Frequently Asked Questions
📊 The Point Platform
What is Point?
Point is an Investment Data Intelligence (IDI) platform that unifies, reconciles, and activates investment, client, and market data across custodians, portfolios, entities, and systems. It provides a multi-client Investment Book of Record (IBOR), an extensible investment data warehouse, data integrity tooling, and modular analytics, reporting, and AI engines — all within an open, cloud-native architecture.
What is Investment Data Intelligence (IDI)?
IDI is a data-first operating model that brings order, structure, and automation to the entire investment data value chain — from aggregation and transformation to activation and distribution. It ensures firms can trust their data, use it consistently across functions, and power both human and AI decision-making from the same unified dataset.
What does the Point platform actually do?
Point is your strategic data orchestration layer sitting between your Books of Record and your Systems of Engagement. It converts fragmented investment data into a single, audit-ready, AI-ready strategic asset across the full data value chain:
- Aggregation — all sources, all formats
- Transformation — cleaning, reconciliation, enrichment
- Activation — analytics, reporting, workflows
- Distribution — secure sharing with clients, partners, portals, and AI agents
Is Point a PMS, CRM, or accounting system?
No. Point is a data intelligence layer. It sits between Books of Record and Systems of Engagement, unifies and structures investment data, and powers analytics, reporting, and AI across the organisation. Point is designed to complement — not replace — core operational systems such as your PMS, CRM, or accounting engine.
What does Point actually replace?
Point replaces or consolidates:
- Fragmented spreadsheets and manual data pipelines
- Bespoke SQL/Python data patches
- Internal data warehouses not purpose-built for investment data
- Inconsistent reporting inputs across teams
It does not replace a PMS, accounting system, or CRM — it makes them work better together.
🔗 Data & Integrations
What types of data does Point aggregate?
Point aggregates all investment-relevant data, including positions, transactions, valuations, corporate actions, cash, fees, benchmark data, client metadata, market data, and documents such as PDF custodian statements. All of this is unified into a reconciled IBOR and an extensible investment data warehouse.
How does Point connect to custodians, PMS systems, banks, and platforms?
Point integrates through automated data feeds, APIs, files (SFTP/S3), and document ingestion pipelines. It supports multi-custodial, multi-jurisdiction, multi-currency environments and can run multiple ingestion patterns concurrently — ideal for firms with mixed systems, complex entity structures, or legacy estates.
How does Point ensure data accuracy and completeness?
Point creates a reconciled, multi-asset IBOR backed by a data quality and exception management layer. Every data point is checked for freshness, completeness, and correctness. Exceptions are flagged, lineage is preserved, and corrections are automatically propagated across analytics, reporting, and AI.
Can Point ingest unstructured data such as PDF statements?
Yes. Point includes document-processing capabilities for statements, contract notes, and custodial reports. Once processed, this data becomes part of the structured investment dataset — available to analytics, reporting, and AI modules. See FusionDocs for more detail on the document import workflow.
Can Point support multiple PMS systems simultaneously?
Yes. Point is PMS-agnostic. It can ingest, reconcile, and normalise data from multiple PMS systems simultaneously — essential for consolidators, multi-family offices, outsourced providers, and private banks managing hybrid technology estates.
Is Point suitable for multi-client, multi-entity environments?
Absolutely. Point is built with true multi-tenancy, supporting segregated books and clients, while enabling cross-client analytics, operational MI, and enterprise oversight where permissions allow.
📈 Analytics
What analytics does Point support out of the box?
Point provides analytics across:
- Asset class, sector, geography, and currency exposures
- Performance and attribution
- Risk, liquidity, and concentration
- Flows and change-over-time analysis
- Look-through for funds and complex structures
These run across individual portfolios, clients, legal entities, booking centres, and the entire firm.
Can we define our own calculated fields or build our own analytics?
Yes. Point supports custom analytics, derived fields, composite definitions, performance rules, private asset structures, and bespoke client metrics — with full lineage back to the underlying data.
Does Point support time-series and historical analytics?
Yes, where the bi-temporal capability is selected. Point maintains complete bi-temporal histories for positions, transactions, and valuations, enabling robust historical analytics, trend detection, multi-period comparisons, and reliable backtesting.
Can different roles access different analytical views?
Yes. Point's role-aware intelligence means CIOs, Portfolio Managers, Relationship Managers, COOs, and executives all see analytics tailored to their responsibilities — from granular trade-level insights through to firm-wide views.
Can PMs and RMs access analytics and reports directly without going through IT?
Yes. The Point platform exposes role-specific dashboards and analytics, and integrates with tools like PowerBI via Fabric/Semantic models. PMs and RMs can drill into exposures, performance, and flows in real time using centrally governed data — significantly reducing the report request queue that typically bottlenecks IT and data teams.
📄 Reporting
What types of reports can Point generate?
Point supports:
- Client reports and quarterly statements
- Performance and exposure reports
- Advisory and discretionary portfolio packs
- Board and executive management information
- Compliance and oversight reporting
- Custom PDF, Excel, or portal-integrated outputs
Reports are multi-dimensional, multi-asset, and multi-entity.
Can reports be customised and white-labelled?
Yes. Reports can be tailored by brand, client segment, portfolio type, jurisdiction, and language. Multiple report templates can be maintained and used simultaneously.
Does Point support automated report production and scheduling?
Yes. Reporting cycles can be fully automated, triggered by data freshness or produced on demand. This significantly reduces operational effort and key-person dependency.
Do we need to replace our existing portal or reporting tool to use Point?
No. Point can feed clean data into your existing portals and reporting tools, or you can choose to use Point's native reporting engine. You are not locked into one model.
🤖 Point AI
What is Point AI?
