Introducing Lift: self-improvement layer for your Voice AI Agent

Today we're introducing Lift, the self-improvement layer for your Voice AI Agent. It reads your live calls, finds the changes that lift conversion, and proves each one on real traffic before it scales. The tuning that took a person weeks now happens in hours. Your agent improves at the speed of the campaign itself, and every call it makes leaves it a little sharper than the one before.

Our Humanoid Voice AI Agents have made tens of millions of calls for India's lenders, insurers, and consumer brands, across gold loans, personal loans, hiring & onboarding, collections, renewals, and lead qualification. That volume showed us exactly where conversion leaks, and why most of it never gets fixed.

Your conversions are hiding in your calls

Improving a Voice AI Agent comes down to one thing. Someone listening to calls and rewriting the agent's instructions based on what they hear. That works, and good teams do it. The catch is arithmetic. A person can listen to maybe thirty or forty calls before they have to act and move on, while a single campaign throws off thousands of calls a day, so every improvement rests on the thin slice someone had time to hear. The conversions you're losing in the other calls stay lost, not because anyone made a bad call, but because no one ever had the time to find and fix them.

How Lift works

Lift reads the calls a person never gets to, and it starts where the lessons are. The near-misses where the customer almost booked, the early drop-offs where something broke in the first thirty seconds, the conversions that closed but took a clumsy path to get there. It scores each one on whether it converted and how cleanly the agent handled it, then finds the specific changes to the agent's instructions that would have turned more of those calls into outcomes.

The fixes are small and exact.  Capping how many times the agent repeats an interest rate, giving it a cleaner way to confirm a loan amount it misheard, tightening how it answers a customer who says the EMI is too high. Precise edits hold up on live calls where a full rewrite of a working agent tends to break things you didn't expect, so Lift changes one thing at a time rather than reworking the whole script.

No change scales on a hunch. Each improved agent goes up against the current one on a small slice of live calls. It wins more traffic only as the conversion data backs it, climbing from a small share to a quarter to half to the whole campaign. If a change hurts something that was already working, it gets caught while it's still on that small slice and corrected before it spreads.

Humans stay in control at every step

A self-improving system is only as trustworthy as the controls around it. Every change Lift proposes is reviewed by a human before it reaches production traffic. Campaign managers see exactly what changed, why the system proposed it, and how it performed against the existing agent in a side-by-side comparison under identical conditions.

The agent's instructions are split into sections our team can lock and sections Lift is allowed to optimize. Compliance language, product rules, pricing guardrails, and identity parameters live in protected sections that Lift cannot touch, so optimization targets only the parts of the conversation where there's room to improve without risking what's already working.

Results in production

A high-volume discovery and lead marketplace ran Lift on an appointment-booking campaign. The optimized agent booked appointments at 2.7% against 1.5% on the existing agent, and it held that lift across more than twelve thousand leads. One of the changes Lift found, a rule that stopped the agent from repeating pricing more than once, cut excessive price repetition sharply on its own.

A leading gold-loan lender saw branch visits scheduled rise from 2.2% to 4.3% on the optimized agent. A major digital-lending NBFC ran the optimized agent against its existing one and moved the entire campaign onto it once the numbers held. One of India's largest jobs and recruitment platforms scaled the optimized agent from a small share of traffic to half the campaign as the positive results sustained.

None of these were a one-time tuning pass. Each agent kept being measured against the version it replaced, on live traffic, and kept more of the campaign only as it kept earning it.

Every campaign makes the next one faster

The first time Lift works on a new kind of campaign, it learns the patterns from scratch. The value builds because those lessons carry forward, so a fix that lifted a lending flow becomes a head start the next time a similar problem shows up in insurance or collections, and later campaigns reach their gains in fewer cycles than the first ones did. The agent you launch with is the floor, and the calls it makes are what move it up from there.

Request a Demo to see how Lift works inside the Humanoid Voice AI Agent.