Superficial is building foundational infrastructure to make artificial intelligence reliable.

Over the past decade, neural networks — particularly large language models — have transformed how we interact with information. These systems are now capable of writing, summarising, translating, and assisting with increasingly complex tasks. They are fast becoming embedded in day-to-day life.

But they remain fundamentally unreliable.

Neural networks are probabilistic systems. They do not reason. They do not understand. They generate outputs by identifying statistical patterns in their training data. As a result, they make mistakes. Not occasionally, but inherently.

These mistakes appear in familiar forms: hallucinated facts, flawed logic, missing context. Today, we compensate with human oversight — reviewing outputs, spotting errors, intervening where needed. This is manageable in low-stakes scenarios but it doesn’t scale to high-risk domains.

And that’s where we’re headed.

AI systems are beginning to act. Agents plan, decide, and execute across software environments and real-world systems. They are being deployed in finance, infrastructure, healthcare, and beyond. In this new phase, errors don’t just mislead — they cascade. They compound. They occur at machine speeds that make human intervention too slow.

The immediate risk from AI is not sentient malevolence as many may suggest, but unchecked malfunction: systems causing harm by accident, through silent errors, unexplainable decisions, and brittle logic paths no one can trace.

Scaling was meant to solve this — larger models with more data and more compute were expected to grow more accurate over time. But recent evidence shows otherwise. The most advanced models today still make mistakes in 5-15% of statements. This is unsurprising. These systems are still statistical engines. More fluent prediction does not equate to more reliable reasoning.

If scale alone cannot deliver trustworthiness, we need a new path.

Neural networks represent just one approach in AI. There is another: symbolic systems. Built on logic, rules, and structure, symbolic AI is deterministic, interpretable, and auditable. It offers consistency. In domains where reliability is not optional — medicine, law, infrastructure — these qualities matter deeply.

But symbolic systems have limitations of their own. They are brittle. They struggle to handle noisy or ambiguous inputs. They require explicit design. Historically, they have failed to generalise.

Each paradigm has strengths. Neural systems are flexible, adaptive, and powerful in unstructured environments. Symbolic systems offer structure, logic, and the ability to explain their conclusions. Alone, each is incomplete. Together, they offer something more.

This is the promise of neurosymbolic AI — systems that combine neural perception with symbolic reasoning.

Some of the most capable AI systems we’ve seen to date emerged from this union. AlphaGo paired neural networks with symbolic tree search to master Go. Code generation tools combine neural text completion with symbolic program analysis. These hybrid approaches outperform either method alone.

But scaling neurosymbolic systems to general-purpose applications remains unsolved. The core challenge is symbolic grounding: how to translate the high-dimensional, ambiguous outputs of a neural model into the structured inputs that symbolic systems require.

This interface between probabilistic pattern matching and deterministic logical reasoning is the current bottleneck. Solving it requires new architectures, verification layers that can audit model outputs in real time, and training techniques that allow neural components to operate within logical constraints.

The need is growing urgent.

As AI agents move from passive tools to active participants in workflows and decisions, the margin for error narrows. In medicine, a hallucinated dosage can be life-threatening. In legal systems, a flawed summary can bias decisions. In infrastructure, a silent logic failure can ripple across dependent systems.

At the same time, regulatory scrutiny is intensifying. Governments and institutions are demanding transparency, traceability, and safety. Compliance standards like the EU AI Act will require systems to explain how decisions are made. Reliability will not be a differentiator — it will be a requirement.

At Superficial, we are building the infrastructure to meet this moment.

We are developing the tools to extract and structure what models say, to verify outputs in real time, and to guide generation with logic.

We believe that the future of AI doesn’t require choosing between capability and reliability. With the right infrastructure, we can build systems that are both.

Our goal is simple: to make artificial intelligence reliable enough for the world it is entering.

And to do it now — before failure becomes the default.