Watch: Verodat’s Bulk Reprocessing Feature:

Want the full picture?

If you’d like a complete feature walkthrough, Aaron, our Head of Product, demos everything below.

The problem with fixing data errors today

If you manage large volumes of bordereaux data, you’ll know this feeling. You’ve been processing files for months — thousands of uploads, millions of rows now sitting in your data warehouse. And then during a routine review you spot it. A mapping error. Net Premium recorded as Gross Premium across a whole batch of cover holders. It’s not one file. It’s dozens. And all of that data, already in your warehouse, is wrong.

In most platforms, that means a consultant call, manual rework across every affected file, and days of waiting — at a cost. Every single time. And the frustrating part is it’s never a once-off. Mappings change. Fields get added. Data that’s already in your warehouse needs correcting. It just keeps happening, and the fees keep adding up.

What bulk file reprocessing does differently

Verodat’s bulk file reprocessing lets your team take control of that process entirely. Rather than sending errors out to be fixed externally, you can update your mapping rules and validation logic inside the platform and reprocess your entire batch automatically — hundreds or thousands of files — in minutes. No consultant, no reprocessing charge, no waiting.

In Aaron’s demo above, you’ll see him load up 36 files across three cover holders, identify where an incorrect mapping has been applied, correct it, add a new column across the full dataset, and reprocess the entire batch in under a minute. What would typically mean days of rework and consultant fees is a one-minute job in Verodat.

Why this matters commercially

For delegated authority teams and MGAs handling large bordereaux volumes, the commercial impact is significant. Every batch correction that previously required external support can now be handled internally, immediately, at no extra cost. When you multiply that across every correction your team makes in a year, you’re looking at a very significant return on your investment, very quickly.

It also means that as your data requirements evolve — new carriers, new formats, new validation rules — you’re not dependent on external support every time something changes. Your team can adapt and reprocess independently, keeping your data clean and your reporting accurate without the overhead.

Watch: What Migration Actually Looks Like

The cost of staying where you are

Every quarter spent on a legacy platform is a quarter of manual workarounds, slow reporting, and data your underwriters can’t fully trust. Teams build processes around the platform’s limitations rather than building capability. Innovation stalls not because of a lack of ambition, but because the data infrastructure can’t support it.

The irony is that most teams know this. The platform isn’t working. The decision to move just never feels urgent enough — until something breaks, or a competitor moves faster, or a client asks a question the data can’t answer.

The assumption: migration is an 18-month project

The reputation is understandable. Legacy platform transitions have historically meant complex data mapping projects, large IT resource allocation, parallel running costs, and months of uncertainty. For teams already stretched, that’s a hard sell internally.

So the calculation becomes: stick with what we have, because the alternative feels worse.

But that calculation is based on an outdated version of what migration actually involves.

The reality: a POC in three weeks, using your own data

What we do is start small and controlled. A three-week proof of concept using your real bordereaux files — not sample data, not a demo environment. Your files, your rules, your validation requirements.

Our team handles the setup. You see how your data performs in the platform, where the gaps are, and what the output quality looks like — before any commitment is made. There’s no big-bang transition and no moment where everything is at risk at once.

From there, teams typically run Verodat in parallel with their existing process for a period, so live operations are never exposed to risk, before making a full transition when they’re ready and confident.

What actually takes time — and what doesn’t

The parts of migration that take longest are usually data quality issues that already exist. Legacy bordereaux with years of inconsistent formatting, incomplete records, or manual workarounds baked in. Those things take time to work through regardless of which platform you move to — and they’re costing you right now, whether you migrate or not.

What Verodat changes is the visibility. Because the platform validates data as it comes in, teams can see exactly where the issues are and why — rather than discovering them six months into a project when something breaks downstream.

The conversation worth having

If you’re managing bordereaux on a platform that isn’t working for you — or across a combination of spreadsheets and manual processes — the real question isn’t whether you can afford to migrate. It’s whether you can afford to keep waiting.

In most cases, when we walk through what a POC actually involves, the response is: “That’s much more manageable than we expected.”

We’re happy to have that conversation. No lengthy sales process — just an honest discussion about what migration looks like for your specific situation.

