Proprietary data inventory
Identify the firm's unique data assets: historical decisions, internal ratings, portfolio outcomes, client behavior, and domain-specific signals not available to competitors or in public training sets.
Method
Five-step proprietary investment framework
The question is not "should we build a proprietary model?" The question is "where does our unique data create an advantage that justifies the investment?" This process answers that precisely.
Advantage specificity analysis
For each data asset, assess whether it creates a measurable performance advantage over commodity models in a specific workflow — and whether that advantage is durable or quickly replicated.
Compounding potential assessment
Evaluate whether the advantage grows over time: does using the system generate more proprietary signal that further improves performance, creating a flywheel competitors cannot access?
Investment scope definition
Define exactly what proprietary intelligence means for this use case: fine-tuning on firm data, a retrieval-augmented knowledge layer, a custom scoring model, or a full end-to-end proprietary system.
Moat durability review
Stress-test the investment thesis: what happens if a competitor builds the same capability? What happens if the underlying foundation models improve enough to close the gap without proprietary data?
Outputs
Artifacts produced by the process
Proprietary data map
Inventory of unique firm data assets with relevance assessment for each candidate use case.
- Data asset type and volume
- Competitive exclusivity rating
- Signal quality and coverage assessment
Advantage specification
Documented hypothesis for where proprietary data creates measurable model performance lift.
- Performance gap vs. commodity baseline
- Compounding mechanism description
- Validation approach and success criteria
Investment justification memo
One-page framing of the proprietary build case for leadership review and resource allocation.
- Use case, data asset, and advantage claim
- Build cost vs. performance value estimate
- Moat durability analysis and risk flags
Build roadmap
Phased plan for developing and validating the proprietary intelligence layer.
- Data preparation and labeling plan
- Model development milestones
- Validation gates and deployment criteria
Engagement Cadence
How the process runs in practice
Typical timeline: 2-3 weeks
- Week 1: proprietary data inventory and advantage specificity analysis
- Week 2: compounding potential assessment and investment scope definition
- Week 3: moat durability review, investment memo, and build roadmap
Output: a clear investment thesis for where proprietary model development creates durable competitive advantage — and a roadmap for building it.