Placement Copilot for Commercial Insurance
Helping brokers deliver faster cover & clearer recommendations
Agentiv-x connects clients to cover by translating business needs into presentable risks, and policy terms into explainable advice.
Powered by neuro-symbolic AI, Agentiv-x combines proprietary decision science, developed with the University of Manchester, with agentic AI to support fast, clear market presentations, policy comparisons, and client recommendations.
Platform Process
Decision Science
The broker sets a business’s cover requirements and uploads documentation.
Agentic AI
AI agents extract and match information from sources against client needs.
Expert Control
The broker reviews and refines recommendations in real time, optimising outputs.
Where Agentiv-x Supports Brokers
Agentiv-Handler
Helps brokers present client requirements, compare insurer terms, and explain trade-offs, getting clients on the right cover faster.
Agentiv-Claims
Helps brokers assess claims submissions, highlight settlement triggers, and relay them to the insurer, securing a better claims experience.
Placing complex risks is too slow and too difficult
Long submissions, ambiguous wording, repeated follow-up, rekeying, and reporting make it harder for brokers to align cover with client needs.
Harder to demonstrate value
When cover is difficult to assess and explain, conversations default to price rather than advice.
Consumes time you don't have
Manually reviewing documents, rekeying information, and comparing terms resulting in operational friction.
Increases E&O risk
Lengthy submissions, ambiguous wording, and poor traceability increase the likelihood of errors and omissions.
Changing the game
Agentiv-x brings AI and decision science together to scale professional judgement across complex commercial placements, producing recommendations that are fully explainable, consistent, and shaped by expert reasoning.
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A Better Solution
Agentiv-x captures how the best brokers weigh evidence and navigate uncertainty, supporting recommendations that are fast, clear, and justifiable when challenged.
Clearer Recommendations
Structures market responses against business needs, so options are easier to compare, explain, and recommend with confidence.
Faster Placement
Carries client requirements through the placement process, reducing rekeying and back-and-forth from initial fact-find to final recommendation.
Fewer E&Os
Links every recommendation back to client submissions, creating an evidence-linked record of why and how the advice was formed.
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.