CZ Test your AI
Inovai Platformrunning Testing, monitoring and evidence in one placeIt attacks your AI after every change and documents every finding. How the platform tests

AI security

AI inventoryEverywhere AI runs in your company, including the unapproved Chatbot and agent testingModel, widget code and data flow, before deployment and in operation Copilot auditPermissions and data leaks in Microsoft 365 Monitoring and evidenceA new run after every change, a report for the auditor OSINT investigationWhat an attacker finds about you and your people

AI integration

Digital colleagueA chatbot for your website and e-shop, and an in-house colleague on top of data and BI AI workflows and agentsConnections to ERP and CRM, agents that write and send Copilot StudioCustom agents in Microsoft 365 Local modelsData never leaves the company

Document extraction

Invoices and ordersExtraction and export to ERP without retyping Contracts, certificates and delivery notesData and deadlines, 10,000+ certificates at Favex Production ordersFrom email through the order to production documents Checks against rulesERP, standards and contracts, exceptions for approval

BI and data

Power BI and Qlik SenseReporting that management actually opens Data platformFabric, Databricks, data warehouses AI on top of reportsCopilot in Power BI and data agents Forecasts and recommendationsDemand forecasting, recommendations, anomalies

Workshops and analysis

Analysis and solution designWhere AI saves time and money, and how to build it AI for managementWhere AI makes sense and where it doesn’t Secure AI for ITAttacks, defence and evidence in practice AI literacy for teamsSafe use of AI, Article 4 of the AI Act
All areas

When you’re dealing with

We’re launching a chatbot or agentPre-deployment testing, retest and evidence for management We’re rolling out CopilotBefore Copilot finds what it shouldn’t We’re buying AI from a vendorTesting before you sign off acceptance We don’t know where AI is runningAn inventory of deployed and unapproved AI An audit or inspection is comingEvidence for the auditor, Czech National Bank or NÚKIB

Industries

Banks and insurersDORA and Czech National Bank supervision Public sectorChatbots for citizens, Copilot in public offices HealthcareSensitive data and the higher-obligations regime Energy and industryAgents on operational data

Partners

Chatbot vendorsSecurity testing under your brand Integrators and IT vendorsAI testing for your projects Law firmsTechnical partner for the AI Act

Evidence

Favex case studyAI we built: 10,000+ certificates in production Use casesEighteen situations, from chatbots to invoices Czech AI Security IndexHow easily models are bypassed in Czech, in progress

Company

TeamFour people, AI, data and security How we workConnect, attack, verify, document BlogFrom practice, no hype

Infrastructure

Coming soonGPU server in CzechiaLocal models, fine-tuning and attack testing without cloud APIs.What it will run

Inovai / Use cases

Every system gives a different number.

One definition of metrics, so BI and AI rest on the same data.

Email usBI and data
Problem

BI, ERP, the warehouse and spreadsheets each hold a piece of the context separately.

What we’ll do

Source map, data owners, one definition of every metric.

Result

One version of the numbers you can build AI on.

How it works

Four steps to a foundation you can trust.

ERPCRMWarehouse
Step 1Source mapWhere data is created, where it flows and who works with it.
Customers3% duplicatesOrderscompletePricescurrency missing
Step 2Input qualityCompleteness, timeliness and consistency across systems.
Revenue1 definitionMarginowner: financeCustomer2 versions
Step 3Metrics and responsibilitiesOne definition for every number, and its owner.
Step 4Target stateWhat to unify and fill in so you can build reporting or AI.Verified by a human

What changes

One version of the numbersMetrics have a clear meaning, source and owner.
A shorter path to a pilotThe AI pilot starts on verified data, not on assumptions.
Lower investment riskBefore you build, you know what you’re really building on.

When it makes sense

It pays off when

  • Reports contradict each other
  • Data is split across systems and spreadsheets
  • You’re planning AI or new reporting

What to watch out for

  • Without data owners, nothing changes
  • Some errors need fixing at the source, not in the report
  • The output is a map and a target state; building is the next step
AI over BI and reportingProcess exception analysisAll use cases