Two problems, one workflow.
A bespoke fund-research implementation combining rule-based matching with explanations for advisor review.
From kickoff to user testing in under four months.
User testing
The implementation is in user acceptance testing. This is not a claim of completed production rollout.
300 planned users
The intended rollout audience is 300 users. Active adoption and measured financial results are not reported here.
Researching funds and explaining the result.
Monarch wanted its fund-research process to reflect its own criteria and operating model. Gathering product information, applying criteria, and preparing a rationale involved work that needed to be consistent and reviewable.
Muuvment worked with Monarch to evaluate the available approaches, then built a bespoke workflow rather than asking the firm to fit a generic tool.
Matching and explanation do different jobs.
Rule-based matching
The engine applies Monarch’s defined matching criteria to eligible funds.
AI-assisted explanation
Generated language helps present the matching result for a person to inspect. It is not a guarantee that an explanation is correct.
Advisor review
Human judgment remains part of the process. Firm-level product review and client suitability responsibilities remain distinct.
Firm-specific integration
The existing case describes English/French support and integration with Monarch’s identity infrastructure. Another firm’s requirements would be separately scoped.
Built around Monarch’s needs.
“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 cost comparison is the client’s account of alternatives considered for this project, not a general savings claim. The referenced KYC backlog is future work, not evidence of a delivered KYC implementation.
Measure the work in use.
The next evidence should distinguish testing from rollout, eligible users from active users, and demonstrated capability from measured business benefit. Research time, review effort, exceptions, and adoption can then be assessed against a baseline.
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