We don't digitize anymore.We solve.
Why we built Vailor this way, and what it changes for your teams.
The future of GRC is written with AI
GRC is a deeply textual, documented and standards-driven world: ideal ground for LLMs, with a still-largely-untapped potential.
We used to digitize
Legacy solutions moved GRC from Excel to more modern interfaces or spreadsheet wrappers. A good step, but designed before ChatGPT, when AI's potential was still unproven.
We solve
It's 2026, post-ChatGPT: merely digitizing GRC is outdated. You can now solve it with AI. Turning to a solution that only digitizes would be a mistake.
We don't digitize anymore. We solve.
AI-Native, not AI-patched
"AI-native" is an overused buzzword. Here is what actually separates patched AI from native AI.
Patched AI
- Depends on the user: whoever prompts better gets a better answer
- Black box: answers that are neither sourced nor traceable
- Generic RAG that rakes over your documents without targeting the right context
- Constant chatbot ↔ platform friction, and confident hallucinations
Native AI
- Traceable and verifiable: when the AI relies on your documents, it quotes the passage, and Vailor checks word for word that it is there
- User-agnostic: prefilling needs no prompt, the AI fills the fields directly and your experts validate. On top of that, "Ask Vailor" answers on every page of an assessment, from the assessment and its documents, with no internet access
- A surgical RAG that brings the right context at the right time, to the right place, with guardrails against hallucinations
- Model-agnostic: you choose the model, including one self-hosted on your side
Next-gen parser
Vailor reads PDFs, Office files, spreadsheets, images and emails, including the diagrams inside them: your sources are understood, not just indexed.
Proprietary RAG
A surgical RAG that brings the right context at the right time, to the right place: no more catch-all generic RAG.
Configurable models
Model-agnostic: no need for the latest Anthropic or OpenAI model to perform. You choose the model, including a self-hosted one.
Quoted and verifiable
When the AI relies on your documents, it quotes the passage, checked word for word; nothing is validated without an expert and every decision is traced: AI that accelerates toward the truth, not into the wall.
How to spot patched AI?
A few questions to ask GRC AI alternatives on the market to tell whether they are genuinely AI-native or merely patched.
Does the AI only exist in a chat?
If yes, the result depends on the user and the quality of their prompt: neither consistent nor traceable. A chat assistant helps as a complement, not as the only way into the AI. Otherwise, that's patched AI.
Does the result depend on how each person phrases their request?
If yes, quality varies from one user to the next and from one project to the next, and no one can vouch for it in an audit. That's patched AI.
Are quoted passages checked and decisions traced?
If not, you can neither check where a proposal comes from nor justify it in an audit. That's patched AI.
Is there a way to check whether the model hallucinated?
If not, it's a black box that cannot be cross-checked. That's patched AI.
Do you go back and forth between a chat and the fields to fill in?
If yes, the AI does not interact with the platform natively the way a human would: it's an overlay, context isn't injected where it's needed and friction is constant. That's patched AI.
Does the AI produce free text, or structured outputs written directly into the platform's fields?
If it's free text, the AI is not manipulating structured elements directly usable inside the platform. That's patched AI.
Should you build your GRC yourself?
You're bound to weigh make vs buy. Prototyping the start of a GRC module without AI can be vibe-coded quickly. A genuinely AI-native GRC platform cannot.
One scoped module, yes; native AI, no
A very scoped GRC module, without AI, can indeed be vibe-coded in a weekend. But the moment you graft AI onto it, the effort grows sharply, and native AI does not get vibe-coded. And that is a single module: for N interconnected modules within a unified platform, complexity grows exponentially.
Applied AI can't be improvised
Years of applied AI, RAG architectures, prompt evaluation and non-regression, anti-hallucination guardrails, complex document parsers: this is proven and engineered. A wrong answer asserted with confidence, with no way to detect it, is the worst possible application of AI: worse than no AI at all.
An in-house tool is shadow IT
It would host your risk registry and your vulnerabilities. Would you sign it off in an audit, with no secure SDLC, no risk assessment, no DR, no SLA, no BCP? The tool that governs your compliance cannot itself be ungovernable. And making it genuinely governable costs, in internal build and run, more than a license.
A product, not a project: the regulatory clock is ticking
Frameworks and regulations evolve continuously: every hour spent maintaining an in-house tool is an hour less on real security. Buying means starting with a scoped pilot; building means waiting months before you have a usable tool.
Built for the enterprise
Built for large groups, mid-market companies and IT services firms: a modern architecture, with data stored in France as SaaS, and built to last.
Your organization, entity by entity
Vailor mirrors your organization: group, subsidiaries, scopes and assets. Each entity follows the group's settings or keeps its own (scales, matrices, risk sources).
Fine-grained RBAC
Precisely control who can view, edit or validate each element, per module and per entity, with custom roles (including a read-only auditor).
Sovereignty & deployment
As SaaS, data stored in France (AWS Paris region) and AI processing in the EU. Or installation on your premises (Docker), with the AI model of your choice: your data stays under your control.
Future-proof by design
Designed after the arrival of LLMs, Vailor puts AI at the heart of every workflow. AI added afterwards to an existing platform remains an overlay.
Not a sales pitch: a conversation
30 minutes to talkabout your needs.
A real conversation with the founding team: we talk AI, GRC and your use cases. You leave with concrete advice on using AI, even if Vailor is not the right answer for you.
- Talk AIWhere AI really saves you time, and where it has no place.
- Talk GRCYour methods, your frameworks, your organization: we start from your reality.
- Leave with adviceStraight recommendations on using AI in your teams, no strings attached.
or write to us at contact@vailor.ai