What We Hear About Building In-House

Technical leaders understand the appeal of custom development better than anyone. Full control over the stack. No compromises on architecture. Built exactly for your requirements.

But they also see the true cost.

• A 24-48 month build means technology decisions made today become legacy before launch.
• Every integration is custom—no leverage from how others have solved similar problems.
• And the ongoing maintenance burden pulls your strongest engineers away from innovation.

“My team wanted to build,” one Head of Delivery told us. “Eighteen months in, they were begging me to find an alternative.”

What We Hear About Legacy Platforms

The buy option should solve these problems. Let someone else handle the infrastructure while your team focuses on higher-value work.

But technical teams tell us legacy platforms create different constraints. Integration architectures designed in the pre-cloud era. APIs that feel like afterthoughts. Data flows that go one direction—in—with extraction requiring custom work or vendor services.

“I evaluated three major platforms,” a Data Architect shared. “None of them could give me a straight answer on how I’d get data back out.”

The worst part: annual release cycles that mean waiting 12 months for a feature your team could build in a sprint. Your roadmap runs at the vendor’s pace, not yours.

What Technical Teams Are Actually Asking For

The requirements we hear are consistent:

• Integration-first architecture. Not bolted-on APIs—genuine cloud-native design where integration is a core capability, not a feature. The platform should fit into your ecosystem, not demand that everything routes through it.
• Bidirectional data flow. Data moves in, transforms, and exports wherever it’s needed. Your data warehouse, your analytics tools, your downstream systems. No extraction projects, no data hostage situations.
• Continuous deployment. Features ship weekly, not annually. When the market or your business needs something, it shouldn’t require a vendor negotiation and a 12-month wait.
• AI-ready infrastructure. Machine learning and advanced analytics shouldn’t require rebuilding your data layer. The architecture should support these capabilities natively.
• No permanent IT allocation. Your engineers should work on strategic initiatives, not bordereaux system maintenance.

How We’ve Responded

Verodat was built by a technical team that understood these requirements because we’d lived them ourselves.

We’re a modern bordereaux and data management platform with cloud-native, integration-first architecture. Microservices design means we deploy continuously—52x more frequently than legacy platforms with their annual release cycles. Bidirectional data flow is a core design principle, not a roadmap item. And our warehouse separation model means your data never rests with us—it stays in infrastructure you control.

Production-ready in weeks, not years. 94% faster than custom development timelines.

If you’re evaluating options and the build vs buy choice feels like picking between two kinds of compromise, we’d welcome a technical conversation.

Watch How Verodat Lets You Get Value From Your Historical Bordereaux — Without the Effort or the Hidden Costs:

Why Historical Backloading Matters

Every insurer and MGA knows the struggle of historical bordereaux.
Years of files stored across shared drives, inconsistent formats, and outdated mapping logic make it nearly impossible to run meaningful analysis or AI models. Yet this historical data holds enormous value — it reveals performance trends, informs underwriting decisions, and strengthens regulatory reporting.

The challenge isn’t intent or technology. It’s scale and complexity. Thousands of files from hundreds of coverholders, each with its own format and quirks. Without a structured, governed process, backloading becomes a never-ending manual task.

What Verodat Does

Verodat turns that problem into a process.
We combine deep Lloyd’s market expertise with AI-powered software to transform historical bordereaux into clean, analysis-ready data — with full governance and auditability built in.

Our clients use Verodat to:

• Accelerate mapping creation and data standardisation across years of legacy files

• Apply AI-assisted field mapping that learns and improves with each iteration

• Deliver a fully auditable, MI-ready dataset to internal systems or warehouses such as SQL or Snowflake

• Free their internal teams from rework while maintaining complete transparency and control

How We Do It

Our process is proven, transparent, and designed for scale.

1. Requirements Capture
   We start by documenting the target state: how your data needs to look to meet Lloyd’s and internal reporting standards. This includes business context, validation rules, and version control.

2. AI-Assisted Mapping
   Verodat’s AI automatically aligns fields based on industry expertise and historic patterns, speeding up the mapping process and reducing manual effort.

3. Iterative Review & Collaborative Refinement
   Each mapping is reviewed through structured workflows and error reports. Your team retains oversight, while we handle the heavy lifting — transforming thousands of files at pace.

