Date
May 14, 2026
6 min read
From Portfolio Data to AI Use Cases: Unlocking Value with our MCP Connector
As private markets become increasingly data-intensive, firms are moving beyond static dashboards and fixed KPI reporting toward dynamic, AI-driven portfolio analysis. Investment teams are increasingly asking questions in real time, exploring performance across multiple dimensions, and generating actionable insights on demand.
The effectiveness of AI in this context depends heavily on the amount and quality of underlying data. However, many existing solutions are still built around a narrow reporting model, capturing only 20-30 core KPIs per company and often relying on fragmented, inconsistent data inputs. This significantly limits the depth, granularity, and overall usefulness of downstream analysis.
At 73 Strings, we solve this through 73 Extract and 73 Monitor, enabling firms to capture and standardize hundreds of data points across portfolio companies with minimal incremental effort. This richer data foundation significantly improves the depth and reliability of AI-driven portfolio insights.
Building on this, our MCP Connector enables firms to take a “model your way” approach. Rather than being locked into a single AI system, investment teams can either use Insights Generator within 73 Monitor or use their data from 73 Strings within their own AI models. Through our MCP Connector, firms can work with their preferred tools including internal LLMs as well as platforms such as Claude, ChatGPT, and Cursor, while ensuring all outputs remain grounded in consistent, governed portfolio data.
This combination of broader data capture and flexible model choice enables a new operating model for private markets intelligence, where firms can scale data coverage effortlessly and generate more powerful, AI-driven insights across their portfolios.
Private Equity
Surface portfolio-wide value creation opportunities
Private equity firms increasingly need to move beyond backward-looking variance reporting toward continuous portfolio intelligence.
With traditional reporting environments, AI systems are often limited to a relatively small set of manually tracked KPIs. This restricts the ability to identify relationships across operational and financial performance drivers at scale.
With 73 Extract, firms can structure and standardize significantly broader datasets across portfolio companies, including full P&L, balance sheet, cash flow, and operational performance metrics. As this data is standardized within a unified framework, AI systems can analyze portfolio performance with far greater context and consistency.
Using our MCP Connector, investment teams can issue prompts such as:
“Across my portfolio, identify companies experiencing margin compression, slowing revenue growth, and rising customer acquisition costs over the last six quarters. Prioritize the businesses with the greatest EBITDA impact and summarize the primary operational drivers.”
The MCP Connector enables AI systems to access governed data from 73 Monitor and apply consistent analytical logic across portfolio companies, generating structured insights with full traceability to source data.
This allows investment teams to evaluate operational and financial performance across the portfolio in a consistent framework, surfacing value creation opportunities that would be difficult to detect through siloed reporting. By connecting trends across margins, growth, and customer dynamics, firms can benchmark performance more effectively and focus management attention where it has the greatest impact.
Private Credit
Improve portfolio surveillance with deeper borrower intelligence
In private credit, delayed reporting and fragmented covenant monitoring often limit the ability to identify emerging risk early enough to act decisively.
Many firms still rely on relatively narrow borrower datasets, making it difficult for AI systems to detect more nuanced patterns of deterioration across the portfolio.
By extracting and standardizing broader borrower-level financial and operational data, 73 Strings enables more comprehensive portfolio observability across credit exposures. In addition to covenant metrics, firms can analyze liquidity trends, leverage profiles, working capital movements, operating performance, and cash flow dynamics within a consistent data structure.
Using our MCP Connector, risk teams can ask:
“For all borrowers in the portfolio, identify businesses where liquidity is declining, leverage is increasing, and operating cash flow has deteriorated over consecutive quarters. Rank exposures by severity and summarize the primary drivers.”
The MCP Connector enables AI systems to analyze interconnected trends across time-series financial data, apply consistent risk logic across obligors, and generate explainable outputs grounded in governed portfolio data.
This provides a more complete and timely view of borrower health, combining liquidity, leverage, and cash flow signals into a unified analytical perspective. As a result, firms can identify early warning signals sooner, rank exposures by severity, and engage borrowers with greater precision and confidence.
Venture Capital
Detect efficiency and runway risks earlier
Venture capital portfolios often suffer from inconsistent reporting structures across companies, making portfolio-wide analysis difficult to scale.
Limited KPI tracking can also prevent AI systems from identifying early operational and liquidity risks before they become material.
With 73 Extract and 73 Monitor, firms can ingest and structure a significantly broader set of financial and operational data points across portfolio companies while maintaining standardized reporting frameworks.
Using our MCP Connector, teams can issue prompts such as:
“Compare burn multiple trends, hiring growth, gross margin performance, and revenue efficiency across all portfolio companies over the last six quarters. Flag companies where operational spending is accelerating faster than commercial performance and summarize the primary drivers.”
The MCP Connector enables harmonization of reporting periods, simultaneous analysis across financial and operational metrics, and structured outputs within 73 Monitor.
With standardized portfolio visibility, AI systems can run consistent comparisons across companies and time, making it easier to detect emerging inefficiencies and execution risks. This improves early identification of runway pressure, capital inefficiency, and misalignment between operational spend and revenue performance across the portfolio.
The Future of Portfolio Intelligence
These use cases reflect the ways in which investment teams increasingly interact with portfolio data in practice.
Whether identifying operational inefficiencies in private equity, monitoring borrower deterioration in private credit, or detecting runway pressure in venture portfolios, the underlying requirement is consistent: AI systems must have access to large-scale, high-quality, structured portfolio data to produce reliable and actionable insights.
Through 73 Extract and 73 Monitor, firms transform fragmented reporting into a unified foundation of structured portfolio intelligence. The MCP Connector extends this foundation into AI environments by securely making governed, permissioned data available to the models and workflows investment teams already rely on.
This enables investment teams to move beyond static reporting toward AI-native portfolio intelligence, where richer, standardized data supports deeper analysis, stronger contextual understanding, and more effective investment decision-making.