💡 The Problem with Traditional Bordereaux Pricing

Most vendors in the bordereaux space price by:

• Number of binders

• Number of submissions

• Or total volume processed

That might make sense for outsourced manual work—but not for automated, governed data processes.

When your pricing is tied to volume, you’re disincentivized to scale.

✅ The Verodat Model: Disruptive by Design

Verodat’s pricing flips the industry standard on its head.

We price based on value delivered, not the number of binders processed. That means:

• More business doesn’t mean more cost

• Automated processes are repeatable with zero incremental admin

• Your cost-per-use-case decreases as you grow

This isn’t just cost-effective—it’s operationally scalable. One client processes dozens of bordereaux submissions without any offshore support or added headcount.

They didn’t have to hire.
They didn’t have to pay by binder.
They just turned on Verodat—and scaled.

🔁 What It Looks Like in Practice

Verodat automates the entire bordereaux workflow:

• Ingests raw claims and premium data

• Structures it according to each binder’s specifications

• Pushes final outputs to systems like VYPR

• Tracks all changes and submissions for full auditability

This same flow runs on a schedule, with no human intervention.

And because the infrastructure is already in place, adding a new binder doesn’t increase the workload or the price.

🔐 Governed, Compliant, and Audit-Ready

Every data point in Verodat is:

• Traceable

• Validated

• Governed

No more spreadsheets passed between teams.
No more manual cut/paste.
No more scrambling for audit evidence.

With Verodat, you don’t just get automation.
You get governance and compliance by default—something few vendors offer.

🧠 Why It Matters Now

With rising regulatory scrutiny and tighter margins in insurance, every efficiency gain matters.

Our pricing is aligned with your growth, not stacked against it. It empowers underwriting and operations teams to scale faster, at a lower cost, and with better data.

The Bouncer at the Door Approach

In the world of business reporting, there’s a familiar scenario that plays out too often:

A report shows unexpected numbers. Someone questions the data. The person responsible breaks into a cold sweat, frantically digging through spreadsheets, tracing data lineage, and questioning every step of their process.

As our CEO Thomas Russell explains: “What happens typically is people build reports at the edge—like a solution inside the house—and this works… it works and then the numbers start to creep off or they look unusual and somebody goes ‘hey that doesn’t look right.’ The person in the room gets a cold sweat going ‘what did I do wrong?’ And they go back trying to figure out and they’re pulling a thread and trying to find data…”

This scenario is familiar because it’s universal. Organizations build reporting solutions that work temporarily, but they’re built on shaky foundations. When questions arise (as they inevitably do), panic ensues.

This approach is backward. Adding another reporting layer to systems built on systems only increases complexity and risk.

At Verodat, we focus on ensuring your data is clean and validated at the point of entry – like a bouncer at the door, only letting quality data through. Instead of reporting at the edge of systems, you’re focusing on ensuring that your source data is clean.

Why This Matters Now

This shift is particularly crucial in today’s business environment for three reasons:

• Audit requirements are increasing: Regulators want to see not just your data, but how you ensure its accuracy
• Decision speed is accelerating: Waiting days to verify numbers is no longer acceptable
• AI adoption demands quality data: AI systems amplify data quality issues – garbage in, garbage out at scale

Beyond Reporting: The Foundation for AI-Ready Data

When you fix your data foundation, everything pointing to it works correctly. Reports become trustworthy not because you’ve added more validation layers, but because the source itself is impeccable.

Case Study: Automating Ireland’s Property Market Analysis with Structured Data

While many organizations are excited about AI agents, successful implementations require structured, trustworthy data foundations that most companies struggle to establish. This is precisely the gap that Verodat fills, enabling truly autonomous AI workflows through proper data governance and standardization.

In this case study, we’ll show how Verodat’s platform enables an AI agent to autonomously analyze Ireland’s Property Price Register (PPR) data—transforming raw property transactions into actionable market intelligence without human intervention.

Verodat’s AI Agent in Action: Property Market Analysis

To demonstrate these capabilities, we’ve implemented an autonomous property market analysis agent that:

✅ Autonomously fetches the latest PPR data through Verodat’s Context API
✅ Correlates property transactions with news headlines about the market
✅ Identifies emerging trends that would take analysts hours to discover manually
✅ Generates complete market analysis reports with zero human intervention
✅ Publishes insights directly to business channels for immediate action

Three major regulations are transforming how organisations govern and use their data in 2025:

• NIS2 (Network and Information Systems Directive) – now in force since October 2024

• DORA (Digital Operational Resilience Act) – enforced since January 2025

• AI Act – passed in 2024, with implementation timelines now rolling out

Why It Matters

These aren’t just checkbox regulations. They demand real data traceability, system resilience, and trustworthy automation.