4. Full Audit Trail & Traceability
   Every transformation is tracked, enabling complete transparency for compliance, audit, and data-quality assurance.

This approach removes maximum pain from a complex process, while maintaining the quality control your team needs

Why Verodat Is Different

Verodat’s advantage lies in its blend of AI automation, Lloyd’s expertise, and disciplined methodology:

• Experience: Multiple backloading projects completed for leading carriers, underpinned by deep knowledge of Lloyd’s frameworks.

• Efficiency: AI-accelerated mapping supervised by human data experts.

• Methodology: A tranche-based model that learns from each iteration to improve accuracy and speed.

• Transparency: Real-time progress tracking, burn-down charts, and comprehensive audit logs.

• Speed: Rapid turnaround on initial mappings, with feedback loops built for continuous improvement.

As Ella Copeland, Customer Success Lead at Verodat, explained, “It’s not about making the process invisible — it’s about taking as much pain out of it as possible. Our way of working gives clients confidence in the results while keeping them in control.”

Proven Success

In a recent large-scale project, Verodat processed 114 mappings in under four weeks, handling thousands of files across multiple years of account. The project demonstrated Verodat’s ability to manage complex, multi-format historical data — transforming it into a unified, high-quality dataset ready for reporting and analytics.

Clients consistently report dramatic improvements:

• 94% faster processing times compared with manual methods

• 100% audit traceability across all transformations

• Significant reduction in rework and operational overhead

The Bigger Picture

Historical backloading is more than a cleanup exercise — it’s a foundation for transformation.
Once legacy bordereaux are structured and standardised, insurers can:

• Run cross-year performance analysis

• Power AI tools safely and effectively

• Simplify renewals and compliance submissions

• Identify risk and pricing trends hidden in years of disconnected data

Verodat makes this achievable — not as a one-off project, but as a repeatable, governed process that scales with your business.

A Platform Designed for Continuous Innovation

From day one, Verodat’s development framework was structured to deploy production-grade features in short, controlled sprints.
Our standard turnaround times:

• Small configuration changes: usually within two weeks

• Minor feature updates: typically four to six weeks

• Larger product launches: phased, but always with measurable progress visible to customers

This approach has allowed Verodat to sustain a constant stream of innovation, led by real business requirements rather than abstract R&D roadmaps.

Example: The Replay Capability

One of our most-requested capabilities this year came from a simple but powerful customer insight:

“What if I could replay my data back through Verodat without starting from scratch?”

In less than eight weeks, that idea became Replay — a feature that enables customers to re-run historical data through their Verodat validation and transformation pipelines.
It’s a breakthrough for backloading — dramatically reducing time and effort when cleansing or onboarding legacy data.

Replay exemplifies what we do best:

• Listen closely to how clients actually work

• Build iteratively and deploy fast

• Deliver measurable operational benefit — in this case, accelerating backloading and ensuring data remains fully audit-ready

(We’ll share a deeper look at Replay soon — see more about this feature here →)

Customer-Centric by Design

Our innovation process starts where it should — with the customer.
Every feature we ship is linked to a real-world need surfaced during onboarding sessions, support tickets, or data governance reviews.
This constant feedback loop means our roadmap evolves with the market, not ahead of it.
And because Verodat is built as a lightweight, modular layer, new features can go from design to live production within weeks, without breaking existing configurations.

Why Speed Matters

Speed of innovation isn’t just about convenience — it’s about competitive advantage.
For our customers, every week saved in automating a process or unlocking a data source translates to lower cost, higher confidence, and faster ROI.
In an environment where others are still planning their next release, Verodat clients are already using the next capability.

Looking Ahead

The pace of innovation at Verodat isn’t slowing down.
Upcoming releases continue to expand on automation, replay, and AI-ready data orchestration — all guided by the same principle that built Replay: listen, build, deliver.

While others prepare for AI, Verodat powers it — securely, at scale, and in production.

From Data Capture to Data Supply

Historically, organisations have focused on capturing data — not on supplying it effectively to the processes that need it.
The result: vast data lakes and warehouses filled with information that’s difficult to use, manage, and trust. As data volumes have exploded, the challenge of turning raw data into usable input has only grown.

