AIPact: Measure the True ROI of Your AI Investments

AIPact answers the question that every CTO and CFO is asking: is our AI investment actually moving the business? Most teams measure AI success with proxy metrics — LLM accuracy scores, latency benchmarks, and feature adoption rates. These metrics do not answer whether AI features are increasing revenue, reducing churn, or improving the outcomes that matter to the business. AIPact uses causal inference methodology to connect AI feature usage to business outcomes. Unlike simple A/B testing, causal attribution accounts for confounding variables, selection bias, and delayed effects that make raw correlation misleading. Teams instrument their AI features with AIPact's lightweight SDK. AIPact then builds a causal graph connecting feature interactions to downstream business events. The platform handles the statistical rigor automatically, surfacing results in a dashboard designed for both technical and non-technical stakeholders. Product managers can finally answer board-level questions about AI ROI with statistical confidence rather than correlation claims. The reporting layer integrates with Looker, Tableau, and any BI tool via standard data connectors. AIPact supports experiment design as well as post-hoc measurement, helping teams plan new AI features with evidence-based projections built from historical attribution data. Finance teams use AIPact reports to justify AI infrastructure spending at budget review cycles. Series B to D companies use AIPact to demonstrate AI-driven growth in investor materials with rigorous backing. The platform is model-agnostic and works with any AI feature regardless of which LLM or framework powers it.