AI agents for benefits
brokers and administrators.
Additions, removals and member questions arrive all day, and renewal shows up with thirty days' notice. Labs designs the agent on your enrolment, service and renewal flow, connected to the systems the book already uses.
The book grows and the service load grows with it, in the same proportion.
Every new client brings enrolment changes, duplicate cards, network questions and reimbursement chasing. That work does not scale with people, and it decides whether the brokerage can take the next account.
- Enrolment changes miss the carrier's cut-off date.
- Network, waiting-period and reimbursement questions arrive all day and occupy the account team.
- Renewal shows up with thirty days' notice, with no claims ratio organised to negotiate.
- The carrier's invoice differs from the active lives and the check is manual.
AI agents for every area
of your book.
Hendu connects the systems and channels your company already uses and keeps the operation's context: what was agreed, with whom, and what is still missing. The agents work on top of that layer, each one as the right arm of an area. You step in to approve.
The brokerage gets found by whoever is switching plans.
The agent answers whoever arrives from the site and from referrals, asks about lives, current carrier and renewal date, and shows which channel brings accounts that close.
- Answers site and referral contacts at once.
- Asks about lives, current carrier and renewal date.
- Shows which channel brought an account that signed.
The same team,
operating at another level.
This is not about your team working harder. Chasing, checking, handing over, remembering: that work, the one that eats their day today, starts happening on its own, following your rule and under your eye.
Efficiency
What took a day starts taking an afternoon. Not because someone ran faster, but because the step that jammed everything stopped existing.
Productivity
People go back to the work that needs people: deciding, negotiating, solving the hard case. The rest arrives finished, waiting to be reviewed.
Cost
Growing stops meaning hiring in the same proportion. The operation absorbs more volume on the structure it already has.
Margin
Less rework, fewer forgotten commitments, fewer missed deadlines. That is margin that was already yours and was leaking on the way.
Scale
What works in one team is repeated in the others the following week, because the rule is written down, not held in one person’s head.
Seven days.
One process, live.
Not a six-month project. One process at a time. And you see the first one working before you decide on the second.
We map the rule
We sit with the people who do the work: the exceptions, the limits, who decides what.
We build
The agent is built in your vocabulary, on the systems the company already uses.
It goes live
It runs on real work, supervised, and is adjusted in use before it leaves our hands.
It expands
With the first one running, the second costs less: context and governance already exist.
AI is already in your company.
The work has not changed.
Someone on your team opened ChatGPT today. It helped write an email, summarise a document. But the payment nobody chased, the deadline nobody checked and Friday's report are still done by hand, exactly as before. It is worth understanding why. That is what the Labs fixes.
Ask an off-the-shelf AI tool for a payment reminder and it writes a flawless email in ten seconds. But it does not know how much that client owes, what was agreed with them last week, whether your finance team already chased them yesterday, or that your company never chases before five days overdue. You are the one who knows all that. So you type in the context, read what comes back, fix what is wrong. And you end up spending nearly as long as writing it yourself. The real work was never writing the email. It is knowing what has to be in it.
This is where the agents live.
You approve, they do the rest.
The agent the Labs builds does not live in a separate window. It comes into this screen, alongside the rest of your team’s work: this is where it shows what it did, what it proposes and what is waiting on your go-ahead.
Sector not listed?
The conversation starts from your process.
Half an hour to find where it hurts in your operation, and a picture of what AI changes in it. If it makes sense, the first agent is live in seven days.