Today, that focus is shifting.
The rise of AI in business processes has made data supply — not just data management — a board-level priority.
AI delivers transformative results only when powered by data with the right quality, context, and compliance built in.

The Rise of Data Supply Management

A new category of tools is emerging to meet this need: data supply management.
These lightweight, fast-to-integrate systems manage how data is supplied to each process — ensuring reliability, traceability, and control from source to use.

For executives, this approach is particularly intuitive.
Data supply mirrors something they already understand — the supply chain.
Just as physical supply chains ensure the right materials reach the right place at the right time, data supply management ensures the right data reaches the right process in the right condition.

This familiar framing allows executives to:

• Understand what needs to happen

• Interrogate how it’s being done

• Govern and control outcomes

In short, it builds on existing leadership skills — enabling operational discipline and governance to extend naturally into the data and AI domain.

Empowering Executives to Unlock AI Innovation

This new approach acts as a release valve for the pressure executives face to “deliver on AI.”
By framing data through the familiar lens of supply-chain management, leaders can confidently oversee, question, and direct their organisation’s digital transformation — not from the sidelines, but from the centre.

It creates a shared language between data teams and leadership, grounding AI adoption in something every executive already understands: supply, demand, and flow.

Verodat’s Role

At Verodat, this is our domain.
For over a decade, we’ve been building and deploying data supply chain tools across highly regulated industries — from insurance and aircraft leasing to operations, finance, HR, and product.

We help executives bring the same discipline and control to data supply that they’ve already mastered in physical supply chains.
Our technology provides the structure and governance needed to move from AI experimentation to AI-driven operations — safely, efficiently, and at scale.

Because when the data supply works, everything else does too.

A Collaborative Approach

Verodat worked closely with Mercury’s data and business applications teams to deliver a solution tailored to the company’s operational and compliance requirements. The collaboration was characterised by transparency, adaptability, and a shared focus on long-term value.

What the Project Involved
Verodat was deployed to centralise data flows across Mercury’s finance, project management, and reporting systems. The platform automated validation and consolidation of critical operational datasets, replacing manual data preparation with a governed supply chain that feeds directly into Mercury’s analytics environment. Within weeks, the company moved from fragmented data updates to a single, real-time source of truth powering executive dashboards and performance reporting.

Delivering Results

With Verodat, Mercury now benefits from:

• Enhanced data quality and governance, reducing manual intervention and improving trust in decision-making.

• Faster, more reliable insights across finance, operations, and project management.

• Improved system performance, enabling AI and analytics tools to operate at full potential.

• A scalable foundation that supports ongoing innovation and automation initiatives.

This collaboration demonstrates how enterprise-scale organisations can turn complex, fragmented data environments into agile, governed ecosystems that drive better business outcomes.

⚙️ About Verodat

Verodat enables organisations to turn their existing systems into AI-ready data infrastructure—connecting, validating, and governing information so it’s always reliable, compliant, and actionable.

The problem with data that isn’t ready

Data sits in multiple systems, is updated inconsistently, and lacks the contextual metadata AI agents need to interpret it correctly. Even the most advanced models struggle in that environment — returning incomplete, inaccurate, or non-compliant outputs.

The result? Projects stall, governance teams lose confidence, and automation never makes it beyond pilot.

As Thomas explained on the podcast, “You can’t scale what you can’t trust. AI doesn’t fail because of the model — it fails because of the data.”

From Experimentation to Execution

Moving from AI experiments to production isn’t about chasing the next model or tool.
It’s about creating an operational environment that gives AI the structure and guardrails it needs to perform reliably.

That means:

• A consistent way to describe and access business data.

• Context around what information means and where it comes from.

• Governance that defines what agents can query and under what conditions.

• Transparency and auditability so decisions can be trusted and explained.

This layer of control is what allows AI to be used in production, safely and repeatably.

How Verodat Makes It Possible

Verodat is built for exactly this challenge.
It acts as the control layer between enterprise data and AI agents, turning automation from pilot to production.

Our platform:

• Connects to existing systems without requiring infrastructure change.

• Enriches and validates data automatically, attaching supply and provenance metadata.

