7 Requirements to Consider for Your Internal RMS Build

Before you build an in-house research management system, audit these requirements. Most teams underestimate each one.

1. MS Office Integration

Excel modelling with check-out/in, version history, calculation scripts, distribution permissions. Your analysts live in Office. Half-baked integration kills adoption. You’ll also need decimal-point-accurate normalization scripts that extract structured outputs from free-form models, plus consensus vs. internal side-by-side comparison views. This isn’t a weekend project, it requires deep understanding of Excel’s add-in architecture and how analysts use Excel for modelling.

2. Permissioned AI Governance

Role-based access, model selection controls, BYO API keys, grounded citations, zero data training commitments. Without this, your compliance team will shut you down. Your system must route data through your organization’s own cloud tenancy to preserve existing encryption and logging controls. Every AI response needs verifiable citations from source documents not hallucinated from training data.

3. Regulatory Audit Trails

Comprehensive logs for every action. You need to satisfy Corporations Act 2001 (Australia), FCA SYSC requirements (UK), and SEC Rules 17a-3/17a-4 (US) simultaneously. This means immutable timestamps on every note edit, model update, AI prompt, and approval workflow. You must be able to reconstruct exactly what was known at any decision point and export logs in formats acceptable to regulators in each jurisdiction.

4. ESG and Stewardship Workflows

Custom scorecards, engagement logs, proxy voting records including voting rationale, SFDR and TCFD reporting, all linked back to investment cases. Engagement tracking must capture the full lifecycle: trigger event, objectives set, progress updates, and outcome achieved. Critically, all ESG data must flow bidirectionally with core investment research. An engagement outcome should update the investment thesis automatically.

5. Multi-Asset Class Support

Different securities mapping, data structures, analytical frameworks, and ESG considerations for equities, fixed income, alternatives, and real assets. Fixed income requires issuer vs. security-level hierarchies and different identifier standards (CUSIP, ISIN). Private assets (venture, private equity, private credit) involve manual data entry from quarterly reports. Each asset class compounds architectural complexity.

6. Security Certifications

SOC 2 and ISO 27001 require ongoing third-party audits. Build in-house and you own this burden, or you ask stakeholders to accept unaudited assurances. These aren’t one-time achievements, they require continuous monitoring, documented incident response, staff training, and regular penetration testing. Direct costs can run into hundreds of thousands annually; indirect costs are often higher.

7. Long-Term Maintenance

Version upgrades, security patches, regulatory changes, schema evolution, new LLM capabilities, staff turnover. The engineer who built it will eventually leave. Cloud providers deprecate services on their timeline, not yours. Vulnerabilities require urgent patching. Regulatory frameworks expand. And when your architect leaves, you inherit a codebase only they fully understood.

Bottom line: If you can’t resource all seven, you’re building technical debt, not a platform.