AIx2 CORTEX: Optimal Matching and Reasoning Algorithms for Private Markets

At AIx2, we have developed AIx2 Cortex—a suite of optimal matching and reasoning algorithms tailored for private markets. Cortex operates on top of our proprietary AIx2 Cortex, a high-dimensional embedding space that captures the entire U.S. private market alongside each user’s unique preferences. Using vector space embedding for curated search and reasoning is based on our research with Stanford and Harvard researchers (see paper here). 

Private markets are vast, opaque, and dynamic. Gaining an edge in deal sourcing, portfolio construction, or due diligence often hinges on having both the right data with the right structure and the right algorithms building on that data structure. With the rapid proliferation of large language model (LLM)–based recommendation systems, many firms are exploring AI-driven solutions for private investments. Yet such solutions often fall short for one fundamental reason: they are not built upon a finance-specific, multi-dimensional representation of companies, investors, and user preferences. Below, we delve into the technical underpinnings of AIx2 Cortex, highlighting its geometry-based matching, finance-aware reasoning, and how it outperforms generic LLM solutions for financial workflows.

1. AIx2 Memora as the Foundational Vector Space

1.1 The Geometry of Finance

Our starting point is AIx2 Memora a vector space that integrates structured, unstructured, and confidential user data. Memora is domain-optimized for finance, meaning it fuses insights from:

  • Structured Datasets: Traditional financial metrics—revenue, EBITDA, growth rates, valuations—from sources like Bloomberg, Capital IQ, and other private databases.

  • Unstructured Data: News articles, press releases, regulatory filings, and other textual documents to capture contextual signals such as sentiment or market narratives.

  • User Preferences: Each user’s existing portfolio, sector focuses, risk tolerances, and strategic goals are mapped as high-dimensional vectors.

Within Memora, every entity—be it a startup, mature private company, limited partner (LP), general partner (GP), or individual investor—is mapped into the same semantic and numeric space. This single framework underlies all matching and due diligence tasks.

1.2 Continuous Updates

Memora is dynamically updated to reflect market changes. New financing rounds, mergers, industry disruptions, or user feedback reshape the embeddings, ensuring real-time accuracy. This fluidity is critical when deriving signals for nuanced tasks like risk modeling or evaluating newly emerged sectors.

2. AIx2 CORTEX: A Suite of Tailored Algorithms

Having established Memora as the foundation, AIx2 Cortex is where the analytical power emerges. CORTEX is a collection of geometry-based and finance-aware algorithms designed to match (e.g., find deals or prospective LPs) and reason (e.g., conduct diligence or comparative analysis) with high precision.

2.1 Geometry of Matching

2.1.1 Euclidean Distances and Beyond

Most recommendation engines rely on proximity in embedding spaces to drive “people who liked this also liked…” suggestions. Cortex extends this approach with a finance twist: we incorporate user-defined constraints, portfolio risks, and sector filters. At its simplest level, Cortex can compute Euclidean distances in Memora to:

  • Identify Similar Assets: For instance, find companies “near” an existing top performer in the embedding space, signifying comparable risk profiles or business models.

  • Find Compatible LPs/GPs: Narrow searches to partners who have historically aligned with the user’s investment style.

2.1.2 Spheres and Clusters

By creating spheres (or other geometric objects) around a user’s preference vector, Cortex can define “zones of acceptability.” Entities falling within these zones satisfy key criteria—whether it’s growth stage, sector alignment, or market cap. Cortex’s clustering algorithms then group these entities to surface hidden patterns or complementary relationships that might not be visible through linear screening.

2.2 Finance-Specific Reasoning

2.2.1 Fundamentals and Benchmarks

Generic LLM-based recommendations typically lack direct integrations with fundamental metrics. AIx2 Cortex, however, seamlessly incorporates fundamentals (like revenue run rates, P/E multiples, or IRR benchmarks) into the similarity logic. A target entity’s distance to a user’s “ideal vector” might be weighted by these financially relevant factors, preserving the fidelity of finance-specific signals.

2.2.2 Peer Comparisons and Portfolio-Level Analysis

Due diligence and portfolio optimization involve evaluating assets against peer sets and existing holdings. Cortex automates such analyses through:

  • Peer Clustering: Identifying not just the nearest neighbors, but a cohort of similar entities to gauge typical performance or risk exposures.

  • Portfolio Aggregation: Merging embeddings of existing holdings to calculate overarching portfolio footprints, thereby assessing how new deals fit within the user’s overall investment strategy.

2.2.3 Deep Diligence Through Sector Insights

LLMs are powerful for summarizing textual data, but they often miss numeric nuance and real-time finance data. In contrast, Cortex uses Memora’s integrated vector space to highlight macro-level trends, competitive landscapes, or emerging regulatory dynamics. By clustering similarly positioned assets or analyzing time-series trajectory, Cortex provides a broad, data-driven perspective to guide diligence findings.

3. Distinctions from Generic LLM Recommendation Systems

A growing literature explores LLM-based recommendation engines, but for private finance we see fundamental gaps. In our joint work with researchers at Stanford and Harvard, we identified critical pain points that led us to design a vector-based curated search and reasoning framework. Key differentiators include:

  1. Contextual Breadth: Traditional LLMs are limited by token-based contexts. Cortex references a vast repository of structured and unstructured data in Cortex without context-window constraints.

  2. Security and Confidentiality: Passing sensitive data to general LLM APIs is risky. Cortex’s algorithms run on secure, in-house servers, ensuring user portfolios and competitive intelligence remain private.

  3. Finance-Tuned Intelligence: Off-the-shelf LLMs can’t parse private market fundamentals or factor in time-sensitive valuations as elegantly as an embedding space that was purpose-built for finance.

4. Use Cases: Matching and Due Diligence in Action

4.1 Matching: Finding Similar Assets, LPs, or Individuals

An investor might ask: “Which startups in the AI biotech sector resemble my current top performer?”

  1. Identify Vector: The top performer’s vector is retrieved from Memora.

  2. Calculate Euclidean Distances: Cortex’s matching pipeline locates companies clustered around that vector.

  3. Refine by Fundamentals: Filter results using revenue growth or burn rates.

  4. Surface Opportunities: The final list pinpoints targets most likely to replicate (or complement) the success of the reference asset.

4.2 Due Diligence: Market Research and Peer Analysis

When evaluating a new investment, a user can query Cortex to:

  1. Embed the Target: Map the target startup’s textual descriptors, fundamentals, and market footprints into Memora.

  2. Identify Competitive Clusters: Locate peer sets or direct competitors in the same vector region.

  3. Derive Insights: Compare valuations, growth potential, or historical exit patterns within the identified cluster.

  4. Report Generation: Cortex synthesizes the findings, flagging key risk factors or synergy opportunities based on real-time market data.

5. AIx2 next steps

AIx2 Cortex and Memora form a continually evolving system. They absorb new transaction data, refine user preference vectors based on real-world deal outcomes, and adapt to emerging market shifts. The next phase of our research (in collaboration with Stanford & Harvard) aims to incorporate advanced time-series embeddings and finer-grained industry-specific taxonomies, further improving the accuracy of matches and the depth of due diligence insights.

AIx2 Cortex encapsulates a new frontier for deal sourcing and due diligence in private markets. By grounding its algorithms in AIx2 Memora—an optimized embedding space that unifies user preferences with comprehensive market data—Cortex delivers domain-specific intelligence that surpasses generic LLM-based recommendations. Its geometry-based matching, finance-centric reasoning, and dynamic update capabilities make Cortex a game-changer for private investors seeking clarity and efficiency in an increasingly complex landscape.