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

The chart shows a drop, but not why.

AI explains the variance and links to the documents it is based on.

Email usBI and data
Problem

Management sees the number has moved, and the commentary takes hours to write.

What we’ll do

AI over the semantic model, explanation with a source.

Result

AI prepares the report commentary; a human just verifies it.

How it works

Four steps from number to explanation.

ERPModelReport
Step 1Loading dataFrom BI or directly from source systems.
Step 2ComparisonAgainst the plan, history or comparable segments.
Margin, October
Step 3CauseAI finds the connections in documents and comments.
Why did the margin drop?Purchase prices of profiles +6%, source: model, October.
Step 4Commentary and suggestionSummary, recommendation and a source for every number.Verified by a human

What changes

AI prepares the commentaryInstead of compiling, a human reviews a finished draft.
Variances in timeThe workflow runs daily; it doesn’t wait for the monthly report.
Every number traceableEach comment shows the source and the rule it is based on.

When it makes sense

It pays off when

  • The report shows numbers, not the cause
  • Commentary is written by hand outside BI
  • Management wants an answer sooner than in a month

What to watch out for

  • Not even AI helps with inconsistent data; then we start with the data foundation
  • The commentary is input; a person decides
  • Metrics must have one definition
Data FoundationProcess exception analysisAll use cases