Openlayer is making reliable AI an immediate reality.

AI has the potential to transform industries, but too often, inconsistent results and a lack of robust testing hold back that progress. Frustrated by the gap between AI's potential and its real-world performance, we set out to create something better—an environment where teams can confidently develop and deploy AI systems. We named it Openlayer to represent transparency and trust.

What began as a simple way to validate AI models has evolved into a powerful platform that streamlines the entire AI lifecycle, from development to production monitoring. Openlayer isn't just about running tests; it's about enabling teams to build AI systems that consistently perform, ensuring reliability at every step.

Today, AI-driven teams—from startups to global enterprises—use Openlayer to deliver trustworthy AI experiences. Openlayer helps them focus on what matters: making AI dependable and impactful in the real world.

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Our world is consumed by the potential of artificial intelligence. It doesn’t feel like sci-fi or lunacy anymore.

To better understand the current moment in AI, let’s start by laying some groundwork. Where are we? Where can this all go? What are our collective challenges and how do we overcome them?

AI is already reshaping our world, but our baby steps will soon turn into a full-fledged sprint. At Openlayer, our goal is to make this transition as fast, as frictionless and as fulfilling as possible. We are lighting the torch of the AI revolution.

What is the AI revolution?

Okay, so an AI revolution is underway, but what exactly is so tantalizing about it? Culturally, we’ve always been fascinated by the idea of playing God, but what potential does AI hold from an economic perspective? Let’s examine three axioms:

• AI is fluid – the boundaries of its capabilities shift continually and rapidly.

• AI is democratic – anyone has access to powerful foundation models through simple APIs.

• AI is horizontal – its applications are seemingly limitless (it’s a new civilizational door, just like the internet).

The more creatively we apply AI and the faster we execute, the better our chances are of seizing opportunities and creating economic value.

What are the challenges involved?

Throughout history, every revolution has come with major risks. In the case of the AI revolution, we grapple with the uncertainty of yielding safe and functional outcomes for any given project.

Why are the outcomes of a given project uncertain? While the potential of an idea is relatively easy to articulate, the pitfalls of applying AI as the solution are not. Why? Today’s AI models are gigantic neural nets (black-boxes), trained on swaths of data we haven’t been able to comb through ourselves. There is a tremendous amount of unpredictability involved. Discovering and guarding against the universe of pitfalls that will inevitably arise is the mighty task of any AI practitioner.

Hence, the primary challenge doesn’t lie in building a working prototype of an application — it lies in ensuring reliably safe and functional outcomes across the spectrum of real-world scenarios. Let’s call this the reliability problem.

Will the reliability problem solve itself?

You might be having the following thoughts:

• As AI models improve, AI applications will stop making mistakes.

• A central authority — be it our governments, OpenAI or some coalition of grown-ups in the room — will put in place robust AI safeguards to prevent unwanted behavior.

These developments will transform the reliability problem but will not eliminate it. Here’s why:

• Improved models expand the universe of both possibilities and pitfalls. We can do a lot more (good as well as bad) with GPT-4 than we could with BERT.

• Universal safeguards will only prohibit egregiously bad behavior. Safeguards defined by a central authority can never capture the requirements of every feasible use case. The reliability problem is not someone else’s to deal with. We need to own the problem.

Is there a path to addressing the reliability problem?

Here’s the good news. We’ve already figured out how to handle the reliability problem — just in a different context.

The morning after the revolution, virtually every group’s endeavor toward safe and functional outcomes has been accomplished by establishing a set of shared values, then converting those values into rules for self-governance. Let’s call these rules a constitution.

A crucial point to recognize is that different constitutions govern the behaviors of different people, groups, societies and entities. There is no single set of guidelines that can govern everything for everyone. Certain rules may hold true at the highest level of abstraction (e.g. the Golden Rule), but these are often insufficient to account for the variety of desired outcomes across the range of human endeavor.

Some examples of the different “constitutions” we rely on:

The United Nations charter.

The Declaration of Independence and the Bill of Rights.

Regulatory codes in different industries (e.g. finance, pharma, energy, telecom, agriculture).

Company charters and mission statements.

Reddit forum guidelines.

Unlike sacred texts, constitutions are living documents. We need to specify what matters at varying levels of granularity, but “what matters” is also a perpetually moving target. As our goals, priorities and environment evolve, so must the rules. Our system of governance — democracy — places the power to shape these rules in the hands of the people directly impacted by the outcomes of the human endeavor.

A hard truth: there is no universal or stable system to ensure safe and reliable outcomes across the board. The world is inherently dynamic, and the best we can do is meticulously define and update what success means in a given context, then continuously validate everything we do against these criteria.

How do we apply these ideas to the AI revolution?

Quick reminder – the bottleneck to capitalizing on the AI revolution is the reliability problem. Extrapolating from history, the path ahead requires us to repeatedly crystallize a range of safety and functional requirements into something akin to a constitution.

Foundation model builders attempt to do this using a technique called "Constitutional AI." The gist is that you draft a constitution in natural language and then train a model with the constitution present at every step. Some of these rules may be universally applicable (e.g. do not hallucinate or leak PII); others may be domain-specific (e.g. return numbers in decimals, not fractions). The more we flesh out these rules, the more we extinguish uncertainty. Turning this into a repeatable process allows us to dramatically increase the number of applications we can pursue. The more applications we can pursue and release with near behavioral guarantees, the more we are able to capitalize on the AI revolution.

Openlayer: Lighting the torch of the AI revolution

This all sounds great, but how do we convert a set of sophisticated guidelines or rules written in plain language into an AI application with true behavioral guarantees? Constitutional AI as currently envisioned has some major limitations – it requires vast resources: training large-scale models with these governing rules embedded and continuously retraining them as the constitution evolves. The challenge doesn’t stop there—one still needs to ensure the real-world behavior of AI applications consistently aligns with this evolving constitution.

Cue Openlayer.

Rather than retraining powerful models with a static set of rules, Openlayer provides a dynamic system for continuous governance—allowing AI systems to evolve alongside their guiding principles.

At Openlayer, we are building the fundamental tools that make this process feel symbiotic and effortless. Our mission is to operationalize AI governance—defining, enforcing, and improving system behavior in a way that’s intuitive and largely automated.

To do this, we borrow ideas from the foundational structure of democratic societies, where governance is distributed across three branches, each with a distinct function.

• The legislature creates laws that guide behavior.

• The executive enforces these laws.

• The judiciary ensures laws are applied fairly and consistently.

Openlayer translates this framework for the world of AI. We help teams build and scale a structure of AI governance that meets their needs:

• A legislature to create the quantifiable rules by which an AI system’s behavior is judged, translating abstract principles or a "constitution" into concrete metrics.

• An executive to apply these rules throughout the AI lifecycle, from pre-release testing to post-deployment monitoring.

• A judiciary to step in when AI behavior deviates from expectations, reviewing complex cases and determining corrective actions.

By making Constitutional AI accessible for the many, not the few, we turn safe and functional outcomes into a near guarantee, rather than an uncertainty. Because creative and ambitious teams should be free to focus on running experiments and turning crazy ideas into reality.

The AI revolution is underway. Will you join us?