Product Tour
Product Tour
Fund Analyst Intelligence is designed around one objective.
Make monthly fund validation repeatable, evidence-first, and allocator-ready.
This tour is organised by the main screens and artefacts.
Each section maps to a real production task.
Each output is designed to support review and accountability.
1. Fund profile
The fund profile is the canonical view of a fund.
It consolidates validated facts, current status, and change history.
You see:
- key identifiers and fund metadata
- strategy and mandate summary
- operational terms and constraints
- risk and performance snapshots (where applicable)
- last validation date and cycle status
Why it matters:
It eliminates “which file is the latest” from daily work.
It provides a stable reference point for clients and internal teams.
2. Source library
Every fund has a controlled library of source artefacts.
Artefacts are versioned and tagged by cycle.
Examples:
- DDQ and ODD files
- factsheets and pitch decks
- subscription documents
- manager letters and email updates
- approved online sources and snapshots
What the system enforces:
- date and version metadata
- provenance links between sources and extracted claims
- retention rules and access controls
Why it matters:
Evidence is captured once and reused.
Review becomes faster and defensible.
3. Monthly validation workspace
This is the operational centre of the product.
It guides a team through a complete monthly cycle.
You see:
- cycle status: ingest → extract → validate → compare → report → approve
- validation score and key quality signals
- an exceptions queue ordered by materiality
- suggested actions and open questions
Why it matters:
The workflow becomes a system.
Progress and ownership are visible.
4. Exceptions and change log
Most funds do not require full rework each month.
They require attention to a small number of changes.
You see:
- a list of detected deltas vs prior snapshot
- classification: operational, strategy, personnel, fees, risk, legal, reporting
- materiality and confidence indicators
- evidence links for each flagged change
- reviewer decisions and resolution notes
Why it matters:
Analysts review exceptions, not entire documents.
Teams focus on what matters.
5. Validation checks
Validation is implemented as deterministic checks.
The goal is consistency and reliability.
Examples:
- required field completeness
- cross-field consistency (e.g., liquidity terms vs redemption frequency)
- internal rules (your investment policy constraints)
- date recency and source freshness
- duplicate or conflicting statements across sources
Why it matters:
It catches issues before they enter reports.
It reduces dependence on memory and manual vigilance.
6. Evidence pack
The evidence pack is the product’s credibility layer.
It is a structured bundle of citations, links, and extracted snippets.
You see:
- source list with versions and dates
- claim-to-evidence mapping
- page references and excerpts
- cycle notes and approvals
Why it matters:
It makes validation auditable.
It makes client and internal queries easy to resolve.
7. Reporting outputs
Reports are generated from validated facts and resolved exceptions.
They are structured for allocator use and internal governance.
Typical outputs:
- monthly validation memo
- quarterly IC pack section
- change summary and risk flags
- updated fund factsheet block
- investor-facing commentary (optional, controlled)
Report properties:
- stable template and section order
- explicit “what changed” segment
- evidence links embedded throughout
- approval signature and timestamp
Why it matters:
Reporting becomes consistent and fast.
Narrative is grounded in validated information.
8. Review and approvals
Human review is treated as part of production.
It is not a workaround.
You see:
- reviewer queue and ownership
- decision states: accepted, edited, needs follow-up, rejected
- comments and rationale captured in the system
- final sign-off for publication
Why it matters:
Governance is preserved.
Automation reduces workload without removing accountability.
9. Search and cross-fund views
Teams need portfolio-level visibility.
Fund Analyst Intelligence supports cross-fund monitoring and analysis.
You see:
- portfolio exception dashboard
- filters by strategy, manager, domicile, risk bucket, status
- trending changes across months
- recurring issues by category
Why it matters:
It makes monitoring scalable.
It reveals patterns that manual workflows hide.
What to do next
If you are evaluating the product, start here:
- Read the Monthly validation cycle.
- Review Data and evidence.
- Check Validation and quality.
- Look at Audit trail.
This sequence mirrors a production onboarding decision.