Quantifying dataset comprehensibility — a metric + SaaS to measure effective data processing

Quantifying dataset comprehensibility — a metric + SaaS to measure effective data processing targets a $16.8B = 280,000 mid/large organizations x $60K ACV total addressable market with medium saturation and a year-over-year growth rate of 30% (analytics + ML ops + observability convergence).

Key trends driving demand: LLM-driven analytics — models can now extract higher-level insights but need evaluation metrics to measure effectiveness.; RAG & graph adoption — vector and graph-first retrieval increases actionable info per dataset, enabling new scoring methodologies.; Data observability growth — teams demand measurable signals tying data quality to business outcomes.; Democratization of AI — cheaper inference and open agent runtimes let many orgs benchmark datasets without vendor lock-in..