NASA’s SBIR/STTR program supports early-stage companies developing cutting-edge technologies with potential for both commercial and federal application. While the program has backed thousands of startups, measuring its long-term commercial impact remained complex and inconsistent—particularly when trying to connect company outcomes directly to NASA’s early support.

To address this, NASA partnered with CrowdPlat, a consulting and crowdsourcing firm that delivers high-skill project teams for government and enterprise innovation challenges. As part of this collaboration, I was brought on as a Consulting Analyst to co-develop a methodology for tracing commercialization success back to NASA’s funding interventions—supporting both performance evaluation and program storytelling at scale.

• Timeline: Jan 2025 – Present

• Scope: Develop a methodology to trace startup commercialization outcomes to NASA's seed funding initiatives.

• Impact: Created a standardized, AI-assisted process for identifying and scoring the strength of attribution across KPIs. 

Despite having access to company data and funding timelines, there was no standardized approach to:

• Track commercialization outcomes (IPOs, acquisitions, licensing deals, etc.)

• Assess the strength of linkage between success metrics and NASA's involvement

• Enable efficient report generation across dozens of companies
  
  

Manual research was:

• Time-consuming

• Inconsistent across analysts

• Incomplete in detecting indirect outcomes or citations
  
  

Phase 1: Stakeholder Discovery & KPI Landscape Mapping 

We began by engaging directly with stakeholders from NASA’s SBIR and SBIR Ignite programs to understand:

• The purpose behind measuring commercialization traceability

• The existing gaps in how success was tracked or interpreted

• The key performance indicators (KPIs) they wanted to prioritize (e.g., product launches, acquisitions, licensing deals, follow-on investments)
  
  

We also reviewed public-facing documentation and prior reports to better understand:

• The structure of SBIR/STTR phases

• Existing company data repositories

• Patterns of funding-to-market trajectories
  
  

This discovery phase clarified the lack of a consistent protocol across teams and the need for a model that could assess attribution strength—not just presence of outcomes.

Phase 2: Manual KPI Traceability Research

We then launched a manual research process across a cohort of NASA-funded startups. For each KPI, we:

• Searched public databases (e.g., news articles, patents, investor updates, grant reports - Federal Databases and Techport)

• Tracked references to NASA funding, grants, or partnerships in company disclosures or third-party media

• Recorded findings in a structured format, tagging the strength and clarity of any attribution links
  
  

The manual process helped surface a key insight: many commercialization successes were only indirectly connected to NASA’s role—and these connections varied widely in how explicitly they were documented.

Phase 3: Attribution Scoring Framework Development

To address the variability and ensure traceability assessments were consistent, I co-developed a quantitative scoring framework as part of a consulting team effort .

This framework included:

• A list of core KPIs

• A rubric to assign attribution strength scores for each data point (e.g., “direct citation of NASA funding” = high score; “uncited growth following grant period” = low score)

• A logic model that connected score ranges to interpretation tiers (e.g., Strong Attribution, Moderate Attribution, Weak/None)
  
  

The scoring system allowed for a more objective, standardized approach to analyzing startup trajectories—critical for making comparisons across firms and funding cycles.

Phase 4: Collaboration with Data Scientist to Explore Automation

To scale the traceability process, I initiated a collaboration with a data scientist. We explored:

• How LLM-powered AI prompts could replicate our manual research process at scale

• The feasibility of using AI to scan publicly available content for NASA-attributed success events

• The integration of the scoring logic into semi-automated pipelines

This exploratory collaboration informed our thinking around how AI could support future research workflows at scale 

Phase 5: SOP Design for Institutional Replication

Finally,I contributed to the design and drafting of a Standard Operating Procedure (SOP) to support knowledge transfer and repeatability . The SOP included:

• An overview of the methodology and rationale

• KPI definitions and attribution logic

• Research steps and suggested sources

• Sample AI prompts for each KPI

• Guidelines on how to score and interpret results

• Reporting templates and sample deliverable structures
  
  

The goal was to ensure that NASA employees or future consultants could replicate our methodology with consistency—regardless of technical or research expertise.

Throughout the process, we presented interim outputs to NASA stakeholders to gather feedback on:

• The clarity and usability of the framework

• The realism of data sourcing assumptions

• The alignment with program objectives
  
  

This feedback loop was crucial in refining both the scoring logic and the reporting structure, ensuring buy-in from decision-makers and relevance to real program needs.