Client Pain Points
A quantitative investment team faced three core challenges:
Manual Analysis Bottleneck — Market data volumes had scaled from gigabytes to terabytes. Traditional manual strategy screening could no longer keep pace with market speed.
Emotion-Driven Decisions — Investment decisions relying on human judgment were susceptible to market sentiment, leading to high return volatility and poor drawdown control.
Missing Technical Architecture — The team had a strong finance background but lacked system architecture experience, unsure how to turn quantitative strategies into reliable software systems.
Limitations of Traditional Approaches
The client engaged three technology consulting firms. Each quoted tens of thousands of dollars with a 2–3 week lead time for an initial proposal. More critically:
- Most consultants came from generic IT backgrounds and did not understand the specific requirements of quantitative investing
- Proposals remained at the "feature list" level, lacking underlying technical architecture design
- None could provide technology stack recommendations for specialized modules like strategy backtesting and risk control models
AIGCLINK Intervention
The client described their vision — "an AI-assisted quantitative investment system" — in a voice recording. AIGCLINK used its AI expert consulting capability to rapidly complete a deep requirements analysis:
Requirements Structuring — Decomposed one vague statement into 6 independent technical modules, each with clear functional boundaries and interface definitions.
Technical Roadmap Planning — Recommended a time-series database solution for the data ingestion layer, designed a distributed computing architecture for factor calculation, and proposed an event-driven framework for the strategy engine.
Risk Control Design — Proactively included a risk control module the client had not explicitly mentioned, covering stop-loss strategies, position management, and extreme market protection.
Core Value of AI Expert Consulting
- Domain Knowledge Depth — Accurately understood quantitative investing terminology and business logic without requiring the client to re-explain fundamentals
- Proactive Gap-Filling — Modules the client hadn't mentioned — risk control, compliance, and disaster recovery — were proactively added to the proposal
- Precise Technology Recommendations — Delivered specific tech stack choices and architecture designs, not generic advice
Deliverables
| Document Type | Content |
|---|---|
| Overall Solution | System overview, module relationships, implementation roadmap |
| Product Proposal | 6 core modules, user scenarios, acceptance criteria |
| Technical Proposal | Distributed architecture, tech stack selection, deployment plan |
| Pricing Quote | Per-module pricing, implementation schedule |
| Technical Service Contract | Delivery conditions, IP ownership, confidentiality terms |