Agents run securely via API or natively, keeping data local and traceable.
Plugs into your accounting stack (e.g., Paxus, QuickBooks, custom SQL) to close daily, monthly, or quarterly NAVs automatically, applying firm-specificvaluation and fee policies while producing audit-ready ledgers and P&Ls inminutes.
Access data in any format, accurately. Builds your SOI accurately. Generates accurate LP reports, graphs, and presentations.Automates 90% of the work for 10% of the cost.”
Auto-collects updates. Reconciles cap tables. Creates dynamic, traceable SOIs that are always current and require zero change management.
Connect any system. Process any data. One-click traceability. No more CSV/ERP/CRM headaches.”
Unifies positions, balances, and prices. One source of truth. Clean data for reporting, attribution, and dashboards.
Fetch. Match. Flag. Post. Close sameday.
Ingests hundreds of pages. Understands terms.Extracts what matters. Powers compliance.
Intelligent agents that work together to streamline your venture capital operations
Each of our agents
Automatically gather and organize critical venture data.
Streamlines the collection and organization of company data, financial metrics, and market intelligence.
Manage complex cap tables with ease.
Maintains accurate capitalization tables and provides instant insights into ownership structures.
Generate comprehensive LP reports instantly.
Creates detailed limited partner reports with key metrics, portfolio updates, and performance analysis.
Accelerate your investment decisions.
Automates the due diligence process by analyzing company data, market conditions, and risk factors to provide comprehensive investment insights.
Stay compliant with automated oversightAutomatically gather and organize critical venture data.
Ensures regulatory compliance by monitoring transactions, maintaining audit trails, and generating compliance reports automatically.
Deep insights into your investments.
Provides real-time portfolio performance analysis, identifies trends, and offers data-driven recommendations for portfolio optimization.
Agblox has assembled a tightly aligned family of four U.S. patents that together protect a complete technology stack for domain‑specific, self‑learning AI Agents that turn raw, heterogeneous information into forecasts, recommendations, and automated actions.
Simultaneous ingestion of any structured
Auto‑generated taxonomy + temporal parameters tied to the target subject.
Encodes expert heuristics into machine‑readable rules.
Subject‑specific NNs whose topology, weights, and activation thresholds are continuously tuned by deep‑learning meta‑networks as new predictors emerge.
Natural‑language queries return structured metrics and can fire downstream tasks
Sentiment and rules-based equity analysis using customized neural networks in multi-layer, machine learning-based model Sentiment and rules-based equity analysis using customized neural networks in multi-layer, machine learning-based model
A data analytics platform is provided for forecasting future states of commodities and other assets, based on processing of both textual and numerical data sources. The platform includes a multi-layer machine learning-based model that extracts sentiment from textual data in a natural language processing engine, evaluates numerical data in a time-series analysis, and generates an initial forecast for the commodity or asset being analyzed.The platform includes multiple applications of neural networks to develop augmented forecasts from further analysis of relevant information as it is collected. These include commodity-specific neural networks designed to continually develop taxonomies used to process commodity sentiment, and applications of reinforcement learning, symbolic networks, and unsupervised meta learning to improve overall performance and accuracy of the forecasts generated. A data analytics platform is provided for forecasting future states of commodities and other assets, based on processing of both textual and numerical data sources. The platform includes a multi-layer machine learning-based model that extracts sentiment from textual data in a natural language processing engine, evaluates numerical data in a time-series analysis, and generates an initial forecast for the commodity or asset being analyzed. The platform includes multiple applications of neural networks to develop augmented forecasts from further analysis of relevant information as it is collected. These include commodity-specific neural networks designed to continually develop taxonomies used to process commodity sentiment, and applications of reinforcement learning, symbolic networks, and unsupervised meta learning to improve overall performance and accuracy of the forecasts generated.
Curated sentiment analysis in multi-layer, machine learning-based forecasting model using customized, commodity-specific neural networks. Curated sentiment analysis in multi-layer, machine learning-based forecasting model using customized, commodity-specific neural networks.