Point AI is Point's suite of enterprise-grade AI capabilities, built directly on top of Point's reconciled, deterministic, AI-ready investment dataset. It produces auditable, compliant, genuinely useful narratives and insights — not hallucinations — because it operates only on clean, governed, pre-calculated financial data.
See the Point AI overview for full details.
What features does Point AI provide?
Point AI currently includes three flagship capabilities:
- AI Briefing Notes — instant, role-specific intelligence packs generated from reconciled investment data, tailored for client meetings, RM preparation, CIO or committee reviews, and C-suite oversight
- FusionDocs — automated extraction of transaction data from PDF and image documents with human-in-the-loop approval before import
- Point MSP (Model Serving & Sharing Protocol) — the integration layer enabling external AI agents and enterprise LLMs to safely consume Point's reconciled, AI-ready data
How does Point AI ensure accuracy, explainability, and auditability?
Point AI follows a deterministic-first, AI-second principle:
- Point performs all financial calculations (exposures, performance, risk, liquidity) using deterministic methods
- AI operates only on these validated outputs
- Every AI statement is fully traceable back to source data with clear lineage
- AI never replaces the computations required for regulatory or fiduciary oversight
This enables explainable AI that can be defended to clients, regulators, boards, and auditors.
How does Point AI protect client confidentiality and prevent data leakage?
Point AI is built with data security as a guiding principle:
- No client-sensitive data is sent to uncontrolled external models
- Data passed to AI is filtered, permissioned, and audited
- Access to datasets is restricted via role-based controls
- PII can be anonymised before engaging any external AI agents
- All AI activity is logged for compliance and oversight
📥 FusionDocs
What is FusionDocs and how does it reduce manual work?
FusionDocs automates the extraction of transaction data from PDF bank statements, custodial statements, cash reports, and similar documents. Instead of manual transcription, FusionDocs reads the document, extracts structured transaction fields, and presents them for human review and approval before import into the IBOR.
This dramatically reduces operational burden while maintaining accuracy, oversight, and full audit control. See the FusionDocs overview for full details.
How long does FusionDocs take to process a document?
Processing typically takes 1–3 minutes from upload to the review screen, depending on document length and complexity.
Can FusionDocs handle scanned documents?
Yes. FusionDocs automatically applies optical character recognition (OCR) when it detects a scanned or image-based document — no pre-processing is required on your end.
What file formats does FusionDocs support?
FusionDocs supports PDF (text-based and scanned), Word documents (DOCX), and image files (photographed statements and scanned pages).
🔒 Security & Compliance
Is Point GDPR compliant?
Yes. Point is built with privacy-by-design principles:
- Documents processed by FusionDocs can be discarded immediately after import, leaving no copy in storage
- Data retention periods are configurable (1 to 365 days) per your data governance policy
- Data can be deleted on demand in response to right-to-erasure requests
- PII can be anonymised before AI processing
- All processing activity is logged and auditable
How is data kept secure within Point?
- End-to-end encryption for data in transit and at rest
- Complete data isolation between clients at every level of the system
- Role-based access: users only see data appropriate to their permissions
- All secrets managed through Azure Key Vault with support for automatic rotation
- Multi-factor authentication for administrative access
- ISO-aligned security controls across the platform
Can Point be deployed in a dedicated or private environment?
Yes. While the default deployment is multi-tenant on Azure, Point supports dedicated private cloud environments where required by regulatory, data sovereignty, or contractual constraints — including jurisdiction-specific hosting in the UK, EU, and Switzerland.
⚙️ Technical Architecture
How is the Point platform architected?
Point is designed to sit between a firm's Books of Record (custodians, PMS, banks, CRM, private asset systems) and its Systems of Engagement (portals, CRM, dashboards, reporting, AI interfaces). At its core, Point combines:
- A next-generation Independent IBOR (IIBOR) for reconciled transaction, position, and valuation data
- An extensible, bi-temporal data warehouse for historical, multi-dimensional analysis
- A deterministic analytics engine for performance, exposure, flows, and risk calculations at scale
- An open integration layer (APIs, exports, Fabric/Semantic support) for downstream systems and third-party technologies
Is Point cloud-based? Which cloud providers are supported?
Yes. Point is a cloud-native SaaS platform, currently deployed on Microsoft Azure using PostgreSQL Flexible Servers. This architecture is enterprise-grade, globally resilient, and compatible with extensions into Snowflake, Redshift, or cloud lakehouse architectures.
How does Point integrate with an existing technology stack?
Point is explicitly designed to layer into an existing estate. Supported integration patterns include:
- REST APIs — including Point's open API for partners and AI tools
- SFTP/file ingestion — for custodians, banks, private equity managers, and platforms
- Direct database connectors — subject to vendor permissions
- Data warehouse/lake integrations — including Fabric/Semantic Models and Snowflake compatibility
- Outbound feeds — to portals, CRMs, OMS systems, and AI tools
How does Point scale for large data volumes and multi-jurisdiction operations?
Point's architecture was built for scale and multi-tenant complexity:
- PostgreSQL on Azure enables horizontal and vertical scaling for large datasets
- Multi-client IBOR and warehouse models support thousands of portfolios across multiple jurisdictions
- Multi-asset ingestion pipelines handle liquid, illiquid, private assets, alternatives, and unstructured sources
- Batch and on-demand pipelines handle daily, intraday, and event-triggered calculations efficiently
How does Point support AI-enabled operating models?
AI-enabled investment managers require consistent pre-calculated data, structured schemas, complete history, reconciled positions, transparent lineage, unified metadata, and machine-readable formats. Point delivers all of these natively, transforming your operating model to be AI-ready without replacing your existing technology stack.