Integrity Cortex

Integrity Cortex map

The enterprise the cortex draws: each department, what it is for, and the functions it connects.

A neurosymbolic memory layer for the enterprise

Every action taken. Every fact extracted. Every AI suggestion. Every human confirmation.

Assembled into a single, self-sustaining digital cortex.

Specifications

Click any card to see the proof attached to Integrity Cortex of sci2sci.

This proof page is part of our live Integrity Cortex

Each specification card is linked to our own Integrity Cortex — not a demo instance, but the live one used in production. The values on the proof page were derived by the engine, cryptographically signed, and published with their full audit trail.

You may notice that our claims are absolute values. It's because they come from the nature of the design. You won't be surprised by Apple claiming the iPhone will start when you press the button, or that the screen will work with finger touches. The specifications of Integrity Cortex have the same nature — it's just how it works. To understand why, see our FAQ further on the page.

The systems behind these cards are complex because they describe how Integrity Cortex itself works — formal methods, computability theory, and self-reflexive verification. That's our foundation, not yours. Your cortex will speak your native tongue.

Below you can also review the integrity metadata and verify the signature against the public key. In the proofs you can inspect any node of the graph. This is how we ensure the reproducibility and compliance of the Integrity Cortex for mission-critical applications.

Knowledge as Code

Every fact is addressable, versionable, verifiable — like source code.

Self-sustaining coherence

The cortex maintains its own integrity — no manual reconciliation.

What Integrity Cortex does

Living knowledge extraction

Automatically extracts facts, claims and relationships from every data source. Connects them into a unified system that updates as your data changes.

Cascade dependency tracking

When a fact changes, every downstream dependency surfaces immediately — across systems, teams and jurisdictions. Every downstream impact, in seconds.

Neurosymbolic verification

Detects LLM hallucinations and inconsistencies that existed in the ground truth before AI ever touched the data. Every output traced to source.

Built for regulated industries

Full audit trail, deterministic verification, 21 CFR Part 11 compatible. Designed for biotech/pharma, banking and industries where integrity is the license to operate.

When your teams use AI in VectorCat, Integrity Cortex becomes the neurosymbolic memory for the entire organization.

Certainty of results

If you need more than yet another confidence benchmark.

FAQ

Why Integrity Cortex?

Because we sell one thing that none of the multibillion-dollar AI companies can offer you right now: reliability and integrity.

It's not normal for software to work in 95% of cases and fail in the other 5%. Five years ago, if someone had offered you a service that failed in 5 out of 100 interactions, you would have laughed. For over fifty years, software has scaled for exactly one reason: it has been more reliable than humans.

The baseline of reliability for software itself is 100% — and only if it's 100% can you compose and scale it further. Even bugs are reproducible. The baseline for most complex services built on this software, combining billions of lines of code, thousands of units of hardware, and millions of human hours, is 99.9%. The slack is the price of integration — humans, handoffs, edge cases. The reliable core is what lets the service absorb them. Now swap the core. Replace 100%-predictable software with a component that fails 5 times in 100, and wrap it in the same humans, the same integrations, the same edge cases. The errors don't cancel. They compound.

That's why, despite all the big promises, you see neither "AI employees" nor "agentic swarms." To exist, they need integrity; to have integrity, they need to be predictably reliable; to be predictably reliable, you need to know the failure mode. Even if tomorrow we see not 5 but 1 error in 100 turns, or even 1 in 1000, that's not enough. The core must be pure and error-free.

Any engineer knows that a random, non-reproducible bug — one tied to timing, or to a random parameter — is the worst kind. If it happens in a critical part — a kernel, or the engine of a massive service — that becomes tomorrow's headline. But for the LLM's "engine" randomness is not a bug. It's the design.

To offer you an analogy, consider which of two scenarios you would prefer. In the first, you simply know a certain compound doesn't work. In the second, it looks promising, but by Phase 3 there's a good chance that an error in one of the previous stages has erased all the progress — along with the $500M you've spent on the molecule. Every biopharma professional has an intuition for why we need to kill drugs early. The same logic applies to software errors.

