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AI Document Intelligence for Alternative Investments

Transform Unstructured Alternative Investment Data into Actionable Insights – Instantly

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"FundCount AI Document Intelligence extracts so many more data points from more types of statements – compared it’s competitors!"

CFO, Multi-Family Office, New York

Benefits

Increased Efficiency

Turn unstructured GP documents into structured, investment-ready data so you can see exposures, performance drivers, and cash needs without waiting on manual rollups. Faster, cleaner inputs mean fewer “surprises” at quarter-end and more confidence in portfolio decisions.

Stop chasing PDFs across portals and email by automating extraction into consistent, tie-out-ready data for capital activity, fees, and allocations across funds and entities so your team works exceptions instead of re-keying, reduces reconciliations caused by format drift, closes faster, and keeps an audit trail back to source documents.

Standardize the inputs that feed tax and regulatory workflows, even when every manager’s statements look different. Improve completeness and traceability so you can validate figures quickly and support filings with clear source documentation.

Works with Funds and Co-Investments

Eliminate time-consuming, manual data entry and document management workflows, and allow team to focus on high-value tasks like analysis and strategy.

Rich set of data points is extracted from sophisticated Capital account statements, Calls, Distributions, K-1s and Co-Investments Financial statements into a standardized format and displayed on a dashboard for review – by your clients or accounting teams in real time.

Direct data feed into FundCount or other accounting systems facilitates fast automated data delivery for recording and analysis. Original Documents are one click away within reports in FundCount investment accounting and reporting software

Enhanced Data Details and Accuracy

Automated extraction with modern LLM and verification processes significantly reduce the risk of human error associated with manual data handling.

Unlike old machine learning and OCR technologies, the new AI-driven approach is able to overcome issues like watermarks in PDFs, sensitivity to change in statements’ formats and unstable elements positioning in complex documents like combined call and distribution notices.

Leveraging LLMs allows extraction of a very rich book and tax data set from previously un-seen statements of any complexity without additional model training. Coupled with minimum involvement of people, new technology allows to reduce errors, cut time and operational costs.

Scalability of Expertise

AI Document Intelligence platform provides a scalable foundation for firms to manage a growing volume and complexity of alternative investments without proportionally increasing human resources.

Private equity professionals in investment, accounting and tax could be hard to find and retain.

Allocating time of these costly human resources from time consuming routine operations to the higher value analytical and decision-making tasks becomes a game changer and competitive advantage for asset managers, family offices and fund administrators globally.

FundCount AI Document Intelligence for Alternative Investments in combination with it’s investment accounting and reporting platform automate data collection and reporting and save hours of manual work.

FAQ

AI document intelligence turns unstructured files (PDFs, scans, emailed statements) into structured data by understanding both text and layout. The output is usable fields and tables you can validate and move into reporting or accounting workflows.
OCR mainly “reads” characters, while document intelligence interprets meaning and structure (e.g., which numbers are fees vs commitments vs distributions). That context reduces mis-mapped fields when formats change or tables move.
Traditional IDP often relies on rigid templates and brittle rules, which break when managers change statement layouts. AI-first approaches are built to generalize across formats and handle variation with fewer manual templates.
Common examples include capital call notices, distribution notices, capital account statements, quarterly reports, and fund financial statements. The best fit is documents that are frequent, repetitive, and currently require manual re-keying or reconciliation.
High-quality systems assign confidence scores and route low-confidence fields to review instead of guessing silently. This creates exception-based workflows where humans validate only what needs attention.
Review queues typically show extracted values alongside the source snippet or highlighted location in the document for fast verification. Approvals and edits are logged so teams can trace what changed, by whom, and why.
Documents can be ingested from investor portals, email inboxes, and bulk uploads, then normalized into a standard schema. The goal is to remove “where did we store it?” friction and keep processing consistent across sources.
Yes. Outputs are commonly delivered in structured formats (like CSV/Excel or API-ready data) so downstream tools can consume them. A good implementation also preserves links back to the source document for auditability.
Audit-ready setups maintain data lineage from each reported number back to the source document, plus an action log for reviews and edits. This makes it easier to support audits, investor questions, and internal controls without recreating work.
Test it on your real documents: different managers, different formats, and the messy edge cases you actually see. Evaluate extraction accuracy, exception handling, traceability, and how well it fits your close and reporting workflow.

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