• Provides governance and access rules for both human and AI interactions.

• Enables traceable, audit-ready automation across workflows.

This allows enterprises to deploy AI securely, deliver measurable ROI, and scale automation with confidence.

How Verodat Powers Bordereaux Agent Workflows

Bordereaux management is changing fast.
Insurers and MGAs are under pressure to process data faster, maintain governance, and deliver real-time insights to underwriters, compliance teams, and executives.
For many, this shift is being driven by AI — but the biggest challenge isn’t the technology itself.
It’s whether the data behind it is ready.

That’s where Verodat comes in.

From Manual Files to Governed, AI-Ready Data

Verodat is a data-first platform that automates, validates, and governs bordereaux data across risk, premium, and claims.
Our Bordereaux Management solution transforms fragmented spreadsheets and PDFs into clean, structured, and compliant datasets — without the heavy engineering lift.

Within weeks, Verodat replaces repetitive manual processing with a governed data supply chain that:

• Validates every row automatically against business and contract rules.

• Flags and fixes data quality issues before they hit your team.

• Provides full audit trails and MI-ready outputs.

• Integrates directly into your SQL, Snowflake, or reporting layer.

It’s fast to deploy, fully supported, and instantly usable by business users — no developers or re-platforming required.

Once Your Bordereaux Is Organised — It Doesn’t Stop There

With your data governed and structured by Verodat, you’ve built more than just a reporting pipeline.
You’ve created the foundation for automation — a data layer that can safely power AI-driven workflows across your delegated authority operations.

This is where Verodat’s AI enablement and the ADRI (Agent Data Readiness Index) come in.

Introducing ADRI — The Confidence Layer for Agents

The ADRI (Agent Data Readiness Index) measures and enforces the readiness of data before any AI agent acts on it.
In other words, it ensures that every AI-driven action — whether it’s summarising bordereaux files, generating performance briefings, or forecasting risk exposure — is based on verified, complete, and up-to-date data.

Together, Verodat’s Data Supply Manager and ADRI framework make it possible to safely build and deploy agent workflows on top of your existing bordereaux data, with full visibility and control.

Examples of Bordereaux Agents You Can Activate

Once your bordereaux data sits within Verodat’s governed layer, new possibilities open immediately.
Here are just a few examples of agent workflows our clients are exploring today:

• Underwriter Summary Agent
  Automatically creates a daily or weekly briefing for underwriters — highlighting performance by coverholder, loss ratios, and exposure shifts.

• Exception Monitoring Agent
  Flags rule breaches (e.g. expired policies, incorrect premiums, missing claim references) in real time and alerts the operations team for review.

• Renewal Performance Agent
  Tracks policy renewals and aggregates loss and profitability trends, sending proactive insights to account managers.

• Portfolio Forecasting Agent
  Uses validated bordereaux data combined with external data sources (e.g. market rates, FX trends) to model upcoming exposure and capital requirements.

• Audit-Ready Reporting Agent
  Generates regulator-ready or internal audit reports automatically, with full traceability from file upload to output.

Each of these agents operates securely and reliably because Verodat provides the data supply, governance, and audit logic behind them.

Why It’s So Easy with Verodat

Implementing automation in bordereaux doesn’t have to mean building from scratch or hiring data engineers.
Verodat was designed to fit around how insurers and MGAs already work.
Your team defines the business logic; Verodat handles the technical lift.

• Pre-built connectors for all major file formats and systems.

• Weekly working sessions during setup — no complex onboarding.

• Verodat Logging built in, so every action is observable and verifiable.

• Expandable design — start with bordereaux, then extend to claims, pricing, or MI reporting when you’re ready.

In short: Verodat gives you immediate ROI today, and the foundation for AI tomorrow.

The Opportunity for Delegated Authority

The shift toward agent-based automation is already underway.
As organisations like CJ Coleman and Fenchurch look to operationalise their AI strategies, the ability to trust data will define who moves first — and who moves fastest.

Verodat ensures your bordereaux data is structured, governed, and ready for that future.
Whether you want to improve operational efficiency, reduce reporting cycles, or deploy intelligent agents, Verodat provides the foundation to make it real — safely and at scale.