The requirements now include:

• Being able to track the origin of data

• Proving access controls and permissions

• Showing data update frequency and supply history

• Having full visibility into operational systems and decision-making processes

Where Verodat Comes In

Verodat helps organisations meet these demands without lifting and shifting core systems. We provide a lightweight, governed data layer that:

✅ Connects directly to your existing tools
✅ Structures and enriches your data with context, ownership, and source metadata
✅ Creates audit-ready trails for reporting, decision-making, and compliance
✅ Powers AI adoption on top of trustworthy, verified data

“With Verodat, we cut our audit prep time from weeks to hours. It’s helped us move from firefighting to proactive governance.”
– Verodat Client, Financial Services Sector

Beyond Compliance: The Opportunity for AI Readiness

These regulations don’t just demand structure—they also open doors.

With a Verodat-powered data layer, you’re not just compliant—you’re ready to:

• Build intelligent workflows

• Automate decision-making

• Use AI tools (like Copilot or ChatGPT) securely and reliably

• Give every team confidence in the data they’re working with

Sources & Official References for this blog:

• NIS2 Directive (EU) 2022/2555

  • Official EUR-Lex Text

  • European Commission Info Page

• DORA Regulation (EU) 2022/2554

  • Official EUR-Lex Text

  • European Commission Info Page

• AI Act (EU) 2024/1689

  • Official EUR-Lex Text

  • European Commission Info Page

Real-World Examples of AI & Data Use Cases

Instead of starting from scratch, here are real business examples of how organizations have identified their AI use cases:

1. Payroll Automation in Construction

A leading construction firm found that payroll processing took 14 hours per cycle because:
✔ Data came from three disconnected systems (BrightHR, Sage Payroll, Xero).
✔ Manual data entry & verification steps caused bottlenecks and errors.
✔ Errors required rework & extra validation, slowing everything down.

Verodat automated data integration, cutting payroll processing from 14 hours to just 1 hour while ensuring 100% auditability and compliance.

2. Insurance: Automating Bordereaux Data Processing

An insurance firm was processing bordereaux files manually, dealing with:
✔ Inconsistent data formats across multiple carriers & brokers.
✔ Delays in claims & premium reconciliation.
✔ Compliance risks due to lack of audit trails.

With Verodat:
✅ Data was automatically cleaned & structured, ensuring a standardized format.
✅ AI-driven reconciliation sped up bordereaux processing by weeks.
✅ Compliance was built in, eliminating regulatory risks.

3. HR: Automating Compliance-Ready Workforce Reporting

An engineering company was pulling workforce data manually from multiple systems, causing:
✔ Data inconsistencies across HR & finance systems.
✔ Manual work taking nearly a full day each month to reconcile reporting.
✔ Limited flexibility in Power BI dashboards, requiring constant rework.

With Verodat:
✅ AI-powered rule-building automated data mapping, ensuring real-time accuracy.
✅ A structured, high-performing Snowflake database replaced fragmented spreadsheets.
✅ HR & finance teams gained instant access to clean, governed workforce data.

Your Turn: Define Your Own Use Case

With Verodat, AI adoption isn’t a massive, disruptive overhaul—it’s a strategic, step-by-step transformation.

Start by answering these:
✅ What’s an area in your business that takes too much time?
✅ Does this process involve multiple disconnected systems?
✅ Is this process dependent on stakeholder follow-ups or approvals?
✅ What would happen if this process was automated?

🚀 Ready to Identify Your First AI Use Case?

Don’t let your AI journey stall at the starting line.
With Verodat, you can go from “idea” to AI-ready data in weeks, not months.

✅ We’ll help you define the business problem
✅ Identify and connect the right data sources
✅ Automate everything in a way that’s traceable, governed, and ready for AI tools

👉 Get a Verodat demo for Free and see how simple it can be to start.

The Client:

A virtual insurer fulfilling all core insurance functions — including sales, distribution, pricing, product innovation, claims, analytics, and governance.
With a partner network of over 400 cover holders, the operations team handled hundreds of bordereaux files each month across the market.

By adopting Verodat’s data supply chain management tool, they transformed their data preparation process, achieving:
✅ 94% faster bordereaux processing
✅ Automated data consolidation across systems
✅ Improved transparency & governance

The Challenge:

Each partner submitted bordereaux in different formats, often with inconsistent or incomplete data.
This created significant delays for the operations team, who were responsible for cleaning, formatting, and consolidating files before they could be used for downstream reporting or compliance.

The process:

• Slowed response times

• Introduced data quality issues

• Created operational bottlenecks

• Made real-time insight almost impossible

The Solution:

The insurer adopted Verodat to centralise and automate its bordereaux management. Using Verodat, they were able to:

🚀 End-to-end data preparation in one platform
🔄 Automated data collection, cleansing & transformation
📊 Integrated partner data into downstream systems

Key Features:
✅ Built-in mapping for governance rules & auditability
✅ Data enrichment for smarter business decisions
✅ Self-service automation—reducing IT dependency