Platform
Research and technology for private markets
Research and technology for private markets: company data, documents, meeting intelligence, and quantitative methods in one research system.
BM² combines company data, documents, meeting intelligence, and quantitative methods in one research system. Filings, financial statements, market data, meeting transcripts, and relationship records enter through controlled pipelines and become a shared, analysis-ready research base.
The platform supports origination, forecasting, underwriting, peer analysis, and monitoring. Quantitative methods, including machine learning and AI, support that work. Analysts direct the research, and material outputs are reviewed before they enter the research record.
The Platform
One system, end to end.
20+ governed data pipelines feed a broad feature space, multiple model families and a large number of forecasting, scenario and research configurations.
- 20+
- Governed data pipelines
- Multiple
- Model families across regression, tree ensembles and classification
- Broad
- Financial, operating, semantic and relationship features
- Continuous
- Forecasting, scenario and research configurations on time-based splits
Data Sources
- Company and financial data
- Market and transaction data
- Operating and sector data
- Documents and meetings
- Relationship and entity data
Data Pipelines
20+governed pipelines
- Ingestion
- Normalization
- Period alignment
- Validation
- Identity resolution
- Data quality
- Feature publication
- Provenance
Research Data Platform
- Entity-resolved company records
- Financial and operating histories
- Structured documents
- Feature tables
- Embeddings and vectors
- Ontology and knowledge graph
- Relationship data
- Source provenance
Models and Methods
Forecasting and predictive models
Gradient-boosted trees, regularized regression, and random forests.
Language models and semantic extraction
Entities, themes, and relationships from documents and transcripts.
Similarity, graph and entity models
Hilbert-space similarity, ontology, and the knowledge graph.
Retrieval and research workflows
Vector retrieval and source-linked research synthesis.
Investment Workflows
- Origination
- Forecasting
- Underwriting and scenarios
- Comparable-company selection
- Valuation
- Meeting and relationship intelligence
- Research synthesis
- Monitoring
Data Sources
- Company and financial data
- Market and transaction data
- Operating and sector data
- Documents and meetings
- Relationship and entity data
Data Pipelines
20+governed pipelines
- Ingestion
- Normalization
- Period alignment
- Validation
- Identity resolution
- Data quality
- Feature publication
- Provenance
Research Data Platform
- Entity-resolved company records
- Financial and operating histories
- Structured documents
- Feature tables
- Embeddings and vectors
- Ontology and knowledge graph
- Relationship data
- Source provenance
Models and Methods
Forecasting and predictive models
Gradient-boosted trees, regularized regression, and random forests.
Language models and semantic extraction
Entities, themes, and relationships from documents and transcripts.
Similarity, graph and entity models
Hilbert-space similarity, ontology, and the knowledge graph.
Retrieval and research workflows
Vector retrieval and source-linked research synthesis.
Investment Workflows
- Origination
- Forecasting
- Underwriting and scenarios
- Comparable-company selection
- Valuation
- Meeting and relationship intelligence
- Research synthesis
- Monitoring
Data Sources
- Company and financial data
- Market and transaction data
- Operating and sector data
- Documents and meetings
- Relationship and entity data
Data Pipelines
20+governed pipelines
- Ingestion
- Normalization
- Period alignment
- Validation
- Identity resolution
- Data quality
- Feature publication
- Provenance
Research Data Platform
- Entity-resolved company records
- Financial and operating histories
- Structured documents
- Feature tables
- Embeddings and vectors
- Ontology and knowledge graph
- Relationship data
- Source provenance
Models and Methods
Forecasting and predictive models
Gradient-boosted trees, regularized regression, and random forests.
Language models and semantic extraction
Entities, themes, and relationships from documents and transcripts.
Similarity, graph and entity models
Hilbert-space similarity, ontology, and the knowledge graph.
Retrieval and research workflows
Vector retrieval and source-linked research synthesis.
Investment Workflows
- Origination
- Forecasting
- Underwriting and scenarios
- Comparable-company selection
- Valuation
- Meeting and relationship intelligence
- Research synthesis
- Monitoring
Testing, provenance and analyst review
- Time-based validation
- Data-quality tests
- Source traceability
- Feature and model versions
- Analyst approval
- Recorded corrections
What the Platform Supports
Three areas of research work.
Origination and Meeting Intelligence
Meeting transcripts, company materials, and relationship data enter the system as they arrive. Language models extract entities, operating themes, and possible opportunity signals, and each extraction keeps a reference to its source. Analysts review the results before they join the research record.
Forecasting and Underwriting
Normalized financial and operating histories feed predictive models, together with semantic features drawn from documents and transcripts. The models support forecasting, scenario analysis, and valuation work, and every model is evaluated against historical periods before the team relies on it.
Company, Peer and Relationship Analysis
An ontology and knowledge graph connect companies, sponsors, and executives, and entity resolution keeps those records consistent. Similarity models compare companies in a normalized feature space, so peer sets reflect how companies actually operate. Industry labels alone often miss that.
Methods
Selected technical methods.
Forecasting models
Gradient-boosted trees handle non-linear interactions across financial and operating features. Regularized regression provides an interpretable baseline, and random forests give a variance check on both.
Language models and semantic extraction
Language models read filings, company materials, and meeting transcripts. They extract entities, operating themes, and relationship changes, and map each result to a structured field with a reference back to the source text.
Embeddings, retrieval and similarity
Documents and companies are embedded into vector representations. Vector retrieval assembles evidence for research, and Hilbert-space similarity compares companies across many normalized dimensions to build peer sets.
Ontology and knowledge graph
An ontology defines the entity types and relationships the firm tracks. The knowledge graph records how companies, sponsors, executives, and markets connect, and entity resolution keeps names consistent across sources.
Validation
Forecasting models train on earlier periods and are tested on later ones. Random row splits are not used, because they leak future information into training.
Feature-ablation testing
New feature groups earn their place by comparison. A baseline specification is measured against specifications that add each semantic feature group, separately and combined.
Review and Controls
Nothing enters the research record unreviewed.
The platform is governed at each step. These controls are built into the system, not applied as a later check.
- Data-quality tests
- Pipelines validate structure, ranges, and continuity before data is published to the research platform.
- Temporal validation
- Models are measured on later periods than they trained on, against baseline comparisons.
- Source provenance
- Every extracted value and research output keeps a link to the document, filing, or transcript it came from.
- Model and feature versions
- Each model version is recorded with the feature definitions and data it was trained on.
- Analyst approval
- Material changes are reviewed by an analyst before they enter the research record.
- Recorded corrections
- Corrections are stored with their reasons and are available to later model and research runs.
Learn more about the platform.
Request the BM² overview presentation or contact the firm directly.