Claims Copilot for faster, more consistent claims decisions
Using Agentic AI to reduce handling time, improve evidence gathering and reach faster settlement
Agentiv-x reduces claims handling touch points by improving evidence gathering, claims triage and coverage analysis which helps reduce cost per claim and speed up settlement
Powered by neuro-symbolic AI, Agentiv-x combines proprietary decision science, developed with the University of Manchester, with agentic AI to enhance FNOL with fast, consistent and relevant evidence gathering. It also identifies which missing evidence matters most to the decision and what to ask next. This reduces the need for time consuming and expensive claims handling and leads to better consumer outcomes.
We address
Slow settlement
Long claims lifecycles increase handling cost and customer frustration
Rising leakage
7-14% of claims spend is lost in some form of unnecessary pay out
Poor consumer outcomes
42% of declined home and travel complaints are upheld
Platform Process
Decision Science
The system receives FNOL and uploads policy documentation.
Agentic AI
The system reviews available evidence and claims context to determine next best course of action. This includes identifying which evidence matters next and whether triage, liability or coverage review is required
Expert Control
The handler reviews and refines analysis in real time to triage the claim faster
Where Agentiv-x supports claims teams
Agentiv-Claims Team
Helps claims teams within broker firms, TPAs and insurers to assess the quality of the claim's submissions, gather more relevant evidence at FNOL, support quicker liability and coverage assessments - and triage that information if and where necessary – to secure a better claims experience for policyholders.
Claims settlement is too slow and too costly – on low value claims the cost of claims management can be disproportionate to the cost of indemnity
Poor quality FNOL data containing omissions and/or irrelevant evidence, leads to repeated follow-up, rekeying, and added administration making it harder for claims managers to determine coverage and quickly settle valid claims – this pushes up cost and damages client trust.
Harder to determine liability and coverage
When claims data is incomplete or difficult to evaluate, it can make it harder for claims handlers to know which evidence to collect to determine liability or coverage.
Consumes time you don't have
Manually reviewing claims documents, rekeying information, and comparing against policy wordings results in higher administrative costs and lengthier settlement delays.
Increased reserving
Uncertainty and ambiguity around claims documentation, is likely to lead to unnecessarily high reserves with more capital being tied up for longer.
Changing the game
Agentiv-x brings AI and decision science together to scale professional judgement across the claims management process. We help to improve evidence gathering at the start of the claim to make sure that the right person is receiving the right information at the earliest possible stage – this cuts down on unnecessary back and forth to greatly reduce the cost of claims.
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A Better Solution
Agentiv-x captures how the best claims managers weigh claims evidence and navigate uncertainty, to support claims decisions that are fast, clear, and defensible.
Gather the right evidence quickly
Our system supports the claims handler to assess the available claims evidence quickly to identify and prioritise any missing evidence needed to support triage, liability and coverage assessments.
Triage the claim to the right person
Our system supports claims handlers in assessing whether and where to triage the claim based on the evidence available compared against the policy documentation.
Reduce touch points between brokers, TPAs and insurers
By ensuring the right evidence is gathered quickly at the time the claim is first notified, our system reduces the likelihood of back-and-forth discussions between policyholders, brokers, and claims handlers reducing the cost and time taken to settle claims.
White Paper
Neuro-Symbolic Technology for Reliable Professional Decision-Making
Discover how Agentiv-x is redefining decision-making across the insurance industry. Our white paper explores the science behind our decision intelligence models and their ability to scale expert judgement across high-stakes, complex environments. Learn how Agentiv-x moves beyond black-box AI to deliver fast, consistent and defensible recommendations.
Leadership Team
Agentiv-x is shaped by deep experience working side-by-side with insurance professionals to improve decision quality and consistency in demanding environments.
Mark Twigg
CEO
A founder and CEO who has successfully exited twice, with 25 years’ experience providing risk monitoring technology to many leading insurance brands. He's driven to help organisations make decisions that support clients, protect their reputation and deliver progress.
Karim Derrick
CPO
Award-winning product leader who has built and deployed neuro-symbolic technologies at scale across the insurance sector. He is driven by a commitment to purposeful innovation, using AI to improve accountability and reduce the social impact of poor decision-making.
Tony Joseph
COO
Experienced operations leader with two decades of experience scaling platforms and delivering AI-enabled decision systems in the insurance industry. He has led distributed technology teams with a clear focus on translating advanced machine intelligence into real-world outcomes.
Rob Agnew
CSO
A strategy leader with over 10 years’ experience applying AI and machine learning in professional advisory settings. He has worked extensively in high-scrutiny environments, where poor decision-making carries real institutional and societal costs.
Our Principles
We believe in safe, ethical and purpose-driven artificial intelligence that recognises the innate value of human expertise and the need to leverage it.
Evidential AI
Recommendations are documented in case-file citations, not back-filled rationales.
AI-in-the-loop
Professional knowledge and experience are scaled, not replaced.
Accountable decisions
Decisions remain expert-owned, through controllable, governed decision logic.
Real-world performance
Performance is measured through tangible business results, not abstract benchmarks.
Decision trail
Decisions are documented in an evidence-linked audit trail, not a chatbot history.