The AI Data Engine Bridging Unstructured Data with Human Insights

Fuselab combines advanced AI document processing and reasoning with real-world expert and customer insights—automating the collection, analysis, and synthesis of data for smarter, more informed decisions

Product

Advanced collection, analysis and reasoning of unstructured data and human insights

The Fuselab data engine ingests massive data sets, identifies knowledge gaps and conducts expert and customer interviews—enabling unprecedented decision intelligence at scale and efficiency.

AI-Powered Data Collection and Understanding

Fuselab ingests and processes massive amounts of unstructured and semi-structured documents, including PDF, HTML, Word, Excel, PowerPoint and other document types. The data engine learns relationships and patterns within your data, extracting key knowledge and insights hidden inside— resulting in the most accurate and complete knowledge retrieval.

AI-Driven Expert and Customer Interviews

Fuselab goes beyond document analysis by identifying knowledge gaps and enabling AI-led interviews to gather additional insights. Whether it’s an industry specialist clarifying a complex topic or customers providing feedback on a product, the system captures and integrates human knowledge. These interviews help fill in missing context, validate findings, and enhance decision-making with perspectives that go beyond what’s available in existing data sources.

Real-time Insights and Proactive Intelligence

Fuselab continuously integrates processed data and human insights into a dynamic knowledge base, enabling real-time analysis and decision-making. AI-powered reasoning delivers precise, context-aware answers, while automated alerts proactively surface critical updates as new information becomes available. This ensures organizations stay ahead with the most accurate, up-to-date intelligence at their fingertips.

Adapters

Pre-Built AI Data Adapters

Innovative solutions tailored to meet your unique business needs

Internal document analysis

The document adapter automatically gathers and organizes massive amounts of domain specific unstructured documents and makes all that information accessible to your people and AI systems in real time. The system can ingest and process thousands of documents, including PDF, PowerPoint, Word, HTML and other document formats, making all of them available for real time deep knowledge search and report generation

Tell me all of the near-term events from todays research reports that have a high probability to negatively affect revenue

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Private analyst report research

Buy-side financial services firms are inundated with thousands of analyst reports daily. Readings through all of these documents manually is both time-consuming and error-prone, potentially causing firms to miss critical investment signals or act too late on rapidly evolving information. Fuselab automates the ingestion and extraction of relevant data at scale, providing portfolio managers and analysts with organized insights in near real-time

Subject

Message

Medical correspondence categorization

Fuselab streamlines medical correspondence categorization through automated EOB document intake and processing. Rapidly ingest and digitize faxed, scanned, and other unstructured EOB documents at scale, transforming them into structured datasets ready for analysis. Granular code-level tracking separates patient-specific or code-specific information even when multiple individuals appear on the same document

Public company filings analysis

Fuselab automates the ingestion, extraction, and structuring of data from EDGAR and other public filing repositories in real time, ensuring that traders, analysts, and AI-driven strategies can act on relevant information the moment it appears. Fuselab maps relationships across filings and highlights anomalies and emerging trends, surfacing alpha-generating insights faster and more accurately

Technology

Our Technology

The engine’s unique combination of AI-driven data ingestion and transformation, scale-out storage, self-supervised clustering, and domain-specific modeling ensures that Fuselab AI data stores can process and learn from any type of unstructured documents

AI data processor

Fuselab handles any kind of unstructured documents—importing and processing them into highly accurate and complete “AI stores.” Works with even the most challenging mixed format documents including tables and images

Intelligent scale-out storage

Fuselab storage is designed for speed and scale, making real-time data access seamless. Data is optimized across graph, vector, tabular and relational stores, with massive horizontal scaling for fast store and retrieval times

Self-Supervised Clustering

Fuselab’s query engine uncovers hidden patterns and relationships within data—without relying on human labels or supervision. This means data stores become smarter over time, revealing insights that others often miss

Domain-specific modeling

The AI data engine self-optimizes to your unique data domain, enabling more relevant answers and deeper insights. With context-specific modeling, AI becomes not just accurate, but truly intelligent in your domain

Neural regression

A neural regression engine helps to identify non-linear patterns and correlations in complex datasets, while adaptive self-supervised clustering is used to quickly identify related data across massive unstructured data sets

AI Reasoning Engine

Excels at deriving insights and making predictions. Creates unique multi-step reasoning and action plans with techniques including domain-optimized encoding models that ensure relevance and accuracy

About

Who We Are

Fuselab is on a mission to transform AI from supportive co-pilots to fully autonomous pilots capable of comprehending and navigating complex systems independently. Achieving this requires rethinking data organization, pattern recognition, and system-level reasoning.

Fuselab unlocks a whole new level of autonomy for AI, allowing organizations to save time, discover hidden insights, and make smarter decisions faster—whether that’s accelerating research, streamlining operations, or delivering more personalized customer experiences.

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