VixitAI Cortex: Enterprise AI That Answers From Your Own Data

VixitAI Cortex is the enterprise product in the VixitAI portfolio. It is aimed at a specific and very common situation: an organisation holds years of documents, the answer to almost any question is somewhere inside them, and finding it means a person opening files one at a time.

Cortex lets someone ask a question in plain English and get an answer back with citations to the source document. It runs at cortex.vixitai.com.

The problem Cortex is built for

Every document-heavy organisation eventually hits the same wall. The records are all there — scanned, filed, indexed after a fashion — and none of that helps, because the question a person actually has is never "show me file 4417". It is:

• "Have we handled a claim like this before, and what did we decide?"
• "What does our policy actually say about this edge case?"
• "Which of these contracts has a clause like the one the client is asking about?"
• "What was this patient's history before they transferred to us?"

Those are questions about content, and filename search cannot answer them. So they get answered by the person who has been there longest, from memory, which works until that person is on leave or leaves entirely.

How VixitAI Cortex works

The shape is straightforward, even if the machinery underneath is not:

1. Your documents go in. Records, policies, case files, reports — whatever the corpus is.
2. You ask in plain English. No query syntax, no field names, no training the user on a schema.
3. Cortex answers with citations. Not just an assertion — a pointer to the specific document the answer came from, so a human can check it.

That third step is the one that decides whether a system like this survives contact with a real organisation. An AI answer with no provenance is a rumour: useful when it is right, actively dangerous when it is wrong, and impossible to distinguish between the two. An answer with a citation is a shortcut to the source — the reader still verifies, but they verify one document instead of forty.

Who Cortex is for

Sector	The corpus	The question it shortens
Hospitals	Clinical records, protocols, discharge summaries	What is this patient's relevant history?
Law firms	Case files, contracts, prior opinions	Have we argued this point before?
Banks	Policy documents, credit files, circulars	What does our own policy permit here?
Insurers	Policy wordings, claim histories	How was a comparable claim assessed?
Logistics	Operational records, SOPs, incident reports	What happened last time this route failed?

The pattern across all five is the same: high document volume, high cost of a wrong answer, and an existing process that depends on institutional memory.

Data isolation, encryption and audit

Three properties matter more than features when a company is deciding whether to put its records into a system like this, and VixitAI Cortex is built around them:

• Tenant isolation. Each customer's data is scoped separately. One organisation's corpus is not mingled with another's.
• Encryption. Content is encrypted rather than sitting in the clear.
• Audit logging. Queries and access are recorded, so you can answer the question "who asked what, and which documents did that touch?" after the fact.

Being straight about the limits: VixitAI does not publish third-party compliance certifications for Cortex. If your procurement process requires a specific attestation, raise it during evaluation rather than assuming it exists.

The cost of retrieval, in plain terms

It is easy to underrate this problem because no single instance of it looks expensive. Nobody files a complaint about spending twenty minutes finding a document. The cost is structural rather than dramatic:

• It scales with tenure, in the wrong direction. The longer an organisation operates, the more documents exist and the slower retrieval gets — exactly when the archive is becoming most valuable.
• It concentrates in a few people. Whoever has been there longest becomes the index. That is a single point of failure with a notice period.
• It suppresses the question. This is the real cost. When checking is expensive, people stop checking and work from assumption instead, and the errors that follow never get attributed back to the search that did not happen.

That last one is why a retrieval tool tends to change behaviour rather than just save minutes. When the answer is thirty seconds away, people look things up that they would previously have guessed.

What a rollout actually involves

The honest version, since this is the part that gets skipped in product pages:

1. Deciding what goes in. Usually the hardest step, and it is a policy question rather than a technical one — which document sets, whose access, what stays out entirely.
2. Getting the corpus in a usable state. Scanned documents, inconsistent naming and near-duplicate versions all degrade answers. Some cleanup is normal.
3. A narrow pilot. One team, one document set, questions with known answers. Broad rollouts before this step are the classic way these projects die.
4. Establishing the verification habit. Staff need to treat the citation as the answer and the summary as the shortcut, not the reverse. This is training, not configuration.

What Cortex is not

Worth drawing the boundary clearly, because "enterprise AI" is a phrase that has been stretched past usefulness:

• It is not a general chatbot. It answers from your corpus, not from the open internet or from model training data.
• It is not a decision-maker. It retrieves and summarises with provenance; the judgement stays with the person, which is exactly why the citation is the product.
• It is not a document management system. It sits on top of your records rather than replacing how you store them.
• It does not remove the need to read. It removes the need to read the wrong forty documents first.

How to evaluate it honestly

If you are trialling Cortex, or any system in this category, the useful test is not a demo question with a clean answer. It is this:

1. Pick ten real questions your team asked last month, where you already know the correct answer and roughly how long it took a human to find it.
2. Run them. Score not just correctness but whether the citation actually supports the claim — a right answer with a wrong citation is a failure mode you want to catch early.
3. Include the questions with no good answer. The most important behaviour is what a system does when the corpus genuinely does not contain the answer. Confident invention on those is disqualifying.
4. Time the verification. The saving is retrieval time minus verification time. If verification takes as long as the original search, there is no gain.

That fourth point is where most enterprise AI pilots quietly fail, and it is worth measuring before rollout rather than after.

Cortex and the rest of VixitAI

Cortex sits alongside VixitAI HMS, which is the healthcare product, and VixitAI Studios, which builds custom products for clients. The healthcare connection is not accidental — hospitals were an early example of exactly the problem Cortex targets, since a patient's relevant history is spread across consultations, lab reports, imaging and discharge summaries that no single screen shows.

For pricing and pilot scope, go through cortex.vixitai.com, or see the full product overview for where Cortex fits.