▎ A new category in Indian wealth tech
RetainEQ is an AI copilot for Indian mutual fund distributors. The wedge is simple: identify which client relationships need attention first, explain why, and help the broker run better retention conversations this week.
Industry · India MF
SOURCE: AMFI · MAR 2026▎ The Problem
01
On average, 15–20% of an MFD's AUM redeems each year. At 0.83% trail, that is ₹50–80 lakh of recurring income gone — permanently — for every ₹100 Cr managed.
02
Of 12 Indian MFD platforms analysed, zero use ML to predict redemptions. The category is empty. The first mover defines the playbook.
03
A new ₹1 Cr SIP relationship takes 24–36 months to build. Retaining one takes a single well-timed conversation.
▎ How It Works
STEP 01
For a live pilot, the broker shares anonymized CAMS or KFintech history only. PAN, name, phone, and email stay inside their environment.
STEP 02
RetainEQ scores the book, highlights which households need attention first, and turns the book into a weekly queue instead of a spreadsheet dump.
STEP 03
Each surfaced account comes with plain-language reasons and a suggested next move, so the broker sees why it is on the list.
STEP 04
The broker works from one shared queue: review, call, add notes, override if needed, and keep all follow-up in the same workflow shell.
▎ The Moat
A weekend hacker can build a churn model. They cannot replicate twelve months of intervention outcomes across hundreds of distributors. Every “Contacted → Stayed/Left” click trains the next version of the platform. Causal ML on top of proprietary outcome data is what makes this category-defining.
▎ See It Live
The workspace is loaded with a realistic synthetic book and a working broker workflow shell. The goal is not to look broad. The goal is to show one believable pilot: who needs attention, why, and what the broker should do next.
Open The Workspace →