OpenNashPrepared for Krishnaswami Rajagopalan

Krishnaswami Rajagopalan / Automation and AI Solutions Architect / Liva Insurance Oman

We did the homework
on Liva Insurance Oman.

You do not need an AI pep talk. We found three Liva operating queues where OpenNash can act as a senior build bench and take execution work off your plate.

Your profile claims 2M+ OMR saved and 72 percent TAT reduction
Liva exposes motor quote, renewal, claim, document, and WhatsApp self-service surfaces
Public careers are visible, but role details were not exposed during research
Where it comes fromestimate
Claims intake prep18-28 hrs/mo
Renewal and quote follow-up16-26 hrs/mo
Document queue cleanup16-26 hrs/mo
Three specific painsS.02

Three places we would start.

Pick one workflow. We automate the prep work for 14 days and show whether it can delay a hire, reduce rework, or move people to higher-value queues.

Pain 01 / Claims intake

Claim intake should arrive cleaner.

Problem

Liva customers can initiate and follow claims across digital and WhatsApp-style service surfaces.

Solution

We read claim inputs, check missing documents, prepare the summary, and route exceptions before adjusters touch the queue.

Pain 02 / Renewals

Renewal and quote follow-up should not depend on manual chasing.

Problem

Motor quote, renewal, and policy-service surfaces create repetitive status checks and customer follow-ups.

Solution

We monitor pending renewals, draft follow-ups, update trackers, and flag cases that need human review.

Pain 03 / Documents

Insurance documents create hidden rework.

Problem

Policy documents, service requests, claim files, and customer submissions create repeated checking work.

Solution

We classify documents, match them to policy or claim records, flag gaps, and log the next action.

Kris, give us 30 minutes.

Bring one Liva Insurance Oman queue your team would rather stop babysitting. We will make it worth your time with the automation map, hire-pressure math, and a 14-day no-charge start.