AI introduces a new consumer of your data

AI is often discussed as though it creates an entirely new data problem.

In many ways, it doesn’t.

Organisations have always needed to collect information from different sources, understand what it means, check its quality and make it available to the right resource.

Historically, that resource might have been an employee making a decision, a reporting system producing management information, or a third party receiving data.

Now it can also be AI.

The technology consuming the information may have changed significantly. The need to understand and control the information being supplied to it has not.

This is particularly important as organisations move beyond experimentation and begin considering how AI can support real operational work.

The question isn’t simply whether an AI model is capable of performing a task.

It’s whether the organisation can reliably supply it with the information required to perform that task — and understand what happened along the way.

Accurate information can still be wrong for the decision

Data quality matters. But accuracy alone isn’t enough.

A piece of information can be completely accurate and still be the wrong information to use.

It might be:

• the wrong version;
• out of date;
• missing important context;
• incomplete for the task; or
• originally collected for an entirely different purpose.

For a person making a decision, experience and judgement can sometimes expose those gaps.

When information is supplied automatically to AI, organisations need to think deliberately about how that context travels with it.

That means understanding not just what the data says, but where it came from, how current it is, what happened to it along the way and whether it is appropriate for the task being performed.

This is where provenance and governance become fundamental parts of an AI strategy rather than separate data-management concerns.

The better question isn’t simply “Is our data accurate?” It is “Can we trust this information for this particular use?”

What insurance has taught us about trusted information

Verodat has built deep expertise in the insurance market, working with leading organisations to move and govern complex data.

Insurance is a useful environment in which to learn these lessons.

Information often arrives from multiple organisations, in different structures and at different times. It can change as it moves through a process. Decisions depend not simply on having data available, but on understanding its quality, context and provenance.

The answer isn’t to remove people from the process.

It’s to create a better information supply around them.

That means making it easier to understand where information came from, applying appropriate controls, identifying issues and making trusted information available to the people and systems that need it.

As AI becomes another consumer of that information, those same principles become increasingly relevant.

Different industries will have different use cases, responsibilities and requirements. The application of AI in an insurance workflow is not the same as its application in healthcare.

But the underlying information challenge travels surprisingly well:

How do you get the right information to the right resource, with the right controls around it?

Governance becomes more important, not less

As AI takes on more work, there can be a temptation to focus primarily on what the technology can automate.

But greater automation also increases the importance of understanding the boundaries around it.

Who can access particular information?

What information can an AI use for a particular task?

What happens when something is missing or uncertain?

Where does human judgement remain essential?

Who owns the workflow?

And can someone understand what happened and intervene when necessary?

Governance doesn’t have to mean slowing innovation down. Done well, it creates the conditions in which organisations can use new technology with greater clarity and control.

The objective shouldn’t be to ask people simply to trust AI.

It should be to give them reasons to trust how it is being used.

The technology will keep changing. The fundamentals won’t.

The ways organisations use AI will continue to develop quickly.

Some use cases will prove genuinely valuable. Others won’t. New models, tools and capabilities will continue to emerge.

That makes it tempting to build an AI strategy around the technology itself.

But underneath it sits something much more enduring: the information an organisation already depends on.

Can you find it?

Can you understand it?

Can you trust it for the task?

Can you trace where it came from?

Can you control how it is used?

Those questions mattered before generative AI. They matter today. And they will still matter as the technology changes again.

AI may change what organisations can do with their data.

It doesn’t change what they need from it.

From AI experimentation to operational deployment

Insurance organisations are under increasing pressure to modernise highly manual processes while maintaining strict standards around governance, auditability and compliance.

Bordereaux management is a particularly clear example.

Insurers and MGAs receive large volumes of risk, premium and claims data from third parties, often in inconsistent formats and structures. Before that information can support reporting, analysis or decision-making, teams can spend significant amounts of time manually mapping, validating, reconciling and preparing it.

AI has enormous potential to change this.

But giving an AI model access to fragmented or poorly governed enterprise data doesn’t solve the underlying problem. AI needs a reliable supply of structured, trusted and governed information if it is going to operate safely in production.

That is the problem Verodat was built to solve.

The work recognised by the judges

Our submission demonstrated how Verodat has been used to establish a governed data layer for insurance operations — transforming fragmented bordereaux data into structured, validated and traceable information that can support automation, reporting and AI.

The results included:

• 94% reduction in processing time for complex bordereaux workflows
• 80%+ reduction in manual reporting effort
• 114 unique data mappings completed in under four weeks, turning years of legacy bordereaux into structured, analysis-ready data
• 36 historical files across three coverholders reprocessed in under one minute following a mapping correction
• 100% row-level audit traceability
• Production deployment achieved within a three-week compliance sign-off window

These weren’t results from a standalone AI demonstration. The capability was deployed into live, business-as-usual insurance operations.

A different approach to AI in insurance

For us, that distinction matters.

The conversation around enterprise AI often starts with the model. We believe it needs to start with the information being supplied to it.

Verodat creates a controlled layer between enterprise data and the tools, reporting environments and AI agents that need to use it. Data can be mapped, validated, governed and traced before it reaches the point of consumption.

That makes it possible to introduce automation without sacrificing the controls required in a regulated environment.

