Finance research multi-agent team
Analyst, risk and compliance agents collaborate to ship research and risk tags.
Evidence notice: cases, figures and inferred project metadata are unverified scenario illustrations, not established client outcomes, credentials or return promises. Signed contracts define scope, fees, baselines, acceptance and data responsibilities; client business data stays off-chain.
1. Project Overview
- Customer type
- SMB Banks / brokers / insurers
- Project duration
- 6–8 weeks
- Tech stack
- Multi-agent
- Deployment
- Hybrid cloud
2. Business Context & Pain Points
The customer is a SMB / Department player in Finance, with the core pain concentrated in "Risk & Compliance". Manual workflows and point tools could not scale or stay consistent, driving up response time, operating cost and compliance risk. This project delivered an end-to-end AI upgrade for that workflow.
3. Solution
Anchored on "Multi-Agent Collaboration", Global AI DAO integrated the following capability stack into a single solution:
- Multi-agent —— Wired into the critical business path across ingestion, inference and human-in-the-loop collaboration.
- Deployment: Hybrid cloud, balancing data compliance, performance SLA and operability.
4. Quantified Outcomes (Before vs After)
| Metric | Before | After | Delta |
|---|---|---|---|
| compliance miss rate | 100% | 30% | -70% |
Source metric: 6× research throughput, -70% compliance miss rate.
5. Specs & Fit
| Architecture | Perceive → plan → execute → reflect |
| Built-in agents | Office / finance / risk / marketing / ops — 20+ |
| Protocols | MCP / OpenAPI / custom tools |
| Memory | Short + long-term vector memory, persistent |
| Governance | Sandbox / permissions / audit |