Solution Viability Assessment

Our Approach

A solution is only as strong as its weakest component. Dissect, assess, then invest.

Our Frameworks

Capable Agency Theory (CAT)

A framework for analyzing and designing decision-making systems through the capabilities required for mature agency, including perception, action, reaction, outcomes, impact, governance, delegation, and execution.

Problem Statement Quality Framework

The quality of a problem statement determines the quality of the analyses, decisions, and solutions that follow. The Problem Statement Quality Framework (PSQF) proposes that problem statement quality can be understood through six fundamental dimensions: Conciseness, Cross-Functionality, Knowledge-Driven Representation, Atomicity, Value-Centricity, and Measurability. Together, these dimensions characterize the quality of a problem statement and identify opportunities for improvement.

Data Solution Life Cycle (DSLC)

The Data Solution Life Cycle (DSLC) is a framework for designing and delivering end-to-end data solutions. It emphasizes that successful AI and analytics projects begin with identifying the right problem—not selecting the right algorithm—and that each stage builds upon the previous one.

The framework consists of five stages: Problem Understanding, Data Acquisition & Integration, Data Preparation, Modeling & Validation, and Deployment. 

Dependency Structure Matrix (DSM)

A square matrix representation of a system that captures dependencies among its elements, enabling analysis of system architecture, sequencing, modularity, clustering, and structural complexity.

CRISP-DM

The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a widely adopted methodology that provides a structured, domain-independent framework for planning and executing data mining and analytics projects. It organizes the data mining lifecycle into six iterative phases—business understanding, data understanding, data preparation, modeling, evaluation, and deployment.