Wealth Management. Six workloads, one endpoint.
Private banking, RIAs & family offices — the workloads below need more than one data model at once, which is exactly why they stall on a stitched stack.
Private banks, wealth platforms, SEBI-registered investment advisers, PMS providers and multi-family offices serving HNI and mass-affluent clients.
The forcing function.
India's wealth AUM is projected to more than double by FY29, and the binding constraint is advisor capacity, not client demand. An AI copilot lets each RM manage a larger book — but only if it can see holdings, risk profile, family relationships, research and past conversations in the same query. Split those across systems and the copilot becomes a search box that hallucinates suitability.
The household graph — the true economic unit spans spouses, trusts and operating companies; each folio treated alone hides wallet share and concentration.
India wealth-management AUM, FY24 to FY29 — Deloitte projection
of affluent household wealth still informally managed
the binding constraint — copilots raise AUM served per RM
Six workloads, five query shapes.
Almost no workload here needs only one data model — which is why single-model databases deliver it only with a second system and a sync problem.
Holdings, transactions, risk profile, meeting notes, house research and product literature retrieved together — the RM asks a question and gets an answer grounded in this client, not a generic one.
AUM served per RM rises; preparation time per client review collapses.
Match products to clients using structured risk scores, embedding similarity over objectives and constraints, and graph checks on existing exposure concentration.
Fewer suitability exceptions; a defensible record behind every recommendation.
Traverse the true economic unit — spouse, HUF, trusts, operating companies, nominees and cross-holdings — rather than treating each folio as an island.
Wallet share visible for the first time; concentration risk measured at household level.
Continuous evaluation of drift against mandate, with conversational alerts routed to the covering RM — no dashboard, no BI backlog.
Faster corrective action and demonstrably better mandate adherence.
Semantic and full-text retrieval across house research, third-party notes, filings and transcripts, joined live with which clients actually hold the affected names.
Research reaches the right clients within hours of publication instead of days.
Every recommendation, its rationale and its evidence written atomically; graph analysis surfaces distribution conflicts and concentration patterns across the advisory book.
Fiduciary evidence on demand; mis-selling patterns caught internally first.
Impact statements are directional targets referenced to published industry research — calibrate against your own baseline.
The stack this replaces in Wealth Management.
Five licences, five sync jobs, five security perimeters — collapsed into one atomic store.
A copilot that knows the whole household — the difference between advisor leverage and an expensive chatbot.