Integrity Cortex makes knowledge behave like software: composable, reliable and built to scale. And that's what you can't get from any LLM.

How does it work?

We turn documents and data into code. Once knowledge lives in that form, it inherits forty years of engineering discipline: you can version it, trace it, verify it, diff it, reproduce it.

That's the same principle as "turning text into tokens." It sounds quite straightforward until you think that it gave us Google and the current generation of AI. We're extending this direction by making AI interpretable and consistent.

Wouldn't it be very complex?

Not more than an FDA submission.

How do we know?

We actively use our own systems in-house. Each clickable card in the specification section is linked to our own Integrity Cortex — not to a demo instance, but to the live one used in production.

The systems behind those cards are complex because they describe how Integrity Cortex works, and it's one of the most sophisticated software projects ever built. The language of our Integrity Cortex is formal methods, computability theory, self-reflexive systems, and philosophy of science — that's our foundation. Most of it, including the engine, is open-sourced in our repository. And luckily, it's also our complexity to manage — it won't affect any other installation.

Biopharma, while complex and much larger at scale, has a much more coherent ontology. Your cortex will be for your domain, in your language, with your artifacts — that's the whole point. It will think and speak in your native tongue: assays and compounds; wild types and knockouts; toxicity and safety; cohorts and primary endpoints; SDTM terms and submissions; adverse events and risk assessments.

• repository

Do I need to understand how it works to use it?

You don't. The principle is the same as not needing to understand databases to log into your Google account, or how speech works in the brain to speak. Integrity Cortex just works, and ensures people (and LLMs) get reliable information.

Think about it as documentation or SOPs. Those aren't things you look at every day, and yet they're deeply integrated into your work and shape how it's done. Same here — LLMs give you replies, but now you can check the logic if you'd like; search gives you better documents because it knows how they relate to each other; color-coded checkboxes saying which information is verified, how, and by whom appear in the UI. But you never need to see the whole machinery producing them.

How are you different from Context Graphs and RAG?

Those are storages — they hold information. Cortex actively processes the new information, checks its grounding, and continuously computes how consistent it is with what it already knows. That's what makes it the only system that will automatically update when the documents and data around it change, and react to those changes by updating the shape of its networks.

This lets it solve a completely different class of problems — like, which documents do we need to update if our primary endpoint has changed? That question is impossible for RAG to answer reliably. We do it in seconds, with certainty.

Is it a Knowledge Graph?

Not quite. Cortex, as a substrate, is executable code. It can display its parts as a graph, but it's more fundamental — it's the thing that can assemble knowledge graphs, not just a single KG.

We can also display "computation layers" — which are more like the structure of neural networks — and "live documents," which can show the facts from the cortex in prose, for example in LLM answers. This lets you use a reliable source of facts directly in any chat bot. We use it ourselves in VectorCat research agents.

Your claims are quite assertive. Where is the evidence and the statistics?

It comes from the nature of the claims we make. Quick example — you won't be surprised by Apple claiming the iPhone will start when you press the button, or that the screen will work with finger touches. The things we describe about Integrity Cortex have the same nature. It's just how it was designed, and the only reason it may look surprising is the complexity of the underlying design.

Think about it this way: when you have a drug, you don't need a study about what molecule or protein you use, or which pathway you target — your discovery team just designed it that way. You know the mechanism. Efficacy is measured at later stages.

We don't claim efficacy yet, because we're in what would typically be considered early Phase I — and our partners are the ones we measure it with.

And of course, you can always enroll in our ongoing program.

• enroll in our ongoing program

Integrity Metadata

Signed by v@sci2sci.com at 2026-10-27T15:50:31Z. Root hash 731dd0a24d7d76ee2561418d09bfe575517116f8427c586ec00e2b9f0cff268e, with an Ed25519 signature of the root hash, verifiable against the public key. 23 repositories tracked, 6 notebooks signed.

• integrity-block.json
• ic-sign.pub