About Selflet
Latency, hallucination, audit, voice. Four inputs for conversational AI. Set them, test them, prove them — or inherit them. Selflet.ai is the control plane that keeps powerful conversational AI grounded and governable without making it generic.
The model layer is commoditizing. The work moved to what gets built around it, and the going answer is a document, a vector store, and a prompt. That was sized for text, where every failure is recoverable — re-read it, check the citation, wait a beat. Conversation is going real time, where none of those recoveries exist.
Latency. How fast it comes back. Under two seconds a spoken exchange feels live; past five it stops being a conversation.
Hallucination. How far it may travel from the source, and what happens when it does. A budget, not a switch — held at the floor for a policy document, opened deliberately where range is the point.
Audit. What you can prove afterward. Every stage leaves a record, and every result names the coordinate it came from: model, configuration, prompt, corpus, reasoning level.
Voice. Whether it is worth listening to. Low, it recites. High, the best answer is both new and still answerable to the source.
Every deployment wants different settings. Two of ours, at opposite ends: a role-play trainer↗ in production for a client, holding a governed role under a fixed contract with every session recorded; and Magnifica Humanitas, an encyclical on artificial intelligence answering in a Celtic voice.
We measure where a build fails before it ships, and what you deploy is a build rather than a live configuration: it can be frozen, stood up again on new infrastructure or a new model, and checked against what it was. One pipeline, not an artisanal project per client.
Selflet grew out of a broader research program into how generative systems behave under constraint — how context, history, grounding, model substrate, and control structures change the behavioral space a system can reach. That work includes empirical studies of deployed language models as well as independent theoretical work connecting generative inference to questions in cognition and biological regulation. The common question is simple: what makes an intelligent system remain capable, distinctive, and adaptive while operating inside constraints?
Patent applications filed (Patent Pending):
U.S. Nonprovisional Patent Application No. 19/695,640, titled Systems and Methods for Manufacturing and Controlling Source-Grounded Voice-Faithful Conversational AI Runtimes.
U.S. Provisional Patent Application No. 64/135,081, titled Systems and Methods for Controlled Conversion of Unsupported High-Divergence Events in Retrieval-Grounded Generative Systems.
U.S. Provisional Patent Application No. 64/140,264.
U.S. Provisional Patent Application No. 64/147,462, titled Systems and Methods for Self-Validating, Map-Based Qualification of Generative Deployments by Region-Wise Behavioral Deformation, with Trajectory-Conditioned Qualification at Invariant Stimulus Coordinates.
Research
Homeostatic Drive as Policy Precision: Understanding Biological Motivation Through Large Language Model Inference Architecture. Published in Academia.edu 2025.
Selflet is built by Simons Chase @slchase. Who that is, and why the work matters to him, is here: The Original Prompt Is a Love Story.