridays for the next two weeks so I need to work around that.
• Are there any new default procedures this insurance provider offers in addition to the basic scan?

To be successful the operators need to know every rule, every exception, every workaround. They need to be walking encyclopedias of business logic.

The IT team? They're brilliant at building systems. But they can't possibly code every variation, every edge case, every "except when..." scenario that operators handle daily.

The cost of this gap & PromptQL's impact:

• Every 3 minutes saved would generate $50M in additional annual capacity.

How PromptQL solves this problem:

• PromptQL's agentic semantic layer captures enough business context to allow non-technical users to create and deploy accurate algorithms using their business language
• PromptQL's runtime helps run those algorithms into programmatically executed business logic.
• These automations are embedded in the operators software and automates a chunk of work with the operator in the loop

The business impact of accurate automation:

• Reduced training for operators
• Immediate increase in capacity with the same workforce

Bring your toughest eval-set for a use-case where you'll see a high amount of business impact by deploying accurate AI to run analysis and automation in your enterprise.

Our team will show you how PromptQL helps you smash it within a few days!

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