Moving from attribution to action under uncertainty
We model what cannot be directly measured: indirect exposure, latent paths, and hidden influence.
We optimize policies over time, accounting for how interventions reshape future discovery.
We move beyond one-shot optimization toward closed-loop decision systems with guardrails.
For years, marketing analytics assumed observable exposures, stable environments, and optimization based on static response curves.
Generative discovery breaks these assumptions. Influence is indirect, paths are latent, and today’s actions shape tomorrow’s discovery dynamics.
In this setting, different strategies can look identical in the data — while leading to very different outcomes. This is a structural measurement problem, not a tooling gap.
Static response fitting is fragile when exposure is indirect or missing, and when actions reshape the environment.
Latent paths and feedback loops create attribution ambiguity. Dashed arrows indicate how actions reshape future discovery.
Discovery becomes partially observed and stateful. Decisions must account for uncertainty and long-horizon effects.
In partially observed environments, the same observed outcomes can be consistent with multiple underlying causal stories. If the model is not uniquely identifiable, point estimates can look confident while the policy choice is wrong.
Lekton Labs treats measurement as a belief state and optimizes policies against that uncertainty.
We infer latent discovery state rather than relying on surface-level attribution signals.
We simulate how actions reshape future discovery instead of optimizing myopically.
We surface uncertainty, tradeoffs, and constraints so decisions remain defensible.
Lekton Labs is built by researchers and practitioners with deep experience in marketing science, machine learning, and large-scale decision systems.
Our work draws on close collaboration with academic researchers and industry leaders, combining theoretical rigor with practical deployment experience.
We are intentionally small, opinionated, and focused on hard problems where existing abstractions no longer apply.
Short engagement to assess data, feasibility, and decision gaps.
Thin-slice deployment focused on one business decision or channel.
Continuous decision support with evolving models and guardrails.
If you’re navigating generative discovery and need decisions you can defend — not just attribution outputs — we should talk.
Email: contact@lektonlabs.com