Fund selection that is easier to explain and easier to defend.
Muuvment has built a fund selector to help financial advisors evaluate eligible funds against a firm’s criteria, generate a clear rationale, and keep expert judgment where it belongs.
Built around specific criteria, policies, and operating models.
A recommendation needs more than an answer.
When advisors work across a large fund universe, they need a consistent way to apply Know Your Product (KYP) criteria, evidence the rationale, and review the decision — not another opaque output. Doing that diligence thoroughly and consistently, and documenting it in a way that holds up with clients and regulators, is usually manual, time-consuming, and hard to defend.
Deterministic matching, with AI-generated explanations.
We built a fund-selection engine that combines deterministic matching logic with AI-powered natural-language generation. It follows Monarch’s own rule-based selection criteria, then uses AI to explain its reasoning in language advisors and regulators can understand.
The engine applies Monarch’s eligibility rules to each client profile and criteria, screening the full eligible universe.
It surfaces the qualifying options with a source-grounded, human-readable rationale for each.
Every recommendation is routed for advisor review. Expert judgment stays in the loop.
The system retains a decision record: a rationale that traces from client profile to fund, ready to support review.
Designed around the controls that matter.
For every solution we build, we define the controls that match the workflow and the stakes.
Eligibility logic and matching criteria are configured around the client’s own policy — not a fixed, one-size-fits-all ruleset.
Every recommendation is validated by a person before it reaches a client. The workflow does not decide on its own.
Each recommendation produces a rationale that traces from client profile to fund, designed to support review.
Who can see what, and who can act on it, is defined before the solution is deployed — not an afterthought.
Multi-language support can be built in from day one, where relevant to a firm’s advisors and clients.
The specific measures — hosting, data boundaries, review steps — depend on the client’s systems, regulatory obligations, and use case.
Deterministic fund selection, with KYP built in.
Monarch’s advisors recommend funds to clients out of a universe of close to 40,000. Muuvment built a fund-selection engine that considers every eligible fund against client-specific criteria, combining rule-based matching, AI-assisted natural-language interaction, source-grounded explanations, and human-in-the-loop validation on every recommendation.
“We could not have bought what Muuvment built for us. The off-the-shelf options ran three to ten times the cost, and none of them would have been so tightly integrated into our workflow or met our exact needs. In under four months, we are heading into user acceptance testing, and we are now planning to work with them on our next backlog: KYC.”
The right solution is integrated into your workflow.
Some firms spend a year building what they could have configured in a week. Others buy off-the-shelf for a problem only a purpose-fit system solves. The right approach depends on the problem — and in a high-stakes workflow like fund selection, that usually means something built around a firm’s own criteria, not a generic tool.
Configure tools that already exist. Fastest to deploy. Subscriptions and vendor lock-in add up over time.
Configuration and targeted development on top of an existing solution — faster than a custom build, better fitted than off-the-shelf.
A custom system for problems no off-the-shelf solution fits. Slower and more expensive, but the client owns it. This is where the Monarch engagement sits.
Built for teams that need their own approaches reflected what they deliver.
Wealth managers, advisor networks, and investment product teams evaluating funds against defined criteria. Compliance leaders who need a defensible, reviewable rationale. Technology leaders who need eligibility logic, information boundaries, and approvals to reflect their own operating model — not someone else’s.
