AI for Insurance
Most of a claim is still handled by hand.
AI can change that, if it is built for insurance.
We work with insurers and health plans on claims, documents, and payer operations.
The data is messy, the compliance requirements are real, and the systems have to hold up in production.
Claims operations
What the work involves today
The handling is the cost
Claims handling costs more than it should. Document review, prior auth, keying, and appeals add up on every claim.
Volume keeps rising. Headcount hasn't.
The queue is already too long. Adding adjusters has helped at the margins, but it has not fixed it.
Slow claims cost renewals
A claim that takes 14 days instead of 4 does not show up as an error. It shows up as a member who does not renew.
Audit pressure never eases
Every decision has to be traceable and defensible, at any volume.
Built for insurance
A lot of this can be automated. But not with generic AI.
A lot of claims and document work is a good fit for AI. But off-the-shelf tools do not hold up in a real claims environment. The work has to be built for insurance.
Custom, not off-the-shelf
Shaped around your claims, documents, and processes. Not a generic template adapted for insurance.
Built for messy, regulated data
The records are inconsistent and the rules are strict. The system has to handle both.
Auditing and guardrails built in
Every output has to be traceable. Compliance requirements are not optional.
Human review built in
People stay in the loop on the decisions that matter.
Where we focus
What we can help with
The first step is usually not building. It is working out which claims or document AI opportunity is genuinely ready, what the path to production looks like, and where the constraints are.
Claims and member operations
Prior auth, appeals, routing, and member queries — the repetitive, document-heavy work that grows with claim volume.
AI to automate decisions, reduce handling time, and keep every output traceable.
Document understanding and NLP
Extracting structured information from clinical notes, EOBs, policy documents, and correspondence.
Turning unstructured content into reliable inputs for claims, compliance, and operational decisions.
AI that holds up in production
Getting AI through the governance, audit, and compliance requirements of a real payer.
Strategic guidance, implementation, or both, depending on where you are.
Sector delivery experience
Led by someone who ran AI inside a Fortune 5 health insurer
Clearlead is led by Paul Ferguson, former Director of Data Science at UnitedHealth Group. He spent 8 years at UHG delivering AI and machine learning at scale, inside the governance, production, and compliance environment of a Fortune 5 payer.
That background is directly relevant here. He knows how claims operations work, what production AI actually requires inside a regulated payer, and where these projects stall in large insurance organisations. That is the experience he brings, not borrowed domain knowledge.
You work directly with Paul from the first conversation. No handoffs after the sale, no junior team handling the work behind the scenes.
Testimonials
What clients and colleagues say
Paul is exceptional. We've been so lucky to have him work with us on our health insights engine the past few months. His communication skills are fantastic, he works quickly and thoroughly. We had a number of changes to the scope and plan and he adjusted well to accommodate us.
Angela Edwards
CEO and Founder, Elli Cares
Paul was brought in as an external AI consultant with expertise in search systems to provide guidance on a project to implement an AI-driven supplier sourcing system. As part of this he also built a supplier data enrichment system. I was very happy with Paul's contribution to the project and would have no hesitation in recommending him.
Bill Watts
Managing Director, Eagle Exports
Paul is a great person to work with, and I wouldn't hesitate to recommend him for any role or project. It is rare to find someone who excels in technical expertise as well as interpersonal skills. I have no doubt that all his clients will benefit from his deep technical domain knowledge, his attention to detail and his personal touch.
Peter Cogan
VP Data Science, Optum (UnitedHealth Group)
Paul came into [Qualcomm] to work on the flagship technical capabilities of the company and, in that role, rapidly became a leading light, sharing his broad knowledge and insight with the product team and having direct influence on the roadmap. Diligent and intelligent, he was a key player in the team and a pleasure to work with.
Andrew Pegum
Director of Engineering, Qualcomm
Let's Talk
Ready to talk?
A free 30-minute intro call to talk through the claims, document, or payer operations work you have in mind.