A data analytics platform is provided for forecasting future states of commodities and other assets, based on processing of both textual and numerical data sources. The platform includes a multi-layer machine learning-based model that extracts sentiment from textual data in a natural language processing engine, evaluates numerical data in a time-series analysis, and generates an initial forecast for the commodity or asset being analyzed. The platform includes multiple applications of neural networks to develop augmented forecasts from further analysis of relevant information as it is collected. These include commodity-specific neural networks designed to continually develop taxonomies used to process commodity sentiment, and applications of reinforcement learning, symbolic networks, and unsupervised meta learning to improve overall performance and accuracy of the forecasts generated. A data analytics platform is provided for forecasting future states of commodities and other assets, based on processing of both textual and numerical data sources. The platform includes a multi-layer machine learning-based model that extracts sentiment from textual data in a natural language processing engine, evaluates numerical data in a time-series analysis, and generates an initial forecast for the commodity or asset being analyzed. The platform includes multiple applications of neural networks to develop augmented forecasts from further analysis of relevant information as it is collected. These include commodity-specific neural networks designed to continually develop taxonomies used to process commodity sentiment, and applications of reinforcement learning, symbolic networks, and unsupervised meta learning to improve overall performance and accuracy of the forecasts generated.
Curated Sentiment Analysis in Multi-Layer, Machine Learning-Based Forecasting Model Using Customized, Commodity-Specific Neural Networks. Curated Sentiment Analysis in Multi-Layer, Machine Learning-Based Forecasting Model Using Customized, Commodity-Specific Neural Networks.
A data analytics platform is provided for forecasting future states of commodities and other assets, based on processing of both textual and numerical data sources. The platform includes a multi-layer machine learning-based model that extracts sentiment from textual data in a natural language processing engine, evaluates numerical data in a time-series analysis, and generates an initial forecast for the commodity or asset being analyzed. The platform includes multiple applications of neural networks to develop augmented forecasts from further analysis of relevant information as it is collected. These include commodity-specific neural networks designed to continually develop taxonomies used to process commodity sentiment, and applications of reinforcement learning, symbolic networks, and unsupervised meta learning to improve overall performance and accuracy of the forecasts generated. A data analytics platform is provided for forecasting future states of commodities and other assets, based on processing of both textual and numerical data sources. The platform includes a multi-layer machine learning-based model that extracts sentiment from textual data in a natural language processing engine, evaluates numerical data in a time-series analysis, and generates an initial forecast for the commodity or asset being analyzed. The platform includes multiple applications of neural networks to develop augmented forecasts from further analysis of relevant information as it is collected. These include commodity-specific neural networks designed to continually develop taxonomies used to process commodity sentiment, and applications of reinforcement learning, symbolic networks, and unsupervised meta learning to improve overall performance and accuracy of the forecasts generated.
Generative Artificial Intelligence-Based Agents Using Customized Neural Networks
The first claim is as follows: A method, comprising: defining a selected asset-based subject matter; receiving input data relative to the selected asset-based subject matter, the input data comprised of structured data sources and unstructured data sources; developing a customized artificial intelligence-based agent configured to analyze the selected asset-based subject matter over time in response to one or more user-driven queries that are specific to one or more characteristics of the selected asset-based subject matter, by: contextualizing the input data in a multi-layer machine learning-based model by: building a taxonomy comprising specific keywords and keyword pairings that identify one or more predictors for the selected asset-based subject matter from the unstructured data sources, identifying discrete-time data points constructed from the structured data sources that define temporal parameters relative to the selected asset-based subject matter, modifying the taxonomy with the temporal parameters to create a set of classified content, and constructing knowledge-based rules representing specific knowledge relative to the selected asset-based subject matter and derived from subject matter-specific indicators in the input data, mapping the taxonomy and the knowledge-based rules into one or more subject matter-specific neural networks; and executing the customized artificial intelligence-based agent in response to the one or more user-driven queries, wherein responses to one or more user-driven queries are generated relative to the one or more characteristics of the selected asset-based subject matter by the customized artificial intelligence agent, and automatically initiate one or more actionable outcomes representing a performance of the selected asset-based subject matter that are executed by the customized artificial intelligence-based agent.
