Bordereaux, Data
& AI
Answer six quick questions about your bordereaux data and tooling, and we'll give you instant, tailored insights into how you can use AI to help your BDX processing — plus a perspective we think is worth your time as you answer each question.
The problem isn't what coverholders send — it's what happens when it arrives
No matter how good your downstream platforms are — and they are often very expensive — you're not going to get full value from them unless you sort out the quality of data coming in first. Garbage in, garbage out is as true today as it ever was, and no amount of clever analytics or dashboarding changes that fundamental equation.
Most DA teams are using tools designed for a different era
The majority of BDX processing still relies on tools — Excel, legacy platforms, or general-purpose ETL — that were built before the data complexity of modern delegated authority became what it is today. They can be made to work, but the effort required is almost always manual, repetitive, and fragile. The question isn't whether better tools exist — it's whether the ones you're using are genuinely fit for the problem you have now.
Satisfaction with current tools is rarely the full picture
Organisations often rate their tools as "adequate" because they've adapted their processes around the tool's limitations — not because the tool is actually solving the problem well. The real test is how much invisible manual effort is required to keep things running. If the answer is "quite a lot", the tool probably isn't as fit for purpose as it feels.
Have you considered a tightly controlled AI agent at the front door?
You've probably heard a lot about Large Language Models — the technology behind tools like ChatGPT and Claude. What you may not have been told is how well-suited they are to exactly this kind of problem. Think of it as placing a very precise, very consistent AI agent at the point where data first arrives. It reads bordereaux in almost any format, maps fields to your schema, fills in the gaps sensibly, and flags anything that genuinely needs a human eye — all following a strict set of instructions that you define and control. No guesswork, no hallucination, no black box. And contrary to some of the noise out there, doing this in a safe, auditable, compliant way is far more straightforward than you might have been led to believe.
Want to see this in action?
Optional — upload your BDX template (no data, just the blank format) and we'll send you back an example of how an AI agent normalises messy, real-world data into that exact template. No obligation.
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