And the same foundation doesn’t only support AI. It gives insurance teams cleaner operational data for reporting, analysis, governance and decision-making today.

Watch: Verodat’s approach to Reporting

Get the Data Right First

Verodat creates a consistent data foundation across risk, premium and claims bordereaux.

The platform:

• Standardises bordereaux data from different cover holders into a consistent structure
• Validates data against your business rules before it reaches downstream systems
• Consolidates clean data in your SQL Server or Snowflake environment
• Maintains traceability back to the underlying data
• Makes validated data available for reporting in tools such as Power BI

Instead of repeatedly cleaning and reconciling data within individual reports, the work happens upstream.

The result is a consistent source of trusted bordereaux data that can be used across your reporting environment.

One Data Foundation. Different Reporting Needs.

With clean, structured bordereaux data in place, teams can use it for different purposes.

Performance reporting can provide visibility across risk, premium and claims, including portfolio performance, loss ratios and trends over time.

Operational reporting can help teams understand the bordereaux process itself — from submission performance to recurring data quality issues across cover holders.

Both start with the same thing: reliable underlying data.

Work with the Reporting Tools You Already Use

Verodat doesn’t require insurers to replace their existing reporting tools.

Instead, it prepares and governs the data that feeds them.

Clean, validated bordereaux data can be delivered into your existing warehouse and used by Power BI and other downstream tools — allowing teams to continue working in familiar reporting environments while improving the quality of the data underneath them.

Better reporting starts with better data.

From Data Capture to Data Control

For years, organisations invested heavily in capturing and storing data. They built warehouses. They centralised systems. They expanded data lakes.

But storing data and supplying it effectively are two very different things.

As environments grew more complex, new friction appeared. Data updated on different schedules. Formats varied across systems. Traceability was limited. Teams spent hours preparing data before it could be reported on. And governance concerns meant automation initiatives stalled before they started.

The result was familiar to anyone who’s lived it: massive volumes of data, but continued operational drag. Teams building workarounds in Power BI because the tools they were paying for didn’t deliver what they needed. Individuals becoming single points of failure because they were the only ones who knew how to reconcile the data.

Now, as AI becomes embedded in business processes, this gap has become impossible to ignore. AI tools don’t compensate for fragmented, unstructured, or poorly governed information supply — they amplify the problem.

The question has shifted. It’s no longer about how much data an organisation holds. It’s about how reliably that data reaches the process that needs it, in the right condition, at the right time.

The Emergence of Data Supply Management

A way of thinking about this is starting to take hold across organisations that are serious about operational AI: Data Supply Management.

Rather than focusing purely on storage, it treats data the way a supply chain treats materials. How is information defined? How is it validated? How is it updated, governed, and supplied to specific business processes?

For executives, the analogy is straightforward. In a physical supply chain, success depends on the right materials arriving at the right place at the right time, with quality controls in place. Information works the same way.

When we’ve seen organisations get this right — and we’ve worked with carriers, MGAs, and teams across financial services and construction who have — the impact is tangible. Processes that were manual become repeatable. Reporting moves from retrospective to real-time. Automation becomes reliable because the data feeding it is governed. And compliance stops being a bottleneck because auditability is built into the flow, not bolted on afterwards.

When data supply isn’t structured, the opposite holds. Teams remain dependent on workarounds. Manual reconciliation eats capacity. And every new AI initiative runs into the same foundational problem: the data isn’t ready.

Unlocking AI Without Losing Control

There’s growing pressure to “activate AI” across industries. But AI doesn’t operationalise itself.

Without structured data supply, models return inconsistent outputs. Compliance teams hesitate. Engineering teams spend their time rewriting queries instead of building. Trust in automation erodes — not because the AI doesn’t work, but because the information feeding it isn’t reliable.

AI in production requires more than access to data. It requires clarity around what information a system can access, under what conditions, with what level of traceability, and with what validation controls in place.

In regulated sectors — insurance, financial services, construction — this isn’t optional. We’ve seen teams attempt to deploy AI on top of fragmented data and the pattern is consistent: the technology works, but the outputs can’t be trusted because the inputs aren’t governed. That’s not an AI problem. It’s a data supply problem.

The organisations moving fastest with AI aren’t the ones experimenting most aggressively. They’re the ones that have stabilised how information moves through their business first.

How Verodat Fits

Our focus isn’t on replacing your systems. It’s on establishing the structured layer that governs how information flows between them.

We start with a clearly defined business challenge — not a technology roadmap. From there, we define only the information required to solve it and build a repeatable, governed supply chain for that data. We call this our Lean Data Management approach: start small, prove value fast, then expand.

In practice, that means faster time to value because we’re not boiling the ocean. It means reduced operational friction because teams stop building workarounds. It means clear traceability and audit readiness built into the process, not added as an afterthought. And it means AI systems that operate with confidence because the data feeding them has been validated and governed from the start.

We’ve seen this work across carriers in the Lloyd’s market, MGAs managing complex delegated authority structures, and organisations in financial services and construction dealing with similar data supply challenges. The pattern is the same: once the foundations are in place, what felt impossible — real-time reporting, reliable automation, production AI — becomes operational.

AI isn’t a future ambition. It’s operational — when the foundations are in place.

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’ve shared more on how we use Replay with insurance clients — read the example 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.