- Industry Pain Points: Research is not “writing”—it’s auditable information production
Key pain points include:
Many noisy data sources
Prices, filings, announcements, macro data, news/sentiment, and flows differ in quality and format. Weak data governance leads to misreads and wrong conclusions.
Scattered materials and broken evidence chains
Notes, filings, and models live across tools. Conclusions can’t be traced to original sources, making handover and retrospectives difficult.
Fast strategy iteration without version discipline
Indicators, parameters, windows, and assumptions change frequently. Without versioning and change logs, differences become unexplainable.
Unclear risk and compliance workflows
Disclosures, risk warnings, suitability language, and prohibited phrasing are complex. Without consistent gates, compliance risk rises.
Inconsistent messaging across channels
Different phrasing across app, email, social, and communities harms trust and increases complaints.
Subscription delivery lacks structure
Subscribers need stable cadence and clear service boundaries. Without a service catalog and quality standards, retention and renewal fluctuate.
- Solution Approach: Build a “data → research → strategy → review → publish → subscription” loop
The solution enables execution and auditability via:
Enterprise Back Office (skeleton): data catalogs, indicator/model libraries, research projects, strategy versions and backtests, risk & compliance rules and approvals, content asset library, publishing calendar, subscription packages and SLAs, support tickets, finance and renewals.
Messaging Foundation (nervous system): discussions, confirmations, risk notes, compliance edits, and publish approvals become a searchable fact stream.
Role-based AI Employees (executors): AI Data Analyst, AI Research Assistant, AI Strategy Engineer, AI Risk Assistant, AI Compliance Assistant, AI Editor, AI Publishing Ops, AI Subscription Ops.
- Implementation Path: Govern inputs and evidence first, then strategy versions and risk, then compliant publishing and retention
Phase A — Objectify data sources and indicator definitions (make inputs reliable)
Maintain a data catalog with reliability tiers, update schedules, and anomaly rules. Define an indicator dictionary with calculation standards. AI monitors completeness, flags anomalies, and produces daily quality reports.
Phase B — Projectize research and retain evidence chains (make conclusions reviewable)
Each research project captures question, hypothesis, data sources, charts, conclusions, counter-evidence, and risks. AI drafts summaries and comparison tables; research leads confirm key conclusions and risk statements, creating a “conclusion → evidence → owner” chain.
Phase C — Version strategies and standardize backtests (make iteration explainable)
Strategies are versioned with parameters, windows, rules, cost assumptions, risk thresholds, suitability boundaries, and disallowed contexts. AI drafts backtest reports and stability checks; changes require a change request with rationale and impact scope.
Phase D — Risk and compliance gates (speed without crossing lines)
Pre-publish gates include risk checks (drawdowns, stress scenarios, position boundaries) and compliance checks (disclosures, prohibited phrasing, suitability cues). AI scans content and outputs required edits; final approval remains human-owned with audit trails.
Phase E — Asset-based content and multi-channel publishing (keep messaging consistent)
Insights become structured assets: headline, summary, charts, key conclusions, risk notes, disclosures, applicability. AI rewrites for each channel from the same source asset. Publishing ops schedules, monitors feedback, and triggers correction/withdrawal processes with logs.
Phase F — Subscription service catalog and renewal operations (make delivery productized)
Define subscription packages and SLAs (daily brief, intraday alerts, weekly review, special reports). AI segments users and pushes the right content and renewal prompts, plus periodic “value reports” to improve retention.
- Typical Outcomes: Faster research, lower risk, steadier publishing, sustainable subscriptions
Cleaner data governance, reviewable conclusions, explainable strategy iteration, safer publishing, consistent messaging, and more reliable retention